diff --git a/.gitignore b/.gitignore index caf886a2b581b5976ea391aca5d7a56041bdbaa8..3300be325f1f6c8b2b58301fc87a4f9d241afb84 100644 --- a/.gitignore +++ b/.gitignore @@ -10,7 +10,8 @@ __pycache__/ inference/ inference_results/ output/ - +train_data/ +log/ *.DS_Store *.vs *.user diff --git a/PPOCRLabel/PPOCRLabel.py b/PPOCRLabel/PPOCRLabel.py index ce3d66f07f89cedab463cf38bc0cdc56f3a61237..827f1cf76846d0e232e980bc21f45ae0cd1a640b 100644 --- a/PPOCRLabel/PPOCRLabel.py +++ b/PPOCRLabel/PPOCRLabel.py @@ -28,7 +28,7 @@ from PyQt5.QtCore import QSize, Qt, QPoint, QByteArray, QTimer, QFileInfo, QPoin from PyQt5.QtGui import QImage, QCursor, QPixmap, QImageReader from PyQt5.QtWidgets import QMainWindow, QListWidget, QVBoxLayout, QToolButton, QHBoxLayout, QDockWidget, QWidget, \ QSlider, QGraphicsOpacityEffect, QMessageBox, QListView, QScrollArea, QWidgetAction, QApplication, QLabel, QGridLayout, \ - QFileDialog, QListWidgetItem, QComboBox, QDialog + QFileDialog, QListWidgetItem, QComboBox, QDialog, QAbstractItemView, QSizePolicy __dir__ = os.path.dirname(os.path.abspath(__file__)) @@ -227,6 +227,21 @@ class MainWindow(QMainWindow): listLayout.addWidget(leftTopToolBoxContainer) # ================== Label List ================== + labelIndexListlBox = QHBoxLayout() + + # Create and add a widget for showing current label item index + self.indexList = QListWidget() + self.indexList.setMaximumSize(30, 16777215) # limit max width + self.indexList.setEditTriggers(QAbstractItemView.NoEditTriggers) # no editable + self.indexList.itemSelectionChanged.connect(self.indexSelectionChanged) + self.indexList.setVerticalScrollBarPolicy(Qt.ScrollBarAlwaysOff) # no scroll Bar + self.indexListDock = QDockWidget('No.', self) + self.indexListDock.setWidget(self.indexList) + self.indexListDock.setFeatures(QDockWidget.NoDockWidgetFeatures) + labelIndexListlBox.addWidget(self.indexListDock, 1) + # no margin between two boxes + labelIndexListlBox.setSpacing(0) + # Create and add a widget for showing current label items self.labelList = EditInList() labelListContainer = QWidget() @@ -240,7 +255,32 @@ class MainWindow(QMainWindow): self.labelListDock = QDockWidget(self.labelListDockName, self) self.labelListDock.setWidget(self.labelList) self.labelListDock.setFeatures(QDockWidget.NoDockWidgetFeatures) - listLayout.addWidget(self.labelListDock) + labelIndexListlBox.addWidget(self.labelListDock, 10) # label list is wider than index list + + # enable labelList drag_drop to adjust bbox order + # 设置选择模式为单选 + self.labelList.setSelectionMode(QAbstractItemView.SingleSelection) + # 启用拖拽 + self.labelList.setDragEnabled(True) + # 设置接受拖放 + self.labelList.viewport().setAcceptDrops(True) + # 设置显示将要被放置的位置 + self.labelList.setDropIndicatorShown(True) + # 设置拖放模式为移动项目，如果不设置，默认为复制项目 + self.labelList.setDragDropMode(QAbstractItemView.InternalMove) + # 触发放置 + self.labelList.model().rowsMoved.connect(self.drag_drop_happened) + + labelIndexListContainer = QWidget() + labelIndexListContainer.setLayout(labelIndexListlBox) + listLayout.addWidget(labelIndexListContainer) + + # labelList indexList同步滚动 + self.labelListBar = self.labelList.verticalScrollBar() + self.indexListBar = self.indexList.verticalScrollBar() + + self.labelListBar.valueChanged.connect(self.move_scrollbar) + self.indexListBar.valueChanged.connect(self.move_scrollbar) # ================== Detection Box ================== self.BoxList = QListWidget() @@ -589,15 +629,23 @@ class MainWindow(QMainWindow): self.displayLabelOption.setChecked(settings.get(SETTING_PAINT_LABEL, False)) self.displayLabelOption.triggered.connect(self.togglePaintLabelsOption) + # Add option to enable/disable box index being displayed at the top of bounding boxes + self.displayIndexOption = QAction(getStr('displayIndex'), self) + self.displayIndexOption.setCheckable(True) + self.displayIndexOption.setChecked(settings.get(SETTING_PAINT_INDEX, False)) + self.displayIndexOption.triggered.connect(self.togglePaintIndexOption) + self.labelDialogOption = QAction(getStr('labelDialogOption'), self) self.labelDialogOption.setShortcut("Ctrl+Shift+L") self.labelDialogOption.setCheckable(True) self.labelDialogOption.setChecked(settings.get(SETTING_PAINT_LABEL, False)) + self.displayIndexOption.setChecked(settings.get(SETTING_PAINT_INDEX, False)) self.labelDialogOption.triggered.connect(self.speedChoose) self.autoSaveOption = QAction(getStr('autoSaveMode'), self) self.autoSaveOption.setCheckable(True) self.autoSaveOption.setChecked(settings.get(SETTING_PAINT_LABEL, False)) + self.displayIndexOption.setChecked(settings.get(SETTING_PAINT_INDEX, False)) self.autoSaveOption.triggered.connect(self.autoSaveFunc) addActions(self.menus.file, @@ -606,7 +654,7 @@ class MainWindow(QMainWindow): addActions(self.menus.help, (showKeys, showSteps, showInfo)) addActions(self.menus.view, ( - self.displayLabelOption, self.labelDialogOption, + self.displayLabelOption, self.displayIndexOption, self.labelDialogOption, None, hideAll, showAll, None, zoomIn, zoomOut, zoomOrg, None, @@ -744,6 +792,7 @@ class MainWindow(QMainWindow): self.shapesToItemsbox.clear() self.labelList.clear() self.BoxList.clear() + self.indexList.clear() self.filePath = None self.imageData = None self.labelFile = None @@ -964,9 +1013,10 @@ class MainWindow(QMainWindow): else: self.canvas.selectedShapes_hShape = self.canvas.selectedShapes for shape in self.canvas.selectedShapes_hShape: - item = self.shapesToItemsbox[shape] # listitem - text = [(int(p.x()), int(p.y())) for p in shape.points] - item.setText(str(text)) + if shape in self.shapesToItemsbox.keys(): + item = self.shapesToItemsbox[shape] # listitem + text = [(int(p.x()), int(p.y())) for p in shape.points] + item.setText(str(text)) self.actions.undo.setEnabled(True) self.setDirty() @@ -1004,13 +1054,19 @@ class MainWindow(QMainWindow): for shape in self.canvas.selectedShapes: shape.selected = False self.labelList.clearSelection() + self.indexList.clearSelection() self.canvas.selectedShapes = selected_shapes for shape in self.canvas.selectedShapes: shape.selected = True self.shapesToItems[shape].setSelected(True) self.shapesToItemsbox[shape].setSelected(True) + index = self.labelList.indexFromItem(self.shapesToItems[shape]).row() + self.indexList.item(index).setSelected(True) self.labelList.scrollToItem(self.currentItem()) # QAbstractItemView.EnsureVisible + # map current label item to index item and select it + index = self.labelList.indexFromItem(self.currentItem()).row() + self.indexList.scrollToItem(self.indexList.item(index)) self.BoxList.scrollToItem(self.currentBox()) if self.kie_mode: @@ -1040,13 +1096,21 @@ class MainWindow(QMainWindow): def addLabel(self, shape): shape.paintLabel = self.displayLabelOption.isChecked() + shape.paintIdx = self.displayIndexOption.isChecked() + item = HashableQListWidgetItem(shape.label) - item.setFlags(item.flags() | Qt.ItemIsUserCheckable) - item.setCheckState(Qt.Unchecked) if shape.difficult else item.setCheckState(Qt.Checked) + # current difficult checkbox is disenble + # item.setFlags(item.flags() | Qt.ItemIsUserCheckable) + # item.setCheckState(Qt.Unchecked) if shape.difficult else item.setCheckState(Qt.Checked) + # Checked means difficult is False # item.setBackground(generateColorByText(shape.label)) self.itemsToShapes[item] = shape self.shapesToItems[shape] = item + # add current label item index before label string + current_index = QListWidgetItem(str(self.labelList.count())) + current_index.setTextAlignment(Qt.AlignHCenter) + self.indexList.addItem(current_index) self.labelList.addItem(item) # print('item in add label is ',[(p.x(), p.y()) for p in shape.points], shape.label) @@ -1080,9 +1144,11 @@ class MainWindow(QMainWindow): del self.shapesToItemsbox[shape] del self.itemsToShapesbox[item] self.updateComboBox() + self.updateIndexList() def loadLabels(self, shapes): s = [] + shape_index = 0 for label, points, line_color, key_cls, difficult in shapes: shape = Shape(label=label, line_color=line_color, key_cls=key_cls) for x, y in points: @@ -1094,6 +1160,8 @@ class MainWindow(QMainWindow): shape.addPoint(QPointF(x, y)) shape.difficult = difficult + shape.idx = shape_index + shape_index += 1 # shape.locked = False shape.close() s.append(shape) @@ -1128,6 +1196,13 @@ class MainWindow(QMainWindow): # self.comboBox.update_items(uniqueTextList) + def updateIndexList(self): + self.indexList.clear() + for i in range(self.labelList.count()): + string = QListWidgetItem(str(i)) + string.setTextAlignment(Qt.AlignHCenter) + self.indexList.addItem(string) + def saveLabels(self, annotationFilePath, mode='Auto'): # Mode is Auto means that labels will be loaded from self.result_dic totally, which is the output of ocr model annotationFilePath = ustr(annotationFilePath) @@ -1183,6 +1258,10 @@ class MainWindow(QMainWindow): # fix copy and delete # self.shapeSelectionChanged(True) + def move_scrollbar(self, value): + self.labelListBar.setValue(value) + self.indexListBar.setValue(value) + def labelSelectionChanged(self): if self._noSelectionSlot: return @@ -1195,6 +1274,21 @@ class MainWindow(QMainWindow): else: self.canvas.deSelectShape() + def indexSelectionChanged(self): + if self._noSelectionSlot: + return + if self.canvas.editing(): + selected_shapes = [] + for item in self.indexList.selectedItems(): + # map index item to label item + index = self.indexList.indexFromItem(item).row() + item = self.labelList.item(index) + selected_shapes.append(self.itemsToShapes[item]) + if selected_shapes: + self.canvas.selectShapes(selected_shapes) + else: + self.canvas.deSelectShape() + def boxSelectionChanged(self): if self._noSelectionSlot: # self.BoxList.scrollToItem(self.currentBox(), QAbstractItemView.PositionAtCenter) @@ -1209,18 +1303,54 @@ class MainWindow(QMainWindow): self.canvas.deSelectShape() def labelItemChanged(self, item): - shape = self.itemsToShapes[item] - label = item.text() - if label != shape.label: - shape.label = item.text() - # shape.line_color = generateColorByText(shape.label) - self.setDirty() - elif not ((item.checkState() == Qt.Unchecked) ^ (not shape.difficult)): - shape.difficult = True if item.checkState() == Qt.Unchecked else False - self.setDirty() - else: # User probably changed item visibility - self.canvas.setShapeVisible(shape, True) # item.checkState() == Qt.Checked - # self.actions.save.setEnabled(True) + # avoid accidentally triggering the itemChanged siganl with unhashable item + # Unknown trigger condition + if type(item) == HashableQListWidgetItem: + shape = self.itemsToShapes[item] + label = item.text() + if label != shape.label: + shape.label = item.text() + # shape.line_color = generateColorByText(shape.label) + self.setDirty() + elif not ((item.checkState() == Qt.Unchecked) ^ (not shape.difficult)): + shape.difficult = True if item.checkState() == Qt.Unchecked else False + self.setDirty() + else: # User probably changed item visibility + self.canvas.setShapeVisible(shape, True) # item.checkState() == Qt.Checked + # self.actions.save.setEnabled(True) + else: + print('enter labelItemChanged slot with unhashable item: ', item, item.text()) + + def drag_drop_happened(self): + ''' + label list drag drop signal slot + ''' + # print('___________________drag_drop_happened_______________') + # should only select single item + for item in self.labelList.selectedItems(): + newIndex = self.labelList.indexFromItem(item).row() + + # only support drag_drop one item + assert len(self.canvas.selectedShapes) > 0 + for shape in self.canvas.selectedShapes: + selectedShapeIndex = shape.idx + + if newIndex == selectedShapeIndex: + return + + # move corresponding item in shape list + shape = self.canvas.shapes.pop(selectedShapeIndex) + self.canvas.shapes.insert(newIndex, shape) + + # update bbox index + self.canvas.updateShapeIndex() + + # boxList update simultaneously + item = self.BoxList.takeItem(selectedShapeIndex) + self.BoxList.insertItem(newIndex, item) + + # changes happen + self.setDirty() # Callback functions: def newShape(self, value=True): @@ -1453,6 +1583,7 @@ class MainWindow(QMainWindow): if self.labelList.count(): self.labelList.setCurrentItem(self.labelList.item(self.labelList.count() - 1)) self.labelList.item(self.labelList.count() - 1).setSelected(True) + self.indexList.item(self.labelList.count() - 1).setSelected(True) # show file list image count select_indexes = self.fileListWidget.selectedIndexes() @@ -1560,6 +1691,7 @@ class MainWindow(QMainWindow): settings[SETTING_LAST_OPEN_DIR] = '' settings[SETTING_PAINT_LABEL] = self.displayLabelOption.isChecked() + settings[SETTING_PAINT_INDEX] = self.displayIndexOption.isChecked() settings[SETTING_DRAW_SQUARE] = self.drawSquaresOption.isChecked() settings.save() try: @@ -1946,8 +2078,18 @@ class MainWindow(QMainWindow): self.labelHist.append(line) def togglePaintLabelsOption(self): + self.displayIndexOption.setChecked(False) for shape in self.canvas.shapes: shape.paintLabel = self.displayLabelOption.isChecked() + shape.paintIdx = self.displayIndexOption.isChecked() + self.canvas.repaint() + + def togglePaintIndexOption(self): + self.displayLabelOption.setChecked(False) + for shape in self.canvas.shapes: + shape.paintLabel = self.displayLabelOption.isChecked() + shape.paintIdx = self.displayIndexOption.isChecked() + self.canvas.repaint() def toogleDrawSquare(self): self.canvas.setDrawingShapeToSquare(self.drawSquaresOption.isChecked()) @@ -2042,7 +2184,7 @@ class MainWindow(QMainWindow): self.init_key_list(self.Cachelabel) def reRecognition(self): - img = cv2.imread(self.filePath) + img = cv2.imdecode(np.fromfile(self.filePath,dtype=np.uint8),1) # org_box = [dic['points'] for dic in self.PPlabel[self.getImglabelidx(self.filePath)]] if self.canvas.shapes: self.result_dic = [] @@ -2111,7 +2253,7 @@ class MainWindow(QMainWindow): QMessageBox.information(self, "Information", "Draw a box!") def singleRerecognition(self): - img = cv2.imread(self.filePath) + img = cv2.imdecode(np.fromfile(self.filePath,dtype=np.uint8),1) for shape in self.canvas.selectedShapes: box = [[int(p.x()), int(p.y())] for p in shape.points] if len(box) > 4: @@ -2181,12 +2323,14 @@ class MainWindow(QMainWindow): self.itemsToShapesbox.clear() # ADD self.shapesToItemsbox.clear() self.labelList.clear() + self.indexList.clear() self.BoxList.clear() self.result_dic = [] self.result_dic_locked = [] shapes = [] result_len = len(region['res']['boxes']) + order_index = 0 for i in range(result_len): bbox = np.array(region['res']['boxes'][i]) rec_text = region['res']['rec_res'][i][0] @@ -2205,6 +2349,8 @@ class MainWindow(QMainWindow): x, y, snapped = self.canvas.snapPointToCanvas(x, y) shape.addPoint(QPointF(x, y)) shape.difficult = False + shape.idx = order_index + order_index += 1 # shape.locked = False shape.close() self.addLabel(shape) @@ -2589,6 +2735,7 @@ class MainWindow(QMainWindow): def undoShapeEdit(self): self.canvas.restoreShape() self.labelList.clear() + self.indexList.clear() self.BoxList.clear() self.loadShapes(self.canvas.shapes) self.actions.undo.setEnabled(self.canvas.isShapeRestorable) @@ -2598,6 +2745,7 @@ class MainWindow(QMainWindow): for shape in shapes: self.addLabel(shape) self.labelList.clearSelection() + self.indexList.clearSelection() self._noSelectionSlot = False self.canvas.loadShapes(shapes, replace=replace) print("loadShapes") # 1 diff --git a/PPOCRLabel/README.md b/PPOCRLabel/README.md index 368a835c203f62d529bb874b3cbbf7593b96a8ba..3bdc336827adb87f52e9baa2c012304595b2c656 100644 --- a/PPOCRLabel/README.md +++ b/PPOCRLabel/README.md @@ -2,7 +2,7 @@ English | [简体中文](README_ch.md) # PPOCRLabel -PPOCRLabel is a semi-automatic graphic annotation tool suitable for OCR field, with built-in PPOCR model to automatically detect and re-recognize data. It is written in python3 and pyqt5, supporting rectangular box, table and multi-point annotation modes. Annotations can be directly used for the training of PPOCR detection and recognition models. +PPOCRLabel is a semi-automatic graphic annotation tool suitable for OCR field, with built-in PP-OCR model to automatically detect and re-recognize data. It is written in python3 and pyqt5, supporting rectangular box, table and multi-point annotation modes. Annotations can be directly used for the training of PP-OCR detection and recognition models. @@ -142,14 +142,18 @@ In PPOCRLabel, complete the text information labeling (text and position), compl labeling in the Excel file, the recommended steps are: 1. Table annotation: After opening the table picture, click on the `Table Recognition` button in the upper right corner of PPOCRLabel, which will call the table recognition model in PP-Structure to automatically label -the table and pop up Excel at the same time. - + the table and pop up Excel at the same time. + 2. Change the recognition result: **label each cell** (i.e. the text in a cell is marked as a box). Right click on the box and click on `Cell Re-recognition`. You can use the model to automatically recognise the text within a cell. - + 3. Mark the table structure: for each cell contains the text, **mark as any identifier (such as `1`) in Excel**, to ensure that the merged cell structure is same as the original picture. -4. Export JSON format annotation: close all Excel files corresponding to table images, click `File`-`Export table JSON annotation` to obtain JSON annotation results. + > Note: If there are blank cells in the table, you also need to mark them with a bounding box so that the total number of cells is the same as in the image. + +4. ***Adjust cell order:*** Click on the menu `View` - `Show Box Number` to show the box ordinal numbers, and drag all the results under the 'Recognition Results' column on the right side of the software interface to make the box numbers are arranged from left to right, top to bottom + +5. Export JSON format annotation: close all Excel files corresponding to table images, click `File`-`Export table JSON annotation` to obtain JSON annotation results. ### 2.3 Note @@ -219,14 +223,7 @@ PPOCRLabel supports three ways to export Label.txt - Close application export - -### 3.4 Export Partial Recognition Results - -For some data that are difficult to recognize, the recognition results will not be exported by **unchecking** the corresponding tags in the recognition results checkbox. The unchecked recognition result is saved as `True` in the `difficult` variable in the label file `label.txt`. - -> *Note: The status of the checkboxes in the recognition results still needs to be saved manually by clicking Save Button.* - -### 3.5 Dataset division +### 3.4 Dataset division - Enter the following command in the terminal to execute the dataset division script: @@ -255,7 +252,7 @@ For some data that are difficult to recognize, the recognition results will not | ... ``` -### 3.6 Error message +### 3.5 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. diff --git a/PPOCRLabel/README_ch.md b/PPOCRLabel/README_ch.md index 9b90ec3f09aea9b383744eadf483263e45e1fb73..800ef14004d561045b13bcdf727d813486f24cb3 100644 --- a/PPOCRLabel/README_ch.md +++ b/PPOCRLabel/README_ch.md @@ -7,8 +7,8 @@ PPOCRLabel是一款适用于OCR领域的半自动化图形标注工具，内置P #### 近期更新 -- 2022.05：新增表格标注，使用方法见下方`2.2 表格标注`（by [whjdark](https://github.com/peterh0323); [Evezerest](https://github.com/Evezerest)) -- 2022.02：新增关键信息标注、优化标注体验（by [PeterH0323](https://github.com/peterh0323) ） +- 2022.05：**新增表格标注**，使用方法见下方`2.2 表格标注`（by [whjdark](https://github.com/peterh0323); [Evezerest](https://github.com/Evezerest)) +- 2022.02：**新增关键信息标注**、优化标注体验（by [PeterH0323](https://github.com/peterh0323) ） - 新增：使用 `--kie` 进入 KIE 功能，用于打【检测+识别+关键字提取】的标签 - 提升用户体验：新增文件与标记数目提示、优化交互、修复gpu使用等问题。 - 新增功能：使用 `C` 和 `X` 对标记框进行旋转。 @@ -113,23 +113,29 @@ pip3 install dist/PPOCRLabel-1.0.2-py2.py3-none-any.whl -i https://mirror.baidu. 1. 安装与运行：使用上述命令安装与运行程序。 2. 打开文件夹：在菜单栏点击 “文件” - "打开目录" 选择待标记图片的文件夹[1]. -3. 自动标注：点击 ”自动标注“，使用PPOCR超轻量模型对图片文件名前图片状态[2]为 “X” 的图片进行自动标注。 +3. 自动标注：点击 ”自动标注“，使用PP-OCR超轻量模型对图片文件名前图片状态[2]为 “X” 的图片进行自动标注。 4. 手动标注：点击 “矩形标注”（推荐直接在英文模式下点击键盘中的 “W”)，用户可对当前图片中模型未检出的部分进行手动绘制标记框。点击键盘Q，则使用四点标注模式（或点击“编辑” - “四点标注”），用户依次点击4个点后，双击左键表示标注完成。 5. 标记框绘制完成后，用户点击 “确认”，检测框会先被预分配一个 “待识别” 标签。 -6. 重新识别：将图片中的所有检测画绘制/调整完成后，点击 “重新识别”，PPOCR模型会对当前图片中的**所有检测框**重新识别[3]。 +6. 重新识别：将图片中的所有检测画绘制/调整完成后，点击 “重新识别”，PP-OCR模型会对当前图片中的**所有检测框**重新识别[3]。 7. 内容更改：单击识别结果，对不准确的识别结果进行手动更改。 8. **确认标记：点击 “确认”，图片状态切换为 “√”，跳转至下一张。** 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[4]。 ### 2.2 表格标注 -表格标注针对表格的结构化提取，将图片中的表格转换为Excel格式，因此标注时需要配合外部软件打开Excel同时完成。 -在PPOCRLabel软件中完成表格中的文字信息标注（文字与位置）、在Excel文件中完成表格结构信息标注，推荐的步骤为： +表格标注针对表格的结构化提取，将图片中的表格转换为Excel格式，因此标注时需要配合外部软件打开Excel同时完成。在PPOCRLabel软件中完成表格中的文字信息标注（文字与位置）、在Excel文件中完成表格结构信息标注，推荐的步骤为： 1. 表格识别：打开表格图片后，点击软件右上角 `表格识别` 按钮，软件调用PP-Structure中的表格识别模型，自动为表格打标签，同时弹出Excel -2. 更改识别结果：**以表格中的单元格为单位增加标注框**（即一个单元格内的文字都标记为一个框）。标注框上鼠标右键后点击 `单元格重识别` + +2. 更改标注结果：**以表格中的单元格为单位增加标注框**（即一个单元格内的文字都标记为一个框）。标注框上鼠标右键后点击 `单元格重识别` 可利用模型自动识别单元格内的文字。 -3. 标注表格结构：将表格图像中有文字的单元格，**在Excel中标记为任意标识符（如`1`）**，保证Excel中的单元格合并情况与原图相同即可。 -4. 导出JSON格式：关闭所有表格图像对应的Excel，点击 `文件`-`导出表格JSON标注` 获得JSON标注结果。 + + > 注意：如果表格中存在空白单元格，同样需要使用一个标注框将其标出，使得单元格总数与图像中保持一致。 + +3. **调整单元格顺序：**点击软件`视图-显示框编号` 打开标注框序号，在软件界面右侧拖动 `识别结果` 一栏下的所有结果，使得标注框编号按照从左到右，从上到下的顺序排列 + +4. 标注表格结构：**在外部Excel软件中，将存在文字的单元格标记为任意标识符（如 `1` ）**，保证Excel中的单元格合并情况与原图相同即可（即不需要Excel中的单元格文字与图片中的文字完全相同） + +5. 导出JSON格式：关闭所有表格图像对应的Excel，点击 `文件`-`导出表格JSON标注` 获得JSON标注结果。 ### 2.3 注意 @@ -197,13 +203,7 @@ PPOCRLabel支持三种导出方式： - 关闭应用程序导出 -### 3.4 导出部分识别结果 - -针对部分难以识别的数据，通过在识别结果的复选框中**取消勾选**相应的标记，其识别结果不会被导出。被取消勾选的识别结果在标记文件 `label.txt` 中的 `difficult` 变量保存为 `True` 。 - -> *注意：识别结果中的复选框状态仍需用户手动点击确认后才能保留* - -### 3.5 数据集划分 +### 3.4 数据集划分 在终端中输入以下命令执行数据集划分脚本： @@ -232,7 +232,7 @@ python gen_ocr_train_val_test.py --trainValTestRatio 6:2:2 --datasetRootPath ../ | ... ``` -### 3.6 错误提示 +### 3.5 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。 diff --git a/PPOCRLabel/libs/canvas.py b/PPOCRLabel/libs/canvas.py index e6cddf13ede235fa193daf84d4395d77c371049a..ae9511612a2ba83001c12ae8ed82498952207f98 100644 --- a/PPOCRLabel/libs/canvas.py +++ b/PPOCRLabel/libs/canvas.py @@ -314,21 +314,23 @@ class Canvas(QWidget): QApplication.restoreOverrideCursor() # ? if self.movingShape and self.hShape: - index = self.shapes.index(self.hShape) - if ( - self.shapesBackups[-1][index].points - != self.shapes[index].points - ): - self.storeShapes() - self.shapeMoved.emit() # connect to updateBoxlist in PPOCRLabel.py + if self.hShape in self.shapes: + index = self.shapes.index(self.hShape) + if ( + self.shapesBackups[-1][index].points + != self.shapes[index].points + ): + self.storeShapes() + self.shapeMoved.emit() # connect to updateBoxlist in PPOCRLabel.py - self.movingShape = False + self.movingShape = False def endMove(self, copy=False): assert self.selectedShapes and self.selectedShapesCopy assert len(self.selectedShapesCopy) == len(self.selectedShapes) if copy: for i, shape in enumerate(self.selectedShapesCopy): + shape.idx = len(self.shapes) # add current box index self.shapes.append(shape) self.selectedShapes[i].selected = False self.selectedShapes[i] = shape @@ -524,6 +526,9 @@ class Canvas(QWidget): self.storeShapes() self.selectedShapes = [] self.update() + + self.updateShapeIndex() + return deleted_shapes def storeShapes(self): @@ -619,6 +624,13 @@ class Canvas(QWidget): pal.setColor(self.backgroundRole(), QColor(232, 232, 232, 255)) self.setPalette(pal) + # adaptive BBOX label & index font size + if self.pixmap: + h, w = self.pixmap.size().height(), self.pixmap.size().width() + fontszie = int(max(h, w) / 96) + for s in self.shapes: + s.fontsize = fontszie + p.end() def fillDrawing(self): @@ -651,7 +663,8 @@ class Canvas(QWidget): return self.current.close() - self.shapes.append(self.current) + self.current.idx = len(self.shapes) # add current box index + self.shapes.append(self.current) self.current = None self.setHiding(False) self.newShape.emit() @@ -842,6 +855,7 @@ class Canvas(QWidget): self.hVertex = None # self.hEdge = None self.storeShapes() + self.updateShapeIndex() self.repaint() def setShapeVisible(self, shape, value): @@ -883,10 +897,16 @@ class Canvas(QWidget): self.selectedShapes = [] for shape in self.shapes: shape.selected = False + self.updateShapeIndex() self.repaint() - + @property def isShapeRestorable(self): if len(self.shapesBackups) < 2: return False - return True \ No newline at end of file + return True + + def updateShapeIndex(self): + for i in range(len(self.shapes)): + self.shapes[i].idx = i + self.update() \ No newline at end of file diff --git a/PPOCRLabel/libs/constants.py b/PPOCRLabel/libs/constants.py index 58c8222ec52dcdbff7ddda04911f6703a2bdedc7..f075f4a53919db483ce9af7a09a2547f7ec3df6a 100644 --- a/PPOCRLabel/libs/constants.py +++ b/PPOCRLabel/libs/constants.py @@ -21,6 +21,7 @@ SETTING_ADVANCE_MODE = 'advanced' SETTING_WIN_STATE = 'window/state' SETTING_SAVE_DIR = 'savedir' SETTING_PAINT_LABEL = 'paintlabel' +SETTING_PAINT_INDEX = 'paintindex' SETTING_LAST_OPEN_DIR = 'lastOpenDir' SETTING_AUTO_SAVE = 'autosave' SETTING_SINGLE_CLASS = 'singleclass' diff --git a/PPOCRLabel/libs/editinlist.py b/PPOCRLabel/libs/editinlist.py index 79d2d3aa371ac076de513a4d52ea51b27c6e08f2..4bcc11ec47e090e1cda9083a35baf5b451acf8fc 100644 --- a/PPOCRLabel/libs/editinlist.py +++ b/PPOCRLabel/libs/editinlist.py @@ -26,4 +26,4 @@ class EditInList(QListWidget): def leaveEvent(self, event): # close edit for i in range(self.count()): - self.closePersistentEditor(self.item(i)) + self.closePersistentEditor(self.item(i)) \ No newline at end of file diff --git a/PPOCRLabel/libs/shape.py b/PPOCRLabel/libs/shape.py index 97e2eb72380be5c1fd1e06785be846b596763986..3eda5bdfa13b8c5369dec69bf803145e0fc3ce13 100644 --- a/PPOCRLabel/libs/shape.py +++ b/PPOCRLabel/libs/shape.py @@ -46,15 +46,16 @@ class Shape(object): point_size = 8 scale = 1.0 - def __init__(self, label=None, line_color=None, difficult=False, key_cls="None", paintLabel=False): + def __init__(self, label=None, line_color=None, difficult=False, key_cls="None", paintLabel=False, paintIdx=False): self.label = label - self.idx = 0 + self.idx = None # bbox order, only for table annotation self.points = [] self.fill = False self.selected = False self.difficult = difficult self.key_cls = key_cls self.paintLabel = paintLabel + self.paintIdx = paintIdx self.locked = False self.direction = 0 self.center = None @@ -65,6 +66,7 @@ class Shape(object): self.NEAR_VERTEX: (4, self.P_ROUND), self.MOVE_VERTEX: (1.5, self.P_SQUARE), } + self.fontsize = 8 self._closed = False @@ -124,7 +126,7 @@ class Shape(object): color = self.select_line_color if self.selected else self.line_color pen = QPen(color) # Try using integer sizes for smoother drawing(?) - pen.setWidth(max(1, int(round(2.0 / self.scale)))) + # pen.setWidth(max(1, int(round(2.0 / self.scale)))) painter.setPen(pen) line_path = QPainterPath() @@ -155,7 +157,7 @@ class Shape(object): min_y = min(min_y, point.y()) if min_x != sys.maxsize and min_y != sys.maxsize: font = QFont() - font.setPointSize(8) + font.setPointSize(self.fontsize) font.setBold(True) painter.setFont(font) if self.label is None: @@ -164,6 +166,25 @@ class Shape(object): min_y += MIN_Y_LABEL painter.drawText(min_x, min_y, self.label) + # Draw number at the top-right + if self.paintIdx: + min_x = sys.maxsize + min_y = sys.maxsize + for point in self.points: + min_x = min(min_x, point.x()) + min_y = min(min_y, point.y()) + if min_x != sys.maxsize and min_y != sys.maxsize: + font = QFont() + font.setPointSize(self.fontsize) + font.setBold(True) + painter.setFont(font) + text = '' + if self.idx != None: + text = str(self.idx) + if min_y < MIN_Y_LABEL: + min_y += MIN_Y_LABEL + painter.drawText(min_x, min_y, text) + if self.fill: color = self.select_fill_color if self.selected else self.fill_color painter.fillPath(line_path, color) diff --git a/PPOCRLabel/resources/strings/strings-en.properties b/PPOCRLabel/resources/strings/strings-en.properties index 0b112c46461b6626dfbebaa87babd691b2492d0a..1b628016c079ad1c5eb5514c7d6eb2cba842b7e3 100644 --- a/PPOCRLabel/resources/strings/strings-en.properties +++ b/PPOCRLabel/resources/strings/strings-en.properties @@ -61,6 +61,7 @@ labels=Labels autoSaveMode=Auto Save mode singleClsMode=Single Class Mode displayLabel=Display Labels +displayIndex=Display box index fileList=File List files=Files advancedMode=Advanced Mode diff --git a/PPOCRLabel/resources/strings/strings-zh-CN.properties b/PPOCRLabel/resources/strings/strings-zh-CN.properties index 184247e85b634af22394d6c038229ce3aadd9e8d..0758729a8ca0cae862a4bf5bcf2e5b24f2d95822 100644 --- a/PPOCRLabel/resources/strings/strings-zh-CN.properties +++ b/PPOCRLabel/resources/strings/strings-zh-CN.properties @@ -61,6 +61,7 @@ labels=标签 autoSaveMode=自动保存模式 singleClsMode=单一类别模式 displayLabel=显示类别 +displayIndex=显示box序号 fileList=文件列表 files=文件 advancedMode=专家模式 diff --git a/README.md b/README.md index b168ecc5e59261580e71335ced43533b2cb1732e..f57672e5055df042ede9ae03bbed590889c5941c 100644 --- a/README.md +++ b/README.md @@ -72,6 +72,7 @@ PaddleOCR support a variety of cutting-edge algorithms related to OCR, and devel + ## PP-OCR Series Model List（Update on September 8th） | Model introduction | Model name | Recommended scene | Detection model | Direction classifier | Recognition model | diff --git a/README_ch.md b/README_ch.md index f837980498c4b2e2c55a44192b7d3a2c7afb0ba9..e801ce561cb41aafb376f81a3016f0a6b838320d 100755 --- a/README_ch.md +++ b/README_ch.md @@ -71,6 +71,8 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力 ## 《动手学OCR》电子书 - [《动手学OCR》电子书📚](./doc/doc_ch/ocr_book.md) +## 场景应用 +- PaddleOCR场景应用覆盖通用，制造、金融、交通行业的主要OCR垂类应用，在PP-OCR、PP-Structure的通用能力基础之上，以notebook的形式展示利用场景数据微调、模型优化方法、数据增广等内容，为开发者快速落地OCR应用提供示范与启发。详情可查看[README](./applications)。 ## 开源社区 diff --git a/applications/README.md b/applications/README.md new file mode 100644 index 0000000000000000000000000000000000000000..eba1e205dc13dd226066784659bdb6f353e776ca --- /dev/null +++ b/applications/README.md @@ -0,0 +1,41 @@ +# 场景应用 + +PaddleOCR场景应用覆盖通用，制造、金融、交通行业的主要OCR垂类应用，在PP-OCR、PP-Structure的通用能力基础之上，以notebook的形式展示利用场景数据微调、模型优化方法、数据增广等内容，为开发者快速落地OCR应用提供示范与启发。 + +> 如需下载全部垂类模型，可以扫描下方二维码，关注公众号填写问卷后，加入PaddleOCR官方交流群获取20G OCR学习大礼包（内含《动手学OCR》电子书、课程回放视频、前沿论文等重磅资料） + +
+ +
+ + +> 如果您是企业开发者且未在下述场景中找到合适的方案，可以填写[OCR应用合作调研问卷](https://paddle.wjx.cn/vj/QwF7GKw.aspx)，免费与官方团队展开不同层次的合作，包括但不限于问题抽象、确定技术方案、项目答疑、共同研发等。如果您已经使用PaddleOCR落地项目，也可以填写此问卷，与飞桨平台共同宣传推广，提升企业技术品宣。期待您的提交！ + +## 通用 + +| 类别 | 亮点 | 类别 | 亮点 | +| ------------------------------------------------- | -------- | ---------- | ------------ | +| [高精度中文识别模型SVTR](./高精度中文识别模型.md) | 新增模型 | 手写体识别 | 新增字形支持 | + +## 制造 + +| 类别 | 亮点 | 类别 | 亮点 | +| ------------------------------------------------------------ | ------------------------------ | ------------------------------------------- | -------------------- | +| [数码管识别](./光功率计数码管字符识别/光功率计数码管字符识别.md) | 数码管数据合成、漏识别调优 | 电表识别 | 大分辨率图像检测调优 | +| [液晶屏读数识别](./液晶屏读数识别.md) | 检测模型蒸馏、Serving部署 | [PCB文字识别](./PCB字符识别/PCB字符识别.md) | 小尺寸文本检测与识别 | +| [包装生产日期](./包装生产日期识别.md) | 点阵字符合成、过曝过暗文字识别 | 液晶屏缺陷检测 | 非文字字符识别 | + +## 金融 + +| 类别 | 亮点 | 类别 | 亮点 | +| ------------------------------ | ------------------------ | ------------ | --------------------- | +| [表单VQA](./多模态表单识别.md) | 多模态通用表单结构化提取 | 通用卡证识别 | 通用结构化提取 | +| 增值税发票 | 尽请期待 | 身份证识别 | 结构化提取、图像阴影 | +| 印章检测与识别 | 端到端弯曲文本识别 | 合同比对 | 密集文本检测、NLP串联 | + +## 交通 + +| 类别 | 亮点 | 类别 | 亮点 | +| ------------------------------- | ------------------------------ | ---------- | -------- | +| [车牌识别](./轻量级车牌识别.md) | 多角度图像、轻量模型、端侧部署 | 快递单识别 | 尽请期待 | +| 驾驶证/行驶证识别 | 尽请期待 | | | \ No newline at end of file diff --git a/applications/corpus/digital.txt "b/applications/\345\205\211\345\212\237\347\216\207\350\256\241\346\225\260\347\240\201\347\256\241\345\255\227\347\254\246\350\257\206\345\210\253/corpus/digital.txt" similarity index 100% rename from applications/corpus/digital.txt rename to "applications/\345\205\211\345\212\237\347\216\207\350\256\241\346\225\260\347\240\201\347\256\241\345\255\227\347\254\246\350\257\206\345\210\253/corpus/digital.txt" diff --git a/applications/fonts/DS-DIGI.TTF "b/applications/\345\205\211\345\212\237\347\216\207\350\256\241\346\225\260\347\240\201\347\256\241\345\255\227\347\254\246\350\257\206\345\210\253/fonts/DS-DIGI.TTF" similarity index 100% rename from applications/fonts/DS-DIGI.TTF rename to "applications/\345\205\211\345\212\237\347\216\207\350\256\241\346\225\260\347\240\201\347\256\241\345\255\227\347\254\246\350\257\206\345\210\253/fonts/DS-DIGI.TTF" diff --git a/applications/fonts/DS-DIGIB.TTF "b/applications/\345\205\211\345\212\237\347\216\207\350\256\241\346\225\260\347\240\201\347\256\241\345\255\227\347\254\246\350\257\206\345\210\253/fonts/DS-DIGIB.TTF" similarity index 100% rename from applications/fonts/DS-DIGIB.TTF rename to "applications/\345\205\211\345\212\237\347\216\207\350\256\241\346\225\260\347\240\201\347\256\241\345\255\227\347\254\246\350\257\206\345\210\253/fonts/DS-DIGIB.TTF" diff --git 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"a/applications/\345\214\205\350\243\205\347\224\237\344\272\247\346\227\245\346\234\237\350\257\206\345\210\253.md" "b/applications/\345\214\205\350\243\205\347\224\237\344\272\247\346\227\245\346\234\237\350\257\206\345\210\253.md" new file mode 100644 index 0000000000000000000000000000000000000000..73c174c4f954d7cd348b57fc5d6b00f6c5dab64b --- /dev/null +++ "b/applications/\345\214\205\350\243\205\347\224\237\344\272\247\346\227\245\346\234\237\350\257\206\345\210\253.md" @@ -0,0 +1,685 @@ +# 一种基于PaddleOCR的产品包装生产日期识别模型 + +- [1. 项目介绍](#1-项目介绍) +- [2. 环境搭建](#2-环境搭建) +- [3. 数据准备](#3-数据准备) +- [4. 直接使用PP-OCRv3模型评估](#4-直接使用PPOCRv3模型评估) +- [5. 基于合成数据finetune](#5-基于合成数据finetune) + - [5.1 Text Renderer数据合成方法](#51-TextRenderer数据合成方法) + - [5.1.1 下载Text Renderer代码](#511-下载TextRenderer代码) + - [5.1.2 准备背景图片](#512-准备背景图片) + - [5.1.3 准备语料](#513-准备语料) + - [5.1.4 下载字体](#514-下载字体) + - [5.1.5 运行数据合成命令](#515-运行数据合成命令) + - [5.2 模型训练](#52-模型训练) +- [6. 基于真实数据finetune](#6-基于真实数据finetune) + - [6.1 python爬虫获取数据](#61-python爬虫获取数据) + - [6.2 数据挖掘](#62-数据挖掘) + - [6.3 模型训练](#63-模型训练) +- [7. 基于合成+真实数据finetune](#7-基于合成+真实数据finetune) + + +## 1. 项目介绍 + +产品包装生产日期是计算机视觉图像识别技术在工业场景中的一种应用。产品包装生产日期识别技术要求能够将产品生产日期从复杂背景中提取并识别出来，在物流管理、物资管理中得到广泛应用。 + +![](https://ai-studio-static-online.cdn.bcebos.com/d9e0533cc1df47ffa3bbe99de9e42639a3ebfa5bce834bafb1ca4574bf9db684) + + +- 项目难点 + +1. 没有训练数据 +2. 图像质量层次不齐: 角度倾斜、图片模糊、光照不足、过曝等问题严重 + +针对以上问题， 本例选用PP-OCRv3这一开源超轻量OCR系统进行包装产品生产日期识别系统的开发。直接使用PP-OCRv3进行评估的精度为62.99%。为提升识别精度，我们首先使用数据合成工具合成了3k数据，基于这部分数据进行finetune，识别精度提升至73.66%。由于合成数据与真实数据之间的分布存在差异，为进一步提升精度，我们使用网络爬虫配合数据挖掘策略得到了1k带标签的真实数据，基于真实数据finetune的精度为71.33%。最后，我们综合使用合成数据和真实数据进行finetune，将识别精度提升至86.99%。各策略的精度提升效果如下： + +| 策略 | 精度| +| :--------------- | :-------- | +| PP-OCRv3评估 | 62.99| +| 合成数据finetune | 73.66| +| 真实数据finetune | 71.33| +| 真实+合成数据finetune | 86.99| + +AIStudio项目链接： [一种基于PaddleOCR的包装生产日期识别方法](https://aistudio.baidu.com/aistudio/projectdetail/4287736) + +## 2. 环境搭建 + +本任务基于Aistudio完成, 具体环境如下： + +- 操作系统: Linux +- PaddlePaddle: 2.3 +- PaddleOCR: Release/2.5 +- text_renderer: master + +下载PaddlleOCR代码并安装依赖库: +```bash +git clone -b dygraph https://gitee.com/paddlepaddle/PaddleOCR + +# 安装依赖库 +cd PaddleOCR +pip install -r PaddleOCR/requirements.txt +``` + +## 3. 数据准备 + +本项目使用人工预标注的300张图像作为测试集。 + +部分数据示例如下： + +![](https://ai-studio-static-online.cdn.bcebos.com/39ff30e0ab0442579712255e6a9ea6b5271169c98e624e6eb2b8781f003bfea0) + + +标签文件格式如下： +```txt +数据路径 标签（中间以制表符分隔） +``` + +|数据集类型|数量| +|---|---| +|测试集| 300| + +数据集[下载链接](https://aistudio.baidu.com/aistudio/datasetdetail/149770)，下载后可以通过下方命令解压: + +```bash +tar -xvf data.tar +mv data ${PaddleOCR_root} +``` + +数据解压后的文件结构如下： + +```shell +PaddleOCR +├── data +│ ├── mining_images # 挖掘的真实数据示例 +│ ├── mining_train.list # 挖掘的真实数据文件列表 +│ ├── render_images # 合成数据示例 +│ ├── render_train.list # 合成数据文件列表 +│ ├── val # 测试集数据 +│ └── val.list # 测试集数据文件列表 +| ├── bg # 合成数据所需背景图像 +│ └── corpus # 合成数据所需语料 +``` + +## 4. 直接使用PP-OCRv3模型评估 + +准备好测试数据后，可以使用PaddleOCR的PP-OCRv3模型进行识别。 + +- 下载预训练模型 + +首先需要下载PP-OCR v3中英文识别模型文件，下载链接可以在https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.5/doc/doc_ch/ppocr_introduction.md#6 获取，下载命令: + +```bash +cd ${PaddleOCR_root} +mkdir ckpt +wget -nc -P ckpt https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_train.tar +pushd ckpt/ +tar -xvf ch_PP-OCRv3_rec_train.tar +popd +``` + +- 模型评估 + +使用以下命令进行PP-OCRv3评估: + +```bash +python tools/eval.py -c configs/rec/PP-OCRv3/ch_PP-OCRv3_rec_distillation.yml \ + -o Global.checkpoints=ckpt/ch_PP-OCRv3_rec_train/best_accuracy \ + Eval.dataset.data_dir=./data \ + Eval.dataset.label_file_list=["./data/val.list"] + +``` + +其中各参数含义如下： + +```bash +-c: 指定使用的配置文件，ch_PP-OCRv3_rec_distillation.yml对应于OCRv3识别模型。 +-o: 覆盖配置文件中参数 +Global.checkpoints: 指定评估使用的模型文件路径 +Eval.dataset.data_dir: 指定评估数据集路径 +Eval.dataset.label_file_list: 指定评估数据集文件列表 +``` + +## 5. 基于合成数据finetune + +### 5.1 Text Renderer数据合成方法 + +#### 5.1.1 下载Text Renderer代码 + +首先从github或gitee下载Text Renderer代码，并安装相关依赖。 + +```bash +git clone https://gitee.com/wowowoll/text_renderer.git + +# 安装依赖库 +cd text_renderer +pip install -r requirements.txt +``` + +使用text renderer合成数据之前需要准备好背景图片、语料以及字体库，下面将逐一介绍各个步骤。 + +#### 5.1.2 准备背景图片 + +观察日常生活中常见的包装生产日期图片，我们可以发现其背景相对简单。为此我们可以从网上找一下图片，截取部分图像块作为背景图像。 + +本项目已准备了部分图像作为背景图片，在第3部分完成数据准备后,可以得到我们准备好的背景图像，示例如下： + +![](https://ai-studio-static-online.cdn.bcebos.com/456ae2acb27d4a94896c478812aee0bc3551c703d7bd40c9be4dc983c7b3fc8a) + + + +背景图像存放于如下位置： + +```shell +PaddleOCR +├── data +｜ ├── bg # 合成数据所需背景图像 +``` + +#### 5.1.3 准备语料 + +观察测试集生产日期图像，我们可以知道如下数据有如下特点： +1. 由年月日组成，中间可能以“/”、“-”、“:”、“.”或者空格间隔，也可能以汉字年月日分隔 +2. 有些生产日期包含在产品批号中，此时可能包含具体时间、英文字母或数字标识 + +基于以上两点，我们编写语料生成脚本： + +```python +import random +from random import choice +import os + +cropus_num = 2000 #设置语料数量 + +def get_cropus(f): + # 随机生成年份 + year = random.randint(0, 22) + # 随机生成月份 + month = random.randint(1, 12) + # 随机生成日期 + day_dict = {31: [1,3,5,7,8,10,12], 30: [4,6,9,11], 28: [2]} + for item in day_dict: + if month in day_dict[item]: + day = random.randint(0, item) + # 随机生成小时 + hours = random.randint(0, 24) + # 随机生成分钟 + minute = random.randint(0, 60) + # 随机生成秒数 + second = random.randint(0, 60) + + # 随机生成产品标识字符 + length = random.randint(0, 6) + file_id = [] + flag = 0 + my_dict = [i for i in range(48,58)] + [j for j in range(40, 42)] + [k for k in range(65,90)] # 大小写字母 + 括号 + + for i in range(1, length): + if flag: + if i == flag+2: #括号匹配 + file_id.append(')') + flag = 0 + continue + sel = choice(my_dict) + if sel == 41: + continue + if sel == 40: + if i == 1 or i > length-3: + continue + flag = i + my_ascii = chr(sel) + file_id.append(my_ascii) + file_id_str = ''.join(file_id) + + #随机生成产品标识字符 + file_id2 = random.randint(0, 9) + + rad = random.random() + if rad < 0.3: + f.write('20{:02d}{:02d}{:02d} {}'.format(year, month, day, file_id_str)) + elif 0.3 < rad < 0.5: + f.write('20{:02d}年{:02d}月{:02d}日'.format(year, month, day)) + elif 0.5 < rad < 0.7: + f.write('20{:02d}/{:02d}/{:02d}'.format(year, month, day)) + elif 0.7 < rad < 0.8: + f.write('20{:02d}-{:02d}-{:02d}'.format(year, month, day)) + elif 0.8 < rad < 0.9: + f.write('20{:02d}.{:02d}.{:02d}'.format(year, month, day)) + else: + f.write('{:02d}:{:02d}:{:02d} {:02d}'.format(hours, minute, second, file_id2)) + +if __name__ == "__main__": + file_path = '/home/aistudio/text_renderer/my_data/cropus' + if not os.path.exists(file_path): + os.makedirs(file_path) + file_name = os.path.join(file_path, 'books.txt') + f = open(file_name, 'w') + for i in range(cropus_num): + get_cropus(f) + if i < cropus_num-1: + f.write('\n') + + f.close() +``` + +本项目已准备了部分语料，在第3部分完成数据准备后,可以得到我们准备好的语料库，默认位置如下： + +```shell +PaddleOCR +├── data +│ └── corpus #合成数据所需语料 +``` + +#### 5.1.4 下载字体 + +观察包装生产日期，我们可以发现其使用的字体为点阵体。字体可以在如下网址下载： +https://www.fonts.net.cn/fonts-en/tag-dianzhen-1.html + +本项目已准备了部分字体，在第3部分完成数据准备后,可以得到我们准备好的字体，默认位置如下： + +```shell +PaddleOCR +├── data +│ └── fonts #合成数据所需字体 +``` + +下载好字体后，还需要在list文件中指定字体文件存放路径，脚本如下: + +```bash +cd text_renderer/my_data/ +touch fonts.list +ls /home/aistudio/PaddleOCR/data/fonts/* > fonts.list +``` + +#### 5.1.5 运行数据合成命令 + +完成数据准备后，my_data文件结构如下： + +```shell +my_data/ +├── cropus +│ └── books.txt #语料库 +├── eng.txt #字符列表 +└── fonts.list #字体列表 +``` + +在运行合成数据命令之前，还有两处细节需要手动修改： +1. 将默认配置文件`text_renderer/configs/default.yaml`中第9行enable的值设为`true`，即允许合成彩色图像。否则合成的都是灰度图。 + +```yaml + # color boundary is in R,G,B format + font_color: ++ enable: true #false +``` + +2. 将`text_renderer/textrenderer/renderer.py`第184行作如下修改，取消padding。否则图片两端会有一些空白。 + +```python +padding = random.randint(s_bbox_width // 10, s_bbox_width // 8) #修改前 +padding = 0 #修改后 +``` + +运行数据合成命令: + +```bash +cd /home/aistudio/text_renderer/ +python main.py --num_img=3000 \ + --fonts_list='./my_data/fonts.list' \ + --corpus_dir "./my_data/cropus" \ + --corpus_mode "list" \ + --bg_dir "/home/aistudio/PaddleOCR/data/bg/" \ + --img_width 0 +``` + +合成好的数据默认保存在`text_renderer/output`目录下，可进入该目录查看合成的数据。 + + +合成数据示例如下 +![](https://ai-studio-static-online.cdn.bcebos.com/d686a48d465a43d09fbee51924fdca42ee21c50e676646da8559fb9967b94185) + +数据合成好后，还需要生成如下格式的训练所需的标注文件， +``` +图像路径 标签 +``` + +使用如下脚本即可生成标注文件： + +```python +import random + +abspath = '/home/aistudio/text_renderer/output/default/' + +#标注文件生成路径 +fout = open('./render_train.list', 'w', encoding='utf-8') + +with open('./output/default/tmp_labels.txt','r') as f: + lines = f.readlines() + for item in lines: + label = item[9:] + filename = item[:8] + '.jpg' + fout.write(abspath + filename + '\t' + label) + + fout.close() +``` + +经过以上步骤，我们便完成了包装生产日期数据合成。 +数据位于`text_renderer/output`，标注文件位于`text_renderer/render_train.list`。 + +本项目提供了生成好的数据供大家体验,完成步骤3的数据准备后，可得数据路径位于: + +```shell +PaddleOCR +├── data +│ ├── render_images # 合成数据示例 +│ ├── render_train.list #合成数据文件列表 +``` + +### 5.2 模型训练 + +准备好合成数据后，我们可以使用以下命令，利用合成数据进行finetune: +```bash +cd ${PaddleOCR_root} +python tools/train.py -c configs/rec/PP-OCRv3/ch_PP-OCRv3_rec_distillation.yml \ + -o Global.pretrained_model=./ckpt/ch_PP-OCRv3_rec_train/best_accuracy \ + Global.epoch_num=20 \ + Global.eval_batch_step='[0, 20]' \ + Train.dataset.data_dir=./data \ + Train.dataset.label_file_list=['./data/render_train.list'] \ + Train.loader.batch_size_per_card=64 \ + Eval.dataset.data_dir=./data \ + Eval.dataset.label_file_list=["./data/val.list"] \ + Eval.loader.batch_size_per_card=64 + +``` + +其中各参数含义如下： + +```txt +-c: 指定使用的配置文件，ch_PP-OCRv3_rec_distillation.yml对应于OCRv3识别模型。 +-o: 覆盖配置文件中参数 +Global.pretrained_model: 指定finetune使用的预训练模型 +Global.epoch_num: 指定训练的epoch数 +Global.eval_batch_step: 间隔多少step做一次评估 +Train.dataset.data_dir: 训练数据集路径 +Train.dataset.label_file_list: 训练集文件列表 +Train.loader.batch_size_per_card: 训练单卡batch size +Eval.dataset.data_dir: 评估数据集路径 +Eval.dataset.label_file_list: 评估数据集文件列表 +Eval.loader.batch_size_per_card: 评估单卡batch size +``` + +## 6. 基于真实数据finetune + + +使用合成数据finetune能提升我们模型的识别精度，但由于合成数据和真实数据之间的分布可能有一定差异，因此作用有限。为进一步提高识别精度，本节介绍如何挖掘真实数据进行模型finetune。 + +数据挖掘的整体思路如下： +1. 使用python爬虫从网上获取大量无标签数据 +2. 使用模型从大量无标签数据中构建出有效训练集 + +### 6.1 python爬虫获取数据 + +- 推荐使用[爬虫工具](https://github.com/Joeclinton1/google-images-download)获取无标签图片。 + +图片获取后，可按如下目录格式组织： + +```txt +sprider +├── file.list +├── data +│ ├── 00000.jpg +│ ├── 00001.jpg +... +``` + +### 6.2 数据挖掘 + +我们使用PaddleOCR对获取到的图片进行挖掘，具体步骤如下： +1. 使用 PP-OCRv3检测模型+svtr-tiny识别模型，对每张图片进行预测。 +2. 使用数据挖掘策略，得到有效图片。 +3. 将有效图片对应的图像区域和标签提取出来，构建训练集。 + + +首先下载预训练模型，PP-OCRv3检测模型下载链接：https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar + +如需获取svtr-tiny高精度中文识别预训练模型，请扫码填写问卷，加入PaddleOCR官方交流群获取全部OCR垂类模型下载链接、《动手学OCR》电子书等全套OCR学习资料🎁 +
+ +
+ + +完成下载后，可将模型存储于如下位置: + +```shell +PaddleOCR +├── data +│ ├── rec_vit_sub_64_363_all/ # svtr_tiny高精度识别模型 +``` + +```bash +# 下载解压PP-OCRv3检测模型 +cd ${PaddleOCR_root} +wget -nc -P ckpt https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar +pushd ckpt +tar -xvf ch_PP-OCRv3_det_infer.tar +popd ckpt +``` + +在使用PPOCRv3检测模型+svtr-tiny识别模型进行预测之前，有如下两处细节需要手动修改： +1. 将`tools/infer/predict_rec.py`中第110行`imgW`修改为`320` + +```python +#imgW = int((imgH * max_wh_ratio)) +imgW = 320 +``` + +2. 将`tools/infer/predict_system.py`第169行添加如下一行，将预测分数也写入结果文件中。 + +```python +"scores": rec_res[idx][1], +``` + +模型预测命令: +```bash +python tools/infer/predict_system.py \ + --image_dir="/home/aistudio/sprider/data" \ + --det_model_dir="./ckpt/ch_PP-OCRv3_det_infer/" \ + --rec_model_dir="/home/aistudio/PaddleOCR/data/rec_vit_sub_64_363_all/" \ + --rec_image_shape="3,32,320" +``` + +获得预测结果后，我们使用数据挖掘策略得到有效图片。具体挖掘策略如下： +1. 预测置信度高于95% +2. 识别结果包含字符‘20’，即年份 +3. 没有中文，或者有中文并且‘日’和'月'同时在识别结果中 + +```python +# 获取有效预测 + +import json +import re + +zh_pattern = re.compile(u'[\u4e00-\u9fa5]+') #正则表达式，筛选字符是否包含中文 + +file_path = '/home/aistudio/PaddleOCR/inference_results/system_results.txt' +out_path = '/home/aistudio/PaddleOCR/selected_results.txt' +f_out = open(out_path, 'w') + +with open(file_path, "r", encoding='utf-8') as fin: + lines = fin.readlines() + + +for line in lines: + flag = False + # 读取文件内容 + file_name, json_file = line.strip().split('\t') + preds = json.loads(json_file) + res = [] + for item in preds: + transcription = item['transcription'] #获取识别结果 + scores = item['scores'] #获取识别得分 + # 挖掘策略 + if scores > 0.95: + if '20' in transcription and len(transcription) > 4 and len(transcription) < 12: + word = transcription + if not(zh_pattern.search(word) and ('日' not in word or '月' not in word)): + flag = True + res.append(item) + save_pred = file_name + "\t" + json.dumps( + res, ensure_ascii=False) + "\n" + if flag ==True: + f_out.write(save_pred) + +f_out.close() +``` + +然后将有效预测对应的图像区域和标签提取出来，构建训练集。具体实现脚本如下： + +```python +import cv2 +import json +import numpy as np + +PATH = '/home/aistudio/PaddleOCR/inference_results/' #数据原始路径 +SAVE_PATH = '/home/aistudio/mining_images/' #裁剪后数据保存路径 +file_list = '/home/aistudio/PaddleOCR/selected_results.txt' #数据预测结果 +label_file = '/home/aistudio/mining_images/mining_train.list' #输出真实数据训练集标签list + +if not os.path.exists(SAVE_PATH): + os.mkdir(SAVE_PATH) + +f_label = open(label_file, 'w') + + +def get_rotate_crop_image(img, points): + """ + 根据检测结果points，从输入图像img中裁剪出相应的区域 + """ + assert len(points) == 4, "shape of points must be 4*2" + img_crop_width = int( + max( + np.linalg.norm(points[0] - points[1]), + np.linalg.norm(points[2] - points[3]))) + img_crop_height = int( + max( + np.linalg.norm(points[0] - points[3]), + np.linalg.norm(points[1] - points[2]))) + pts_std = np.float32([[0, 0], [img_crop_width, 0], + [img_crop_width, img_crop_height], + [0, img_crop_height]]) + M = cv2.getPerspectiveTransform(points, pts_std) + # 形变或倾斜，会做透视变换，reshape成矩形 + dst_img = cv2.warpPerspective( + img, + M, (img_crop_width, img_crop_height), + borderMode=cv2.BORDER_REPLICATE, + flags=cv2.INTER_CUBIC) + dst_img_height, dst_img_width = dst_img.shape[0:2] + if dst_img_height * 1.0 / dst_img_width >= 1.5: + dst_img = np.rot90(dst_img) + return dst_img + +def crop_and_get_filelist(file_list): + with open(file_list, "r", encoding='utf-8') as fin: + lines = fin.readlines() + + img_num = 0 + for line in lines: + img_name, json_file = line.strip().split('\t') + preds = json.loads(json_file) + for item in preds: + transcription = item['transcription'] + points = item['points'] + points = np.array(points).astype('float32') + #print('processing {}...'.format(img_name)) + + img = cv2.imread(PATH+img_name) + dst_img = get_rotate_crop_image(img, points) + h, w, c = dst_img.shape + newWidth = int((32. / h) * w) + newImg = cv2.resize(dst_img, (newWidth, 32)) + new_img_name = '{:05d}.jpg'.format(img_num) + cv2.imwrite(SAVE_PATH+new_img_name, dst_img) + f_label.write(SAVE_PATH+new_img_name+'\t'+transcription+'\n') + img_num += 1 + + +crop_and_get_filelist(file_list) +f_label.close() +``` + +### 6.3 模型训练 + +通过数据挖掘，我们得到了真实场景数据和对应的标签。接下来使用真实数据finetune，观察精度提升效果。 + + +利用真实数据进行finetune: + +```bash +cd ${PaddleOCR_root} +python tools/train.py -c configs/rec/PP-OCRv3/ch_PP-OCRv3_rec_distillation.yml \ + -o Global.pretrained_model=./ckpt/ch_PP-OCRv3_rec_train/best_accuracy \ + Global.epoch_num=20 \ + Global.eval_batch_step='[0, 20]' \ + Train.dataset.data_dir=./data \ + Train.dataset.label_file_list=['./data/mining_train.list'] \ + Train.loader.batch_size_per_card=64 \ + Eval.dataset.data_dir=./data \ + Eval.dataset.label_file_list=["./data/val.list"] \ + Eval.loader.batch_size_per_card=64 +``` + +各参数含义参考第6部分合成数据finetune，只需要对训练数据路径做相应的修改： + +```txt +Train.dataset.data_dir: 训练数据集路径 +Train.dataset.label_file_list: 训练集文件列表 +``` + +示例使用我们提供的真实数据进行finetune，如想换成自己的数据，只需要相应的修改`Train.dataset.data_dir`和`Train.dataset.label_file_list`参数即可。 + +由于数据量不大，这里仅训练20个epoch即可。训练完成后，可以得到合成数据finetune后的精度为best acc=**71.33%**。 + +由于数量比较少，精度会比合成数据finetue的略低。 + + +## 7. 基于合成+真实数据finetune + +为进一步提升模型精度，我们结合使用合成数据和挖掘到的真实数据进行finetune。 + +利用合成+真实数据进行finetune，各参数含义参考第6部分合成数据finetune，只需要对训练数据路径做相应的修改： + +```txt +Train.dataset.data_dir: 训练数据集路径 +Train.dataset.label_file_list: 训练集文件列表 +``` + +生成训练list文件: +```bash +# 生成训练集文件list +cat /home/aistudio/PaddleOCR/data/render_train.list /home/aistudio/PaddleOCR/data/mining_train.list > /home/aistudio/PaddleOCR/data/render_mining_train.list +``` + +启动训练: +```bash +cd ${PaddleOCR_root} +python tools/train.py -c configs/rec/PP-OCRv3/ch_PP-OCRv3_rec_distillation.yml \ + -o Global.pretrained_model=./ckpt/ch_PP-OCRv3_rec_train/best_accuracy \ + Global.epoch_num=40 \ + Global.eval_batch_step='[0, 20]' \ + Train.dataset.data_dir=./data \ + Train.dataset.label_file_list=['./data/render_mining_train.list'] \ + Train.loader.batch_size_per_card=64 \ + Eval.dataset.data_dir=./data \ + Eval.dataset.label_file_list=["./data/val.list"] \ + Eval.loader.batch_size_per_card=64 +``` + +示例使用我们提供的真实+合成数据进行finetune，如想换成自己的数据，只需要相应的修改Train.dataset.data_dir和Train.dataset.label_file_list参数即可。 + +由于数据量不大，这里仅训练40个epoch即可。训练完成后，可以得到合成数据finetune后的精度为best acc=**86.99%**。 + +可以看到，相较于原始PP-OCRv3的识别精度62.99%，使用合成数据+真实数据finetune后，识别精度能提升24%。 + +如需获取已训练模型，可以同样扫描上方二维码下载，将下载或训练完成的模型放置在对应目录下即可完成模型推理。 + +模型的推理部署方法可以参考repo文档： https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.5/deploy/README_ch.md diff --git a/configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_cml.yml b/configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_cml.yml index 3833cb0bad9915a2169b116e7406e01cadd0ef62..df429314cd0ec058aa6779a0ff55656f1b211bbf 100644 --- a/configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_cml.yml +++ b/configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_cml.yml @@ -28,7 +28,7 @@ Architecture: algorithm: DB Transform: Backbone: - name: ResNet + name: ResNet_vd layers: 18 Neck: name: DBFPN diff --git a/configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_distill.yml b/configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_distill.yml index b4644244f849abb05d5501c7d2ed2bf92efe916e..d24ee11f3dee9d17a587d6d4d0765abfe459fbc9 100644 --- a/configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_distill.yml +++ b/configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_distill.yml @@ -45,7 +45,7 @@ Architecture: algorithm: DB Transform: Backbone: - name: ResNet + name: ResNet_vd layers: 18 Neck: name: DBFPN diff --git a/configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_dml.yml b/configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_dml.yml index f3ad966488dbc2f6f7ca12033bc4a3d35e1b3bd7..8b160f63538d51dc57b08ba83f7ebf019e3c9dbb 100644 --- a/configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_dml.yml +++ b/configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_dml.yml @@ -65,7 +65,7 @@ Loss: - ["Student", "Teacher"] maps_name: "thrink_maps" weight: 1.0 - act: "softmax" + # act: None model_name_pairs: ["Student", "Teacher"] key: maps - DistillationDBLoss: diff --git a/configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml b/configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml index 3e77577c17abe2111c501d96ce6b1087ac44f8d6..ef58befd694e26704c734d7fd072ebc3370c8554 100644 --- a/configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml +++ b/configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml @@ -61,7 +61,7 @@ Architecture: model_type: det algorithm: DB Backbone: - name: ResNet + name: ResNet_vd in_channels: 3 layers: 50 Neck: diff --git a/configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_dml.yml b/configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_dml.yml index 3f30ada13f2f1c3c01ba8886bbfba006da516f17..e85127a2ee37fc4a6c59066e465318792f9a0dd3 100644 --- a/configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_dml.yml +++ b/configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_dml.yml @@ -25,7 +25,7 @@ Architecture: model_type: det algorithm: DB Backbone: - name: ResNet + name: ResNet_vd in_channels: 3 layers: 50 Neck: @@ -40,7 +40,7 @@ Architecture: model_type: det algorithm: DB Backbone: - name: ResNet + name: ResNet_vd in_channels: 3 layers: 50 Neck: @@ -60,7 +60,7 @@ Loss: - ["Student", "Student2"] maps_name: "thrink_maps" weight: 1.0 - act: "softmax" + # act: None model_name_pairs: ["Student", "Student2"] key: maps - DistillationDBLoss: diff --git a/configs/det/ch_ppocr_v2.0/ch_det_res18_db_v2.0.yml b/configs/det/ch_ppocr_v2.0/ch_det_res18_db_v2.0.yml index 7b07ef99648956a70b5a71f1e61f09b592226f90..e983c221e2cea87e35b1c5f0a67daed0a55c4257 100644 --- a/configs/det/ch_ppocr_v2.0/ch_det_res18_db_v2.0.yml +++ b/configs/det/ch_ppocr_v2.0/ch_det_res18_db_v2.0.yml @@ -20,7 +20,7 @@ Architecture: algorithm: DB Transform: Backbone: - name: ResNet + name: ResNet_vd layers: 18 disable_se: True Neck: diff --git a/configs/det/det_mv3_db.yml b/configs/det/det_mv3_db.yml index 6edf0b9194ee59143e287394f505b60010ec6644..2f39fbd232fa4bcab4cd30622d21c56d11a72d31 100644 --- a/configs/det/det_mv3_db.yml +++ b/configs/det/det_mv3_db.yml @@ -101,7 +101,7 @@ Train: drop_last: False batch_size_per_card: 16 num_workers: 8 - use_shared_memory: False + use_shared_memory: True Eval: dataset: @@ -129,4 +129,4 @@ Eval: drop_last: False batch_size_per_card: 1 # must be 1 num_workers: 8 - use_shared_memory: False + use_shared_memory: True diff --git a/configs/det/det_r50_db++_icdar15.yml b/configs/det/det_r50_db++_icdar15.yml new file mode 100644 index 0000000000000000000000000000000000000000..e0cd6012b660573a79ff013a1b6e2309074a3d86 --- /dev/null +++ b/configs/det/det_r50_db++_icdar15.yml @@ -0,0 +1,163 @@ +Global: + debug: false + use_gpu: true + epoch_num: 1000 + log_smooth_window: 20 + print_batch_step: 10 + save_model_dir: ./output/det_r50_icdar15/ + save_epoch_step: 200 + eval_batch_step: + - 0 + - 2000 + cal_metric_during_train: false + pretrained_model: ./pretrain_models/ResNet50_dcn_asf_synthtext_pretrained + checkpoints: null + save_inference_dir: null + use_visualdl: false + infer_img: doc/imgs_en/img_10.jpg + save_res_path: ./checkpoints/det_db/predicts_db.txt +Architecture: + model_type: det + algorithm: DB++ + Transform: null + Backbone: + name: ResNet + layers: 50 + dcn_stage: [False, True, True, True] + Neck: + name: DBFPN + out_channels: 256 + use_asf: True + Head: + name: DBHead + k: 50 +Loss: + name: DBLoss + balance_loss: true + main_loss_type: BCELoss + alpha: 5 + beta: 10 + ohem_ratio: 3 +Optimizer: + name: Momentum + momentum: 0.9 + lr: + name: DecayLearningRate + learning_rate: 0.007 + epochs: 1000 + factor: 0.9 + end_lr: 0 + weight_decay: 0.0001 +PostProcess: + name: DBPostProcess + thresh: 0.3 + box_thresh: 0.6 + max_candidates: 1000 + unclip_ratio: 1.5 +Metric: + name: DetMetric + main_indicator: hmean +Train: + dataset: + name: SimpleDataSet + data_dir: ./train_data/icdar2015/text_localization/ + label_file_list: + - ./train_data/icdar2015/text_localization/train_icdar2015_label.txt + ratio_list: + - 1.0 + transforms: + - DecodeImage: + img_mode: BGR + channel_first: false + - DetLabelEncode: null + - IaaAugment: + augmenter_args: + - type: Fliplr + args: + p: 0.5 + - type: Affine + args: + rotate: + - -10 + - 10 + - type: Resize + args: + size: + - 0.5 + - 3 + - EastRandomCropData: + size: + - 640 + - 640 + max_tries: 10 + keep_ratio: true + - MakeShrinkMap: + shrink_ratio: 0.4 + min_text_size: 8 + - MakeBorderMap: + shrink_ratio: 0.4 + thresh_min: 0.3 + thresh_max: 0.7 + - NormalizeImage: + scale: 1./255. + mean: + - 0.48109378172549 + - 0.45752457890196 + - 0.40787054090196 + std: + - 1.0 + - 1.0 + - 1.0 + order: hwc + - ToCHWImage: null + - KeepKeys: + keep_keys: + - image + - threshold_map + - threshold_mask + - shrink_map + - shrink_mask + loader: + shuffle: true + drop_last: false + batch_size_per_card: 4 + 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: + img_mode: BGR + channel_first: false + - DetLabelEncode: null + - DetResizeForTest: + image_shape: + - 1152 + - 2048 + - NormalizeImage: + scale: 1./255. + mean: + - 0.48109378172549 + - 0.45752457890196 + - 0.40787054090196 + std: + - 1.0 + - 1.0 + - 1.0 + order: hwc + - ToCHWImage: null + - KeepKeys: + keep_keys: + - image + - shape + - polys + - ignore_tags + loader: + shuffle: false + drop_last: false + batch_size_per_card: 1 + num_workers: 2 +profiler_options: null diff --git a/configs/det/det_r50_db++_td_tr.yml b/configs/det/det_r50_db++_td_tr.yml new file mode 100644 index 0000000000000000000000000000000000000000..65021bb66184381ba732980ac1b7a65d7bd3a355 --- /dev/null +++ b/configs/det/det_r50_db++_td_tr.yml @@ -0,0 +1,166 @@ +Global: + debug: false + use_gpu: true + epoch_num: 1000 + log_smooth_window: 20 + print_batch_step: 10 + save_model_dir: ./output/det_r50_td_tr/ + save_epoch_step: 200 + eval_batch_step: + - 0 + - 2000 + cal_metric_during_train: false + pretrained_model: ./pretrain_models/ResNet50_dcn_asf_synthtext_pretrained + checkpoints: null + save_inference_dir: null + use_visualdl: false + infer_img: doc/imgs_en/img_10.jpg + save_res_path: ./checkpoints/det_db/predicts_db.txt +Architecture: + model_type: det + algorithm: DB++ + Transform: null + Backbone: + name: ResNet + layers: 50 + dcn_stage: [False, True, True, True] + Neck: + name: DBFPN + out_channels: 256 + use_asf: True + Head: + name: DBHead + k: 50 +Loss: + name: DBLoss + balance_loss: true + main_loss_type: BCELoss + alpha: 5 + beta: 10 + ohem_ratio: 3 +Optimizer: + name: Momentum + momentum: 0.9 + lr: + name: DecayLearningRate + learning_rate: 0.007 + epochs: 1000 + factor: 0.9 + end_lr: 0 + weight_decay: 0.0001 +PostProcess: + name: DBPostProcess + thresh: 0.3 + box_thresh: 0.5 + max_candidates: 1000 + unclip_ratio: 1.5 +Metric: + name: DetMetric + main_indicator: hmean +Train: + dataset: + name: SimpleDataSet + data_dir: ./train_data/ + label_file_list: + - ./train_data/TD_TR/TD500/train_gt_labels.txt + - ./train_data/TD_TR/TR400/gt_labels.txt + ratio_list: + - 1.0 + - 1.0 + transforms: + - DecodeImage: + img_mode: BGR + channel_first: false + - DetLabelEncode: null + - IaaAugment: + augmenter_args: + - type: Fliplr + args: + p: 0.5 + - type: Affine + args: + rotate: + - -10 + - 10 + - type: Resize + args: + size: + - 0.5 + - 3 + - EastRandomCropData: + size: + - 640 + - 640 + max_tries: 10 + keep_ratio: true + - MakeShrinkMap: + shrink_ratio: 0.4 + min_text_size: 8 + - MakeBorderMap: + shrink_ratio: 0.4 + thresh_min: 0.3 + thresh_max: 0.7 + - NormalizeImage: + scale: 1./255. + mean: + - 0.48109378172549 + - 0.45752457890196 + - 0.40787054090196 + std: + - 1.0 + - 1.0 + - 1.0 + order: hwc + - ToCHWImage: null + - KeepKeys: + keep_keys: + - image + - threshold_map + - threshold_mask + - shrink_map + - shrink_mask + loader: + shuffle: true + drop_last: false + batch_size_per_card: 4 + num_workers: 8 +Eval: + dataset: + name: SimpleDataSet + data_dir: ./train_data/ + label_file_list: + - ./train_data/TD_TR/TD500/test_gt_labels.txt + transforms: + - DecodeImage: + img_mode: BGR + channel_first: false + - DetLabelEncode: null + - DetResizeForTest: + image_shape: + - 736 + - 736 + keep_ratio: True + - NormalizeImage: + scale: 1./255. + mean: + - 0.48109378172549 + - 0.45752457890196 + - 0.40787054090196 + std: + - 1.0 + - 1.0 + - 1.0 + order: hwc + - ToCHWImage: null + - KeepKeys: + keep_keys: + - image + - shape + - polys + - ignore_tags + loader: + shuffle: false + drop_last: false + batch_size_per_card: 1 + num_workers: 2 +profiler_options: null diff --git a/configs/det/det_r50_vd_db.yml b/configs/det/det_r50_vd_db.yml index ab67786ece2db9c082ad0484e9dd9a71a795c2d7..288dcc8c1a5934b35d329acc38fa85451ebaeb19 100644 --- a/configs/det/det_r50_vd_db.yml +++ b/configs/det/det_r50_vd_db.yml @@ -20,7 +20,7 @@ Architecture: algorithm: DB Transform: Backbone: - name: ResNet + name: ResNet_vd layers: 50 Neck: name: DBFPN diff --git a/configs/det/det_r50_vd_dcn_fce_ctw.yml b/configs/det/det_r50_vd_dcn_fce_ctw.yml index a9f7c4143d4e9380c819f8cbc39d69f0149111b2..3a4075b322a173796f26ebdbe5b83ba98e98e72d 100755 --- a/configs/det/det_r50_vd_dcn_fce_ctw.yml +++ b/configs/det/det_r50_vd_dcn_fce_ctw.yml @@ -21,7 +21,7 @@ Architecture: algorithm: FCE Transform: Backbone: - name: ResNet + name: ResNet_vd layers: 50 dcn_stage: [False, True, True, True] out_indices: [1,2,3] diff --git a/configs/det/det_r50_vd_east.yml b/configs/det/det_r50_vd_east.yml index e84a5fa7a7af34bde5e0abc6fed2e01f6ce42e6b..af90ef0adb929955141ef31779e917e4d73057ee 100644 --- a/configs/det/det_r50_vd_east.yml +++ b/configs/det/det_r50_vd_east.yml @@ -20,7 +20,7 @@ Architecture: algorithm: EAST Transform: Backbone: - name: ResNet + name: ResNet_vd layers: 50 Neck: name: EASTFPN diff --git a/configs/det/det_r50_vd_pse.yml b/configs/det/det_r50_vd_pse.yml index 8e77506c410af5397a04f73674b414cb28a87c4d..1a971564fda2ce89bf091808dedb361f1caeddc3 100644 --- a/configs/det/det_r50_vd_pse.yml +++ b/configs/det/det_r50_vd_pse.yml @@ -20,7 +20,7 @@ Architecture: algorithm: PSE Transform: Backbone: - name: ResNet + name: ResNet_vd layers: 50 Neck: name: FPN diff --git a/configs/det/det_res18_db_v2.0.yml b/configs/det/det_res18_db_v2.0.yml index 7b07ef99648956a70b5a71f1e61f09b592226f90..e983c221e2cea87e35b1c5f0a67daed0a55c4257 100644 --- a/configs/det/det_res18_db_v2.0.yml +++ b/configs/det/det_res18_db_v2.0.yml @@ -20,7 +20,7 @@ Architecture: algorithm: DB Transform: Backbone: - name: ResNet + name: ResNet_vd layers: 18 disable_se: True Neck: diff --git a/configs/kie/kie_unet_sdmgr.yml b/configs/kie/kie_unet_sdmgr.yml index a6968aaa3aa7a717a848416efc5ccc567f774b4d..da2e4fda504fcbff280788b0baf6d803cf75fe4b 100644 --- a/configs/kie/kie_unet_sdmgr.yml +++ b/configs/kie/kie_unet_sdmgr.yml @@ -17,7 +17,7 @@ Global: checkpoints: save_inference_dir: use_visualdl: False - class_path: ./train_data/wildreceipt/class_list.txt + class_path: &class_path ./train_data/wildreceipt/class_list.txt infer_img: ./train_data/wildreceipt/1.txt save_res_path: ./output/sdmgr_kie/predicts_kie.txt img_scale: [ 1024, 512 ] @@ -72,6 +72,7 @@ Train: order: 'hwc' - KieLabelEncode: # Class handling label character_dict_path: ./train_data/wildreceipt/dict.txt + class_path: *class_path - KieResize: - ToCHWImage: - KeepKeys: @@ -88,7 +89,6 @@ Eval: data_dir: ./train_data/wildreceipt label_file_list: - ./train_data/wildreceipt/wildreceipt_test.txt - # - /paddle/data/PaddleOCR/train_data/wildreceipt/1.txt transforms: - DecodeImage: # load image img_mode: RGB diff --git a/configs/rec/rec_mtb_nrtr.yml b/configs/rec/rec_mtb_nrtr.yml index 4e5826adc990c30aaee1d63fd5b4523944906eee..eacde2965cad1954f9471ba11635936b3654da4b 100644 --- a/configs/rec/rec_mtb_nrtr.yml +++ b/configs/rec/rec_mtb_nrtr.yml @@ -82,7 +82,7 @@ Train: Eval: dataset: name: LMDBDataSet - data_dir: ./train_data/data_lmdb_release/validation/ + data_dir: ./train_data/data_lmdb_release/evaluaiton/ transforms: - DecodeImage: # load image img_mode: BGR diff --git a/configs/rec/rec_r32_gaspin_bilstm_att.yml b/configs/rec/rec_r32_gaspin_bilstm_att.yml new file mode 100644 index 0000000000000000000000000000000000000000..aea71388f703376120af4d0caf2fa8ccd4d92cce --- /dev/null +++ b/configs/rec/rec_r32_gaspin_bilstm_att.yml @@ -0,0 +1,116 @@ +Global: + use_gpu: True + epoch_num: 6 + log_smooth_window: 50 + print_batch_step: 50 + save_model_dir: ./output/rec/rec_r32_gaspin_bilstm_att/ + save_epoch_step: 3 + # evaluation is run every 2000 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/dict/spin_dict.txt + max_text_length: 25 + infer_mode: False + use_space_char: False + save_res_path: ./output/rec/predicts_r32_gaspin_bilstm_att.txt + + +Optimizer: + name: AdamW + beta1: 0.9 + beta2: 0.999 + lr: + name: Piecewise + decay_epochs: [3, 4, 5] + values: [0.001, 0.0003, 0.00009, 0.000027] + clip_norm: 5 + +Architecture: + model_type: rec + algorithm: SPIN + in_channels: 1 + Transform: + name: GA_SPIN + offsets: True + default_type: 6 + loc_lr: 0.1 + stn: True + Backbone: + name: ResNet32 + out_channels: 512 + Neck: + name: SequenceEncoder + encoder_type: cascadernn + hidden_size: 256 + out_channels: [256, 512] + with_linear: True + Head: + name: SPINAttentionHead + hidden_size: 256 + + +Loss: + name: SPINAttentionLoss + ignore_index: 0 + +PostProcess: + name: SPINLabelDecode + use_space_char: False + + +Metric: + name: RecMetric + main_indicator: acc + is_filter: True + +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 + - SPINLabelEncode: # Class handling label + - SPINRecResizeImg: + image_shape: [100, 32] + interpolation : 2 + mean: [127.5] + std: [127.5] + - KeepKeys: + keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order + loader: + shuffle: True + batch_size_per_card: 8 + drop_last: True + 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 + - SPINLabelEncode: # Class handling label + - SPINRecResizeImg: + image_shape: [100, 32] + interpolation : 2 + mean: [127.5] + std: [127.5] + - KeepKeys: + keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order + loader: + shuffle: False + drop_last: False + batch_size_per_card: 8 + num_workers: 2 diff --git a/configs/rec/rec_r45_abinet.yml b/configs/rec/rec_r45_abinet.yml index e604bcd12d9c06d1358e250bc76700af920db93b..bc9048a2005044874058179e8d683437a90a8519 100644 --- a/configs/rec/rec_r45_abinet.yml +++ b/configs/rec/rec_r45_abinet.yml @@ -8,7 +8,7 @@ Global: # evaluation is run every 2000 iterations eval_batch_step: [0, 2000] cal_metric_during_train: True - pretrained_model: + pretrained_model: ./pretrain_models/abinet_vl_pretrained checkpoints: save_inference_dir: use_visualdl: False @@ -82,7 +82,7 @@ Train: Eval: dataset: name: LMDBDataSet - data_dir: ./train_data/data_lmdb_release/validation/ + data_dir: ./train_data/data_lmdb_release/evaluation/ transforms: - DecodeImage: # load image img_mode: RGB diff --git a/configs/rec/rec_svtrnet.yml b/configs/rec/rec_svtrnet.yml index c1f5cc380ab9652e3ac750f8b8edacde9837daf0..e8ceefead6e42de5167984ffa0c18f7ecb03157b 100644 --- a/configs/rec/rec_svtrnet.yml +++ b/configs/rec/rec_svtrnet.yml @@ -77,7 +77,7 @@ Metric: Train: dataset: name: LMDBDataSet - data_dir: ./train_data/data_lmdb_release/training + data_dir: ./train_data/data_lmdb_release/training/ transforms: - DecodeImage: # load image img_mode: BGR @@ -97,7 +97,7 @@ Train: Eval: dataset: name: LMDBDataSet - data_dir: ./train_data/data_lmdb_release/validation + data_dir: ./train_data/data_lmdb_release/evaluation/ transforms: - DecodeImage: # load image img_mode: BGR diff --git a/configs/rec/rec_vitstr_none_ce.yml b/configs/rec/rec_vitstr_none_ce.yml index b969c83a5d77b8c8ffde97ce5f914512074cf26e..ebe304fa4bad3a33b4a226e761ccabbfda41e202 100644 --- a/configs/rec/rec_vitstr_none_ce.yml +++ b/configs/rec/rec_vitstr_none_ce.yml @@ -81,7 +81,7 @@ Train: Eval: dataset: name: LMDBDataSet - data_dir: ./train_data/data_lmdb_release/validation/ + data_dir: ./train_data/data_lmdb_release/evaluation/ transforms: - DecodeImage: # load image img_mode: BGR diff --git a/configs/table/table_master.yml b/configs/table/table_master.yml new file mode 100755 index 0000000000000000000000000000000000000000..b8daf3630755e61322665b6fc5f830e4a45875b8 --- /dev/null +++ b/configs/table/table_master.yml @@ -0,0 +1,136 @@ +Global: + use_gpu: true + epoch_num: 17 + log_smooth_window: 20 + print_batch_step: 100 + save_model_dir: ./output/table_master/ + save_epoch_step: 17 + eval_batch_step: [0, 6259] + cal_metric_during_train: true + pretrained_model: null + checkpoints: + save_inference_dir: output/table_master/infer + use_visualdl: false + infer_img: ppstructure/docs/table/table.jpg + save_res_path: ./output/table_master + character_dict_path: ppocr/utils/dict/table_master_structure_dict.txt + infer_mode: false + max_text_length: 500 + process_total_num: 0 + process_cut_num: 0 + + +Optimizer: + name: Adam + beta1: 0.9 + beta2: 0.999 + lr: + name: MultiStepDecay + learning_rate: 0.001 + milestones: [12, 15] + gamma: 0.1 + warmup_epoch: 0.02 + regularizer: + name: L2 + factor: 0.0 + +Architecture: + model_type: table + algorithm: TableMaster + Backbone: + name: TableResNetExtra + gcb_config: + ratio: 0.0625 + headers: 1 + att_scale: False + fusion_type: channel_add + layers: [False, True, True, True] + layers: [1,2,5,3] + Head: + name: TableMasterHead + hidden_size: 512 + headers: 8 + dropout: 0 + d_ff: 2024 + max_text_length: 500 + +Loss: + name: TableMasterLoss + ignore_index: 42 # set to len of dict + 3 + +PostProcess: + name: TableMasterLabelDecode + box_shape: pad + +Metric: + name: TableMetric + main_indicator: acc + compute_bbox_metric: False + +Train: + dataset: + name: PubTabDataSet + data_dir: train_data/table/pubtabnet/train/ + label_file_list: [train_data/table/pubtabnet/PubTabNet_2.0.0_train.jsonl] + transforms: + - DecodeImage: + img_mode: BGR + channel_first: False + - TableMasterLabelEncode: + learn_empty_box: False + merge_no_span_structure: True + replace_empty_cell_token: True + - ResizeTableImage: + max_len: 480 + resize_bboxes: True + - PaddingTableImage: + size: [480, 480] + - TableBoxEncode: + use_xywh: True + - NormalizeImage: + scale: 1./255. + mean: [0.5, 0.5, 0.5] + std: [0.5, 0.5, 0.5] + order: hwc + - ToCHWImage: null + - KeepKeys: + keep_keys: [image, structure, bboxes, bbox_masks, shape] + loader: + shuffle: True + batch_size_per_card: 10 + drop_last: True + num_workers: 8 + +Eval: + dataset: + name: PubTabDataSet + data_dir: train_data/table/pubtabnet/val/ + label_file_list: [train_data/table/pubtabnet/PubTabNet_2.0.0_val.jsonl] + transforms: + - DecodeImage: + img_mode: BGR + channel_first: False + - TableMasterLabelEncode: + learn_empty_box: False + merge_no_span_structure: True + replace_empty_cell_token: True + - ResizeTableImage: + max_len: 480 + resize_bboxes: True + - PaddingTableImage: + size: [480, 480] + - TableBoxEncode: + use_xywh: True + - NormalizeImage: + scale: 1./255. + mean: [0.5, 0.5, 0.5] + std: [0.5, 0.5, 0.5] + order: hwc + - ToCHWImage: null + - KeepKeys: + keep_keys: [image, structure, bboxes, bbox_masks, shape] + loader: + shuffle: False + drop_last: False + batch_size_per_card: 10 + num_workers: 8 \ No newline at end of file diff --git a/configs/table/table_mv3.yml b/configs/table/table_mv3.yml index 1a91ea95afb4ff91d3fd68fe0df6afaac9304661..66c1c83e124d4e94e1f4036a494dfd80c840f229 100755 --- a/configs/table/table_mv3.yml +++ b/configs/table/table_mv3.yml @@ -4,21 +4,20 @@ Global: log_smooth_window: 20 print_batch_step: 5 save_model_dir: ./output/table_mv3/ - save_epoch_step: 3 + save_epoch_step: 400 # evaluation is run every 400 iterations after the 0th iteration eval_batch_step: [0, 400] cal_metric_during_train: True pretrained_model: - checkpoints: + checkpoints: save_inference_dir: use_visualdl: False - infer_img: doc/table/table.jpg + infer_img: ppstructure/docs/table/table.jpg + save_res_path: output/table_mv3 # for data or label process character_dict_path: ppocr/utils/dict/table_structure_dict.txt character_type: en - max_text_length: 100 - max_elem_length: 800 - max_cell_num: 500 + max_text_length: 800 infer_mode: False process_total_num: 0 process_cut_num: 0 @@ -44,11 +43,8 @@ Architecture: Head: name: TableAttentionHead hidden_size: 256 - l2_decay: 0.00001 loc_type: 2 - max_text_length: 100 - max_elem_length: 800 - max_cell_num: 500 + max_text_length: 800 Loss: name: TableAttentionLoss @@ -61,28 +57,34 @@ PostProcess: Metric: name: TableMetric main_indicator: acc + compute_bbox_metric: false # cost many time, set False for training Train: dataset: name: PubTabDataSet data_dir: train_data/table/pubtabnet/train/ - label_file_path: train_data/table/pubtabnet/PubTabNet_2.0.0_train.jsonl + label_file_list: [train_data/table/pubtabnet/PubTabNet_2.0.0_train.jsonl] transforms: - DecodeImage: # load image img_mode: BGR channel_first: False + - TableLabelEncode: + learn_empty_box: False + merge_no_span_structure: False + replace_empty_cell_token: False + - TableBoxEncode: - ResizeTableImage: max_len: 488 - - TableLabelEncode: - NormalizeImage: scale: 1./255. mean: [0.485, 0.456, 0.406] std: [0.229, 0.224, 0.225] order: 'hwc' - PaddingTableImage: + size: [488, 488] - ToCHWImage: - KeepKeys: - keep_keys: ['image', 'structure', 'bbox_list', 'sp_tokens', 'bbox_list_mask'] + keep_keys: [ 'image', 'structure', 'bboxes', 'bbox_masks', 'shape' ] loader: shuffle: True batch_size_per_card: 32 @@ -92,24 +94,29 @@ Train: Eval: dataset: name: PubTabDataSet - data_dir: train_data/table/pubtabnet/val/ - label_file_path: train_data/table/pubtabnet/PubTabNet_2.0.0_val.jsonl + data_dir: /home/zhoujun20/table/PubTabNe/pubtabnet/val/ + label_file_list: [/home/zhoujun20/table/PubTabNe/pubtabnet/val_500.jsonl] transforms: - DecodeImage: # load image img_mode: BGR channel_first: False + - TableLabelEncode: + learn_empty_box: False + merge_no_span_structure: False + replace_empty_cell_token: False + - TableBoxEncode: - ResizeTableImage: max_len: 488 - - TableLabelEncode: - NormalizeImage: scale: 1./255. mean: [0.485, 0.456, 0.406] std: [0.229, 0.224, 0.225] order: 'hwc' - PaddingTableImage: + size: [488, 488] - ToCHWImage: - KeepKeys: - keep_keys: ['image', 'structure', 'bbox_list', 'sp_tokens', 'bbox_list_mask'] + keep_keys: [ 'image', 'structure', 'bboxes', 'bbox_masks', 'shape' ] loader: shuffle: False drop_last: False diff --git a/configs/vqa/re/layoutlmv2_funsd.yml b/configs/vqa/re/layoutlmv2_funsd.yml new file mode 100644 index 0000000000000000000000000000000000000000..1c3d8f7854cab2fe71a2c22738c0ea1252753998 --- /dev/null +++ b/configs/vqa/re/layoutlmv2_funsd.yml @@ -0,0 +1,125 @@ +Global: + use_gpu: True + epoch_num: &epoch_num 200 + log_smooth_window: 10 + print_batch_step: 10 + save_model_dir: ./output/re_layoutlmv2_funsd + save_epoch_step: 2000 + # evaluation is run every 10 iterations after the 0th iteration + eval_batch_step: [ 0, 57 ] + cal_metric_during_train: False + save_inference_dir: + use_visualdl: False + seed: 2022 + infer_img: train_data/FUNSD/testing_data/images/83624198.png + save_res_path: ./output/re_layoutlmv2_funsd/res/ + +Architecture: + model_type: vqa + algorithm: &algorithm "LayoutLMv2" + Transform: + Backbone: + name: LayoutLMv2ForRe + pretrained: True + checkpoints: + +Loss: + name: LossFromOutput + key: loss + reduction: mean + +Optimizer: + name: AdamW + beta1: 0.9 + beta2: 0.999 + clip_norm: 10 + lr: + learning_rate: 0.00005 + warmup_epoch: 10 + regularizer: + name: L2 + factor: 0.00000 + +PostProcess: + name: VQAReTokenLayoutLMPostProcess + +Metric: + name: VQAReTokenMetric + main_indicator: hmean + +Train: + dataset: + name: SimpleDataSet + data_dir: ./train_data/FUNSD/training_data/images/ + label_file_list: + - ./train_data/FUNSD/train.json + ratio_list: [ 1.0 ] + transforms: + - DecodeImage: # load image + img_mode: RGB + channel_first: False + - VQATokenLabelEncode: # Class handling label + contains_re: True + algorithm: *algorithm + class_path: &class_path train_data/FUNSD/class_list.txt + - VQATokenPad: + max_seq_len: &max_seq_len 512 + return_attention_mask: True + - VQAReTokenRelation: + - VQAReTokenChunk: + max_seq_len: *max_seq_len + - Resize: + size: [224,224] + - NormalizeImage: + scale: 1./255. + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'entities', 'relations'] + loader: + shuffle: True + drop_last: False + batch_size_per_card: 8 + num_workers: 8 + collate_fn: ListCollator + +Eval: + dataset: + name: SimpleDataSet + data_dir: ./train_data/FUNSD/testing_data/images/ + label_file_list: + - ./train_data/FUNSD/test.json + transforms: + - DecodeImage: # load image + img_mode: RGB + channel_first: False + - VQATokenLabelEncode: # Class handling label + contains_re: True + algorithm: *algorithm + class_path: *class_path + - VQATokenPad: + max_seq_len: *max_seq_len + return_attention_mask: True + - VQAReTokenRelation: + - VQAReTokenChunk: + max_seq_len: *max_seq_len + - Resize: + size: [224,224] + - NormalizeImage: + scale: 1./255. + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'entities', 'relations'] + loader: + shuffle: False + drop_last: False + batch_size_per_card: 8 + num_workers: 8 + collate_fn: ListCollator diff --git a/configs/vqa/re/layoutlmv2.yml b/configs/vqa/re/layoutlmv2_xund_zh.yml similarity index 80% rename from configs/vqa/re/layoutlmv2.yml rename to configs/vqa/re/layoutlmv2_xund_zh.yml index 2fa5fd1165c20bbfa8d8505bbb53d48744daebef..986b9b5cef17bf6b8347ee47f7e045ac0ed13124 100644 --- a/configs/vqa/re/layoutlmv2.yml +++ b/configs/vqa/re/layoutlmv2_xund_zh.yml @@ -3,16 +3,16 @@ Global: epoch_num: &epoch_num 200 log_smooth_window: 10 print_batch_step: 10 - save_model_dir: ./output/re_layoutlmv2/ + save_model_dir: ./output/re_layoutlmv2_xfund_zh save_epoch_step: 2000 # evaluation is run every 10 iterations after the 0th iteration - eval_batch_step: [ 0, 19 ] + eval_batch_step: [ 0, 57 ] cal_metric_during_train: False save_inference_dir: use_visualdl: False seed: 2048 - infer_img: doc/vqa/input/zh_val_21.jpg - save_res_path: ./output/re/ + infer_img: ppstructure/docs/vqa/input/zh_val_21.jpg + save_res_path: ./output/re_layoutlmv2_xfund_zh/res/ Architecture: model_type: vqa @@ -21,7 +21,7 @@ Architecture: Backbone: name: LayoutLMv2ForRe pretrained: True - checkpoints: + checkpoints: Loss: name: LossFromOutput @@ -52,7 +52,7 @@ Train: name: SimpleDataSet data_dir: train_data/XFUND/zh_train/image label_file_list: - - train_data/XFUND/zh_train/xfun_normalize_train.json + - train_data/XFUND/zh_train/train.json ratio_list: [ 1.0 ] transforms: - DecodeImage: # load image @@ -61,7 +61,7 @@ Train: - VQATokenLabelEncode: # Class handling label contains_re: True algorithm: *algorithm - class_path: &class_path ppstructure/vqa/labels/labels_ser.txt + class_path: &class_path train_data/XFUND/class_list_xfun.txt - VQATokenPad: max_seq_len: &max_seq_len 512 return_attention_mask: True @@ -77,7 +77,7 @@ Train: order: 'hwc' - ToCHWImage: - KeepKeys: - keep_keys: [ 'input_ids', 'bbox', 'image', 'attention_mask', 'token_type_ids','entities', 'relations'] # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids','image', 'entities', 'relations'] # dataloader will return list in this order loader: shuffle: True drop_last: False @@ -90,7 +90,7 @@ Eval: name: SimpleDataSet data_dir: train_data/XFUND/zh_val/image label_file_list: - - train_data/XFUND/zh_val/xfun_normalize_val.json + - train_data/XFUND/zh_val/val.json transforms: - DecodeImage: # load image img_mode: RGB @@ -114,7 +114,7 @@ Eval: order: 'hwc' - ToCHWImage: - KeepKeys: - keep_keys: [ 'input_ids', 'bbox', 'image', 'attention_mask', 'token_type_ids','entities', 'relations'] # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image','entities', 'relations'] # dataloader will return list in this order loader: shuffle: False drop_last: False diff --git a/configs/vqa/re/layoutxlm_funsd.yml b/configs/vqa/re/layoutxlm_funsd.yml new file mode 100644 index 0000000000000000000000000000000000000000..af28be10d6e0390f106d0f06d5e9c26b20dcabb8 --- /dev/null +++ b/configs/vqa/re/layoutxlm_funsd.yml @@ -0,0 +1,129 @@ +Global: + use_gpu: True + epoch_num: &epoch_num 200 + log_smooth_window: 10 + print_batch_step: 10 + save_model_dir: ./output/re_layoutxlm_funsd + save_epoch_step: 2000 + # evaluation is run every 10 iterations after the 0th iteration + eval_batch_step: [ 0, 57 ] + cal_metric_during_train: False + save_inference_dir: + use_visualdl: False + seed: 2022 + infer_img: train_data/FUNSD/testing_data/images/83624198.png + save_res_path: ./output/re_layoutxlm_funsd/res/ + +Architecture: + model_type: vqa + algorithm: &algorithm "LayoutXLM" + Transform: + Backbone: + name: LayoutXLMForRe + pretrained: True + checkpoints: + +Loss: + name: LossFromOutput + key: loss + reduction: mean + +Optimizer: + name: AdamW + beta1: 0.9 + beta2: 0.999 + clip_norm: 10 + lr: + learning_rate: 0.00005 + warmup_epoch: 10 + regularizer: + name: L2 + factor: 0.00000 + +PostProcess: + name: VQAReTokenLayoutLMPostProcess + +Metric: + name: VQAReTokenMetric + main_indicator: hmean + +Train: + dataset: + name: SimpleDataSet + data_dir: ./train_data/FUNSD/training_data/images/ + label_file_list: + - ./train_data/FUNSD/train_v4.json + # - ./train_data/FUNSD/train.json + ratio_list: [ 1.0 ] + transforms: + - DecodeImage: # load image + img_mode: RGB + channel_first: False + - VQATokenLabelEncode: # Class handling label + contains_re: True + algorithm: *algorithm + class_path: &class_path ./train_data/FUNSD/class_list.txt + use_textline_bbox_info: &use_textline_bbox_info True + - VQATokenPad: + max_seq_len: &max_seq_len 512 + return_attention_mask: True + - VQAReTokenRelation: + - VQAReTokenChunk: + max_seq_len: *max_seq_len + - Resize: + size: [224,224] + - NormalizeImage: + scale: 1 + mean: [ 123.675, 116.28, 103.53 ] + std: [ 58.395, 57.12, 57.375 ] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'entities', 'relations'] + loader: + shuffle: False + drop_last: False + batch_size_per_card: 8 + num_workers: 16 + collate_fn: ListCollator + +Eval: + dataset: + name: SimpleDataSet + data_dir: ./train_data/FUNSD/testing_data/images/ + label_file_list: + - ./train_data/FUNSD/test_v4.json + # - ./train_data/FUNSD/test.json + transforms: + - DecodeImage: # load image + img_mode: RGB + channel_first: False + - VQATokenLabelEncode: # Class handling label + contains_re: True + algorithm: *algorithm + class_path: *class_path + use_textline_bbox_info: *use_textline_bbox_info + - VQATokenPad: + max_seq_len: *max_seq_len + return_attention_mask: True + - VQAReTokenRelation: + - VQAReTokenChunk: + max_seq_len: *max_seq_len + - Resize: + size: [224,224] + - NormalizeImage: + scale: 1 + mean: [ 123.675, 116.28, 103.53 ] + std: [ 58.395, 57.12, 57.375 ] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'entities', 'relations'] + loader: + shuffle: False + drop_last: False + batch_size_per_card: 8 + num_workers: 8 + collate_fn: ListCollator diff --git a/configs/vqa/re/layoutxlm.yml b/configs/vqa/re/layoutxlm_xfund_zh.yml similarity index 84% rename from configs/vqa/re/layoutxlm.yml rename to configs/vqa/re/layoutxlm_xfund_zh.yml index ff16120ac1be92e989ebfda6af3ccf346dde89cd..d8585bb72593d55578ff3c6cd1401b5a843bb683 100644 --- a/configs/vqa/re/layoutxlm.yml +++ b/configs/vqa/re/layoutxlm_xfund_zh.yml @@ -11,7 +11,7 @@ Global: save_inference_dir: use_visualdl: False seed: 2022 - infer_img: doc/vqa/input/zh_val_21.jpg + infer_img: ppstructure/docs/vqa/input/zh_val_21.jpg save_res_path: ./output/re/ Architecture: @@ -52,7 +52,7 @@ Train: name: SimpleDataSet data_dir: train_data/XFUND/zh_train/image label_file_list: - - train_data/XFUND/zh_train/xfun_normalize_train.json + - train_data/XFUND/zh_train/train.json ratio_list: [ 1.0 ] transforms: - DecodeImage: # load image @@ -61,7 +61,7 @@ Train: - VQATokenLabelEncode: # Class handling label contains_re: True algorithm: *algorithm - class_path: &class_path ppstructure/vqa/labels/labels_ser.txt + class_path: &class_path train_data/XFUND/class_list_xfun.txt - VQATokenPad: max_seq_len: &max_seq_len 512 return_attention_mask: True @@ -77,7 +77,7 @@ Train: order: 'hwc' - ToCHWImage: - KeepKeys: - keep_keys: [ 'input_ids', 'bbox', 'image', 'attention_mask', 'token_type_ids','entities', 'relations'] # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox','attention_mask', 'token_type_ids', 'image', 'entities', 'relations'] # dataloader will return list in this order loader: shuffle: True drop_last: False @@ -90,7 +90,7 @@ Eval: name: SimpleDataSet data_dir: train_data/XFUND/zh_val/image label_file_list: - - train_data/XFUND/zh_val/xfun_normalize_val.json + - train_data/XFUND/zh_val/val.json transforms: - DecodeImage: # load image img_mode: RGB @@ -114,7 +114,7 @@ Eval: order: 'hwc' - ToCHWImage: - KeepKeys: - keep_keys: [ 'input_ids', 'bbox', 'image', 'attention_mask', 'token_type_ids','entities', 'relations'] # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'entities', 'relations'] # dataloader will return list in this order loader: shuffle: False drop_last: False diff --git a/configs/vqa/ser/layoutlm_funsd.yml b/configs/vqa/ser/layoutlm_funsd.yml new file mode 100644 index 0000000000000000000000000000000000000000..0ef3502bc8923df6a04662f6eca032f064896a6b --- /dev/null +++ b/configs/vqa/ser/layoutlm_funsd.yml @@ -0,0 +1,124 @@ +Global: + use_gpu: True + epoch_num: &epoch_num 200 + log_smooth_window: 10 + print_batch_step: 10 + save_model_dir: ./output/ser_layoutlm_funsd + save_epoch_step: 2000 + # evaluation is run every 10 iterations after the 0th iteration + eval_batch_step: [ 0, 57 ] + cal_metric_during_train: False + save_inference_dir: + use_visualdl: False + seed: 2022 + infer_img: train_data/FUNSD/testing_data/images/83624198.png + save_res_path: ./output/ser_layoutlm_funsd/res/ + +Architecture: + model_type: vqa + algorithm: &algorithm "LayoutLM" + Transform: + Backbone: + name: LayoutLMForSer + pretrained: True + checkpoints: + num_classes: &num_classes 7 + +Loss: + name: VQASerTokenLayoutLMLoss + num_classes: *num_classes + +Optimizer: + name: AdamW + beta1: 0.9 + beta2: 0.999 + lr: + name: Linear + learning_rate: 0.00005 + epochs: *epoch_num + warmup_epoch: 2 + regularizer: + name: L2 + factor: 0.00000 + +PostProcess: + name: VQASerTokenLayoutLMPostProcess + class_path: &class_path ./train_data/FUNSD/class_list.txt + +Metric: + name: VQASerTokenMetric + main_indicator: hmean + +Train: + dataset: + name: SimpleDataSet + data_dir: ./train_data/FUNSD/training_data/images/ + label_file_list: + - ./train_data/FUNSD/train.json + transforms: + - DecodeImage: # load image + img_mode: RGB + channel_first: False + - VQATokenLabelEncode: # Class handling label + contains_re: False + algorithm: *algorithm + class_path: *class_path + use_textline_bbox_info: &use_textline_bbox_info True + - VQATokenPad: + max_seq_len: &max_seq_len 512 + return_attention_mask: True + - VQASerTokenChunk: + max_seq_len: *max_seq_len + - Resize: + size: [224,224] + - NormalizeImage: + scale: 1 + mean: [ 123.675, 116.28, 103.53 ] + std: [ 58.395, 57.12, 57.375 ] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] + loader: + shuffle: True + drop_last: False + batch_size_per_card: 8 + num_workers: 4 + +Eval: + dataset: + name: SimpleDataSet + data_dir: train_data/FUNSD/testing_data/images/ + label_file_list: + - ./train_data/FUNSD/test.json + transforms: + - DecodeImage: # load image + img_mode: RGB + channel_first: False + - VQATokenLabelEncode: # Class handling label + contains_re: False + algorithm: *algorithm + class_path: *class_path + use_textline_bbox_info: *use_textline_bbox_info + - VQATokenPad: + max_seq_len: *max_seq_len + return_attention_mask: True + - VQASerTokenChunk: + max_seq_len: *max_seq_len + - Resize: + size: [224,224] + - NormalizeImage: + scale: 1 + mean: [ 123.675, 116.28, 103.53 ] + std: [ 58.395, 57.12, 57.375 ] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] + loader: + shuffle: False + drop_last: False + batch_size_per_card: 8 + num_workers: 4 diff --git a/configs/vqa/ser/layoutlm_sroie.yml b/configs/vqa/ser/layoutlm_sroie.yml new file mode 100644 index 0000000000000000000000000000000000000000..6abb1151e5102710d54fc163283ab7ec14f85ff4 --- /dev/null +++ b/configs/vqa/ser/layoutlm_sroie.yml @@ -0,0 +1,124 @@ +Global: + use_gpu: True + epoch_num: &epoch_num 200 + log_smooth_window: 10 + print_batch_step: 10 + save_model_dir: ./output/ser_layoutlm_sroie + save_epoch_step: 2000 + # evaluation is run every 10 iterations after the 0th iteration + eval_batch_step: [ 0, 200 ] + cal_metric_during_train: False + save_inference_dir: + use_visualdl: False + seed: 2022 + infer_img: train_data/SROIE/test/X00016469670.jpg + save_res_path: ./output/ser_layoutlm_sroie/res/ + +Architecture: + model_type: vqa + algorithm: &algorithm "LayoutLM" + Transform: + Backbone: + name: LayoutLMForSer + pretrained: True + checkpoints: + num_classes: &num_classes 9 + +Loss: + name: VQASerTokenLayoutLMLoss + num_classes: *num_classes + +Optimizer: + name: AdamW + beta1: 0.9 + beta2: 0.999 + lr: + name: Linear + learning_rate: 0.00005 + epochs: *epoch_num + warmup_epoch: 2 + regularizer: + name: L2 + factor: 0.00000 + +PostProcess: + name: VQASerTokenLayoutLMPostProcess + class_path: &class_path ./train_data/SROIE/class_list.txt + +Metric: + name: VQASerTokenMetric + main_indicator: hmean + +Train: + dataset: + name: SimpleDataSet + data_dir: ./train_data/SROIE/train + label_file_list: + - ./train_data/SROIE/train.txt + transforms: + - DecodeImage: # load image + img_mode: RGB + channel_first: False + - VQATokenLabelEncode: # Class handling label + contains_re: False + algorithm: *algorithm + class_path: *class_path + use_textline_bbox_info: &use_textline_bbox_info True + - VQATokenPad: + max_seq_len: &max_seq_len 512 + return_attention_mask: True + - VQASerTokenChunk: + max_seq_len: *max_seq_len + - Resize: + size: [224,224] + - NormalizeImage: + scale: 1 + mean: [ 123.675, 116.28, 103.53 ] + std: [ 58.395, 57.12, 57.375 ] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] + loader: + shuffle: True + drop_last: False + batch_size_per_card: 8 + num_workers: 4 + +Eval: + dataset: + name: SimpleDataSet + data_dir: ./train_data/SROIE/test + label_file_list: + - ./train_data/SROIE/test.txt + transforms: + - DecodeImage: # load image + img_mode: RGB + channel_first: False + - VQATokenLabelEncode: # Class handling label + contains_re: False + algorithm: *algorithm + class_path: *class_path + use_textline_bbox_info: *use_textline_bbox_info + - VQATokenPad: + max_seq_len: *max_seq_len + return_attention_mask: True + - VQASerTokenChunk: + max_seq_len: *max_seq_len + - Resize: + size: [224,224] + - NormalizeImage: + scale: 1 + mean: [ 123.675, 116.28, 103.53 ] + std: [ 58.395, 57.12, 57.375 ] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] + loader: + shuffle: False + drop_last: False + batch_size_per_card: 8 + num_workers: 4 diff --git a/configs/vqa/ser/layoutlm.yml b/configs/vqa/ser/layoutlm_xfund_zh.yml similarity index 80% rename from configs/vqa/ser/layoutlm.yml rename to configs/vqa/ser/layoutlm_xfund_zh.yml index 87131170c9daabd8553269b900893ac26fb32bc8..99763c1963e92a010e5f2d2dc795d0ab90755426 100644 --- a/configs/vqa/ser/layoutlm.yml +++ b/configs/vqa/ser/layoutlm_xfund_zh.yml @@ -3,16 +3,16 @@ Global: epoch_num: &epoch_num 200 log_smooth_window: 10 print_batch_step: 10 - save_model_dir: ./output/ser_layoutlm/ + save_model_dir: ./output/ser_layoutlm_xfund_zh save_epoch_step: 2000 # evaluation is run every 10 iterations after the 0th iteration - eval_batch_step: [ 0, 19 ] + eval_batch_step: [ 0, 57 ] cal_metric_during_train: False save_inference_dir: use_visualdl: False seed: 2022 - infer_img: doc/vqa/input/zh_val_0.jpg - save_res_path: ./output/ser/ + infer_img: ppstructure/docs/vqa/input/zh_val_42.jpg + save_res_path: ./output/ser_layoutlm_xfund_zh/res/ Architecture: model_type: vqa @@ -43,7 +43,7 @@ Optimizer: PostProcess: name: VQASerTokenLayoutLMPostProcess - class_path: &class_path ppstructure/vqa/labels/labels_ser.txt + class_path: &class_path train_data/XFUND/class_list_xfun.txt Metric: name: VQASerTokenMetric @@ -54,7 +54,7 @@ Train: name: SimpleDataSet data_dir: train_data/XFUND/zh_train/image label_file_list: - - train_data/XFUND/zh_train/xfun_normalize_train.json + - train_data/XFUND/zh_train/train.json transforms: - DecodeImage: # load image img_mode: RGB @@ -77,7 +77,7 @@ Train: order: 'hwc' - ToCHWImage: - KeepKeys: - keep_keys: [ 'input_ids','labels', 'bbox', 'image', 'attention_mask', 'token_type_ids'] # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] # dataloader will return list in this order loader: shuffle: True drop_last: False @@ -89,7 +89,7 @@ Eval: name: SimpleDataSet data_dir: train_data/XFUND/zh_val/image label_file_list: - - train_data/XFUND/zh_val/xfun_normalize_val.json + - train_data/XFUND/zh_val/val.json transforms: - DecodeImage: # load image img_mode: RGB @@ -112,7 +112,7 @@ Eval: order: 'hwc' - ToCHWImage: - KeepKeys: - keep_keys: [ 'input_ids', 'labels', 'bbox', 'image', 'attention_mask', 'token_type_ids'] # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] # dataloader will return list in this order loader: shuffle: False drop_last: False diff --git a/configs/vqa/ser/layoutlmv2_funsd.yml b/configs/vqa/ser/layoutlmv2_funsd.yml new file mode 100644 index 0000000000000000000000000000000000000000..438edc1aa7148a641645a00493ad9073e9239eab --- /dev/null +++ b/configs/vqa/ser/layoutlmv2_funsd.yml @@ -0,0 +1,123 @@ +Global: + use_gpu: True + epoch_num: &epoch_num 200 + log_smooth_window: 10 + print_batch_step: 10 + save_model_dir: ./output/ser_layoutlmv2_funsd + save_epoch_step: 2000 + # evaluation is run every 10 iterations after the 0th iteration + eval_batch_step: [ 0, 100 ] + cal_metric_during_train: False + save_inference_dir: + use_visualdl: False + seed: 2022 + infer_img: train_data/FUNSD/testing_data/images/83624198.png + save_res_path: ./output/ser_layoutlmv2_funsd/res/ + +Architecture: + model_type: vqa + algorithm: &algorithm "LayoutLMv2" + Transform: + Backbone: + name: LayoutLMv2ForSer + pretrained: True + checkpoints: + num_classes: &num_classes 7 + +Loss: + name: VQASerTokenLayoutLMLoss + num_classes: *num_classes + +Optimizer: + name: AdamW + beta1: 0.9 + beta2: 0.999 + lr: + name: Linear + learning_rate: 0.00005 + epochs: *epoch_num + warmup_epoch: 2 + regularizer: + + name: L2 + factor: 0.00000 + +PostProcess: + name: VQASerTokenLayoutLMPostProcess + class_path: &class_path train_data/FUNSD/class_list.txt + +Metric: + name: VQASerTokenMetric + main_indicator: hmean + +Train: + dataset: + name: SimpleDataSet + data_dir: ./train_data/FUNSD/training_data/images/ + label_file_list: + - ./train_data/FUNSD/train.json + transforms: + - DecodeImage: # load image + img_mode: RGB + channel_first: False + - VQATokenLabelEncode: # Class handling label + contains_re: False + algorithm: *algorithm + class_path: *class_path + - VQATokenPad: + max_seq_len: &max_seq_len 512 + return_attention_mask: True + - VQASerTokenChunk: + max_seq_len: *max_seq_len + - Resize: + size: [224,224] + - NormalizeImage: + scale: 1 + mean: [ 123.675, 116.28, 103.53 ] + std: [ 58.395, 57.12, 57.375 ] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] + loader: + shuffle: True + drop_last: False + batch_size_per_card: 8 + num_workers: 4 + +Eval: + dataset: + name: SimpleDataSet + data_dir: ./train_data/FUNSD/testing_data/images/ + label_file_list: + - ./train_data/FUNSD/test.json + transforms: + - DecodeImage: # load image + img_mode: RGB + channel_first: False + - VQATokenLabelEncode: # Class handling label + contains_re: False + algorithm: *algorithm + class_path: *class_path + - VQATokenPad: + max_seq_len: *max_seq_len + return_attention_mask: True + - VQASerTokenChunk: + max_seq_len: *max_seq_len + - Resize: + size: [224,224] + - NormalizeImage: + scale: 1 + mean: [ 123.675, 116.28, 103.53 ] + std: [ 58.395, 57.12, 57.375 ] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] + loader: + shuffle: False + drop_last: False + batch_size_per_card: 8 + num_workers: 4 diff --git a/configs/vqa/ser/layoutlmv2_sroie.yml b/configs/vqa/ser/layoutlmv2_sroie.yml new file mode 100644 index 0000000000000000000000000000000000000000..549beb8ec520483fe59db0e6caf509339fb90f76 --- /dev/null +++ b/configs/vqa/ser/layoutlmv2_sroie.yml @@ -0,0 +1,123 @@ +Global: + use_gpu: True + epoch_num: &epoch_num 200 + log_smooth_window: 10 + print_batch_step: 10 + save_model_dir: ./output/ser_layoutlmv2_sroie + save_epoch_step: 2000 + # evaluation is run every 10 iterations after the 0th iteration + eval_batch_step: [ 0, 200 ] + cal_metric_during_train: False + save_inference_dir: + use_visualdl: False + seed: 2022 + infer_img: train_data/SROIE/test/X00016469670.jpg + save_res_path: ./output/ser_layoutlmv2_sroie/res/ + +Architecture: + model_type: vqa + algorithm: &algorithm "LayoutLMv2" + Transform: + Backbone: + name: LayoutLMv2ForSer + pretrained: True + checkpoints: + num_classes: &num_classes 9 + +Loss: + name: VQASerTokenLayoutLMLoss + num_classes: *num_classes + +Optimizer: + name: AdamW + beta1: 0.9 + beta2: 0.999 + lr: + name: Linear + learning_rate: 0.00005 + epochs: *epoch_num + warmup_epoch: 2 + regularizer: + + name: L2 + factor: 0.00000 + +PostProcess: + name: VQASerTokenLayoutLMPostProcess + class_path: &class_path ./train_data/SROIE/class_list.txt + +Metric: + name: VQASerTokenMetric + main_indicator: hmean + +Train: + dataset: + name: SimpleDataSet + data_dir: ./train_data/SROIE/train + label_file_list: + - ./train_data/SROIE/train.txt + transforms: + - DecodeImage: # load image + img_mode: RGB + channel_first: False + - VQATokenLabelEncode: # Class handling label + contains_re: False + algorithm: *algorithm + class_path: *class_path + - VQATokenPad: + max_seq_len: &max_seq_len 512 + return_attention_mask: True + - VQASerTokenChunk: + max_seq_len: *max_seq_len + - Resize: + size: [224,224] + - NormalizeImage: + scale: 1 + mean: [ 123.675, 116.28, 103.53 ] + std: [ 58.395, 57.12, 57.375 ] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] + loader: + shuffle: True + drop_last: False + batch_size_per_card: 8 + num_workers: 4 + +Eval: + dataset: + name: SimpleDataSet + data_dir: ./train_data/SROIE/test + label_file_list: + - ./train_data/SROIE/test.txt + transforms: + - DecodeImage: # load image + img_mode: RGB + channel_first: False + - VQATokenLabelEncode: # Class handling label + contains_re: False + algorithm: *algorithm + class_path: *class_path + - VQATokenPad: + max_seq_len: *max_seq_len + return_attention_mask: True + - VQASerTokenChunk: + max_seq_len: *max_seq_len + - Resize: + size: [224,224] + - NormalizeImage: + scale: 1 + mean: [ 123.675, 116.28, 103.53 ] + std: [ 58.395, 57.12, 57.375 ] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] + loader: + shuffle: False + drop_last: False + batch_size_per_card: 8 + num_workers: 4 diff --git a/configs/vqa/ser/layoutlmv2.yml b/configs/vqa/ser/layoutlmv2_xfund_zh.yml similarity index 81% rename from configs/vqa/ser/layoutlmv2.yml rename to configs/vqa/ser/layoutlmv2_xfund_zh.yml index 33406252b31adf4175d7ea2f57772b0faf33cdab..ebdc5f31695d74d13219c65b420e3da3e1adfe92 100644 --- a/configs/vqa/ser/layoutlmv2.yml +++ b/configs/vqa/ser/layoutlmv2_xfund_zh.yml @@ -3,7 +3,7 @@ Global: epoch_num: &epoch_num 200 log_smooth_window: 10 print_batch_step: 10 - save_model_dir: ./output/ser_layoutlmv2/ + save_model_dir: ./output/ser_layoutlmv2_xfund_zh/ save_epoch_step: 2000 # evaluation is run every 10 iterations after the 0th iteration eval_batch_step: [ 0, 19 ] @@ -11,8 +11,8 @@ Global: save_inference_dir: use_visualdl: False seed: 2022 - infer_img: doc/vqa/input/zh_val_0.jpg - save_res_path: ./output/ser/ + infer_img: ppstructure/docs/vqa/input/zh_val_42.jpg + save_res_path: ./output/ser_layoutlmv2_xfund_zh/res/ Architecture: model_type: vqa @@ -44,7 +44,7 @@ Optimizer: PostProcess: name: VQASerTokenLayoutLMPostProcess - class_path: &class_path ppstructure/vqa/labels/labels_ser.txt + class_path: &class_path train_data/XFUND/class_list_xfun.txt Metric: name: VQASerTokenMetric @@ -55,7 +55,7 @@ Train: name: SimpleDataSet data_dir: train_data/XFUND/zh_train/image label_file_list: - - train_data/XFUND/zh_train/xfun_normalize_train.json + - train_data/XFUND/zh_train/train.json transforms: - DecodeImage: # load image img_mode: RGB @@ -78,7 +78,7 @@ Train: order: 'hwc' - ToCHWImage: - KeepKeys: - keep_keys: [ 'input_ids','labels', 'bbox', 'image', 'attention_mask', 'token_type_ids'] # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] # dataloader will return list in this order loader: shuffle: True drop_last: False @@ -90,7 +90,7 @@ Eval: name: SimpleDataSet data_dir: train_data/XFUND/zh_val/image label_file_list: - - train_data/XFUND/zh_val/xfun_normalize_val.json + - train_data/XFUND/zh_val/val.json transforms: - DecodeImage: # load image img_mode: RGB @@ -113,7 +113,7 @@ Eval: order: 'hwc' - ToCHWImage: - KeepKeys: - keep_keys: [ 'input_ids', 'labels', 'bbox', 'image', 'attention_mask', 'token_type_ids'] # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] # dataloader will return list in this order loader: shuffle: False drop_last: False diff --git a/configs/vqa/ser/layoutxlm_funsd.yml b/configs/vqa/ser/layoutxlm_funsd.yml new file mode 100644 index 0000000000000000000000000000000000000000..be1e9d4f1e986864fc57891d56b4a459df63fd78 --- /dev/null +++ b/configs/vqa/ser/layoutxlm_funsd.yml @@ -0,0 +1,123 @@ +Global: + use_gpu: True + epoch_num: &epoch_num 200 + log_smooth_window: 10 + print_batch_step: 10 + save_model_dir: ./output/ser_layoutxlm_funsd + save_epoch_step: 2000 + # evaluation is run every 10 iterations after the 0th iteration + eval_batch_step: [ 0, 57 ] + cal_metric_during_train: False + save_inference_dir: + use_visualdl: False + seed: 2022 + infer_img: train_data/FUNSD/testing_data/images/83624198.png + save_res_path: output/ser_layoutxlm_funsd/res/ + +Architecture: + model_type: vqa + algorithm: &algorithm "LayoutXLM" + Transform: + Backbone: + name: LayoutXLMForSer + pretrained: True + checkpoints: + num_classes: &num_classes 7 + +Loss: + name: VQASerTokenLayoutLMLoss + num_classes: *num_classes + +Optimizer: + name: AdamW + beta1: 0.9 + beta2: 0.999 + lr: + name: Linear + learning_rate: 0.00005 + epochs: *epoch_num + warmup_epoch: 2 + regularizer: + name: L2 + factor: 0.00000 + +PostProcess: + name: VQASerTokenLayoutLMPostProcess + class_path: &class_path ./train_data/FUNSD/class_list.txt + +Metric: + name: VQASerTokenMetric + main_indicator: hmean + +Train: + dataset: + name: SimpleDataSet + data_dir: ./train_data/FUNSD/training_data/images/ + label_file_list: + - ./train_data/FUNSD/train.json + ratio_list: [ 1.0 ] + transforms: + - DecodeImage: # load image + img_mode: RGB + channel_first: False + - VQATokenLabelEncode: # Class handling label + contains_re: False + algorithm: *algorithm + class_path: *class_path + - VQATokenPad: + max_seq_len: &max_seq_len 512 + return_attention_mask: True + - VQASerTokenChunk: + max_seq_len: *max_seq_len + - Resize: + size: [224,224] + - NormalizeImage: + scale: 1 + mean: [ 123.675, 116.28, 103.53 ] + std: [ 58.395, 57.12, 57.375 ] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] + loader: + shuffle: True + drop_last: False + batch_size_per_card: 8 + num_workers: 4 + +Eval: + dataset: + name: SimpleDataSet + data_dir: train_data/FUNSD/testing_data/images/ + label_file_list: + - ./train_data/FUNSD/test.json + transforms: + - DecodeImage: # load image + img_mode: RGB + channel_first: False + - VQATokenLabelEncode: # Class handling label + contains_re: False + algorithm: *algorithm + class_path: *class_path + - VQATokenPad: + max_seq_len: *max_seq_len + return_attention_mask: True + - VQASerTokenChunk: + max_seq_len: *max_seq_len + - Resize: + size: [224,224] + - NormalizeImage: + scale: 1 + mean: [ 123.675, 116.28, 103.53 ] + std: [ 58.395, 57.12, 57.375 ] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] + loader: + shuffle: False + drop_last: False + batch_size_per_card: 8 + num_workers: 4 diff --git a/configs/vqa/ser/layoutxlm_sroie.yml b/configs/vqa/ser/layoutxlm_sroie.yml new file mode 100644 index 0000000000000000000000000000000000000000..dd63d888d0110d98314858c9189ebcefca16e8e2 --- /dev/null +++ b/configs/vqa/ser/layoutxlm_sroie.yml @@ -0,0 +1,123 @@ +Global: + use_gpu: True + epoch_num: &epoch_num 200 + log_smooth_window: 10 + print_batch_step: 10 + save_model_dir: ./output/ser_layoutxlm_sroie + save_epoch_step: 2000 + # evaluation is run every 10 iterations after the 0th iteration + eval_batch_step: [ 0, 200 ] + cal_metric_during_train: False + save_inference_dir: + use_visualdl: False + seed: 2022 + infer_img: train_data/SROIE/test/X00016469670.jpg + save_res_path: res_img_aug_with_gt + +Architecture: + model_type: vqa + algorithm: &algorithm "LayoutXLM" + Transform: + Backbone: + name: LayoutXLMForSer + pretrained: True + checkpoints: + num_classes: &num_classes 9 + +Loss: + name: VQASerTokenLayoutLMLoss + num_classes: *num_classes + +Optimizer: + name: AdamW + beta1: 0.9 + beta2: 0.999 + lr: + name: Linear + learning_rate: 0.00005 + epochs: *epoch_num + warmup_epoch: 2 + regularizer: + name: L2 + factor: 0.00000 + +PostProcess: + name: VQASerTokenLayoutLMPostProcess + class_path: &class_path ./train_data/SROIE/class_list.txt + +Metric: + name: VQASerTokenMetric + main_indicator: hmean + +Train: + dataset: + name: SimpleDataSet + data_dir: ./train_data/SROIE/train + label_file_list: + - ./train_data/SROIE/train.txt + ratio_list: [ 1.0 ] + transforms: + - DecodeImage: # load image + img_mode: RGB + channel_first: False + - VQATokenLabelEncode: # Class handling label + contains_re: False + algorithm: *algorithm + class_path: *class_path + - VQATokenPad: + max_seq_len: &max_seq_len 512 + return_attention_mask: True + - VQASerTokenChunk: + max_seq_len: *max_seq_len + - Resize: + size: [224,224] + - NormalizeImage: + scale: 1 + mean: [ 123.675, 116.28, 103.53 ] + std: [ 58.395, 57.12, 57.375 ] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] + loader: + shuffle: True + drop_last: False + batch_size_per_card: 8 + num_workers: 4 + +Eval: + dataset: + name: SimpleDataSet + data_dir: train_data/SROIE/test + label_file_list: + - ./train_data/SROIE/test.txt + transforms: + - DecodeImage: # load image + img_mode: RGB + channel_first: False + - VQATokenLabelEncode: # Class handling label + contains_re: False + algorithm: *algorithm + class_path: *class_path + - VQATokenPad: + max_seq_len: *max_seq_len + return_attention_mask: True + - VQASerTokenChunk: + max_seq_len: *max_seq_len + - Resize: + size: [224,224] + - NormalizeImage: + scale: 1 + mean: [ 123.675, 116.28, 103.53 ] + std: [ 58.395, 57.12, 57.375 ] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] + loader: + shuffle: False + drop_last: False + batch_size_per_card: 8 + num_workers: 4 diff --git a/configs/vqa/ser/layoutxlm_wildreceipt.yml b/configs/vqa/ser/layoutxlm_wildreceipt.yml new file mode 100644 index 0000000000000000000000000000000000000000..92c039429646f6a4bee6fb0d5dd94e142246b526 --- /dev/null +++ b/configs/vqa/ser/layoutxlm_wildreceipt.yml @@ -0,0 +1,123 @@ +Global: + use_gpu: True + epoch_num: &epoch_num 100 + log_smooth_window: 10 + print_batch_step: 10 + save_model_dir: ./output/ser_layoutxlm_wildreceipt + save_epoch_step: 2000 + # evaluation is run every 10 iterations after the 0th iteration + eval_batch_step: [ 0, 200 ] + cal_metric_during_train: False + save_inference_dir: + use_visualdl: False + seed: 2022 + infer_img: train_data//wildreceipt/image_files/Image_12/10/845be0dd6f5b04866a2042abd28d558032ef2576.jpeg + save_res_path: ./output/ser_layoutxlm_wildreceipt/res + +Architecture: + model_type: vqa + algorithm: &algorithm "LayoutXLM" + Transform: + Backbone: + name: LayoutXLMForSer + pretrained: True + checkpoints: + num_classes: &num_classes 51 + +Loss: + name: VQASerTokenLayoutLMLoss + num_classes: *num_classes + +Optimizer: + name: AdamW + beta1: 0.9 + beta2: 0.999 + lr: + name: Linear + learning_rate: 0.00005 + epochs: *epoch_num + warmup_epoch: 2 + regularizer: + name: L2 + factor: 0.00000 + +PostProcess: + name: VQASerTokenLayoutLMPostProcess + class_path: &class_path ./train_data/wildreceipt/class_list.txt + +Metric: + name: VQASerTokenMetric + main_indicator: hmean + +Train: + dataset: + name: SimpleDataSet + data_dir: ./train_data/wildreceipt/ + label_file_list: + - ./train_data/wildreceipt/wildreceipt_train.txt + ratio_list: [ 1.0 ] + transforms: + - DecodeImage: # load image + img_mode: RGB + channel_first: False + - VQATokenLabelEncode: # Class handling label + contains_re: False + algorithm: *algorithm + class_path: *class_path + - VQATokenPad: + max_seq_len: &max_seq_len 512 + return_attention_mask: True + - VQASerTokenChunk: + max_seq_len: *max_seq_len + - Resize: + size: [224,224] + - NormalizeImage: + scale: 1 + mean: [ 123.675, 116.28, 103.53 ] + std: [ 58.395, 57.12, 57.375 ] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] + loader: + shuffle: True + drop_last: False + batch_size_per_card: 8 + num_workers: 4 + +Eval: + dataset: + name: SimpleDataSet + data_dir: train_data/wildreceipt + label_file_list: + - ./train_data/wildreceipt/wildreceipt_test.txt + transforms: + - DecodeImage: # load image + img_mode: RGB + channel_first: False + - VQATokenLabelEncode: # Class handling label + contains_re: False + algorithm: *algorithm + class_path: *class_path + - VQATokenPad: + max_seq_len: *max_seq_len + return_attention_mask: True + - VQASerTokenChunk: + max_seq_len: *max_seq_len + - Resize: + size: [224,224] + - NormalizeImage: + scale: 1 + mean: [ 123.675, 116.28, 103.53 ] + std: [ 58.395, 57.12, 57.375 ] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] + loader: + shuffle: False + drop_last: False + batch_size_per_card: 8 + num_workers: 4 diff --git a/configs/vqa/ser/layoutxlm.yml b/configs/vqa/ser/layoutxlm_xfund_zh.yml similarity index 81% rename from configs/vqa/ser/layoutxlm.yml rename to configs/vqa/ser/layoutxlm_xfund_zh.yml index eb1cca5a215dd65ef9c302441d05b482f2622a79..68df7d9f035bf1359951fb3a6fb30b47a929717a 100644 --- a/configs/vqa/ser/layoutxlm.yml +++ b/configs/vqa/ser/layoutxlm_xfund_zh.yml @@ -3,7 +3,7 @@ Global: epoch_num: &epoch_num 200 log_smooth_window: 10 print_batch_step: 10 - save_model_dir: ./output/ser_layoutxlm/ + save_model_dir: ./output/ser_layoutxlm_xfund_zh save_epoch_step: 2000 # evaluation is run every 10 iterations after the 0th iteration eval_batch_step: [ 0, 19 ] @@ -11,8 +11,8 @@ Global: save_inference_dir: use_visualdl: False seed: 2022 - infer_img: doc/vqa/input/zh_val_42.jpg - save_res_path: ./output/ser + infer_img: ppstructure/docs/vqa/input/zh_val_42.jpg + save_res_path: ./output/ser_layoutxlm_xfund_zh/res Architecture: model_type: vqa @@ -43,7 +43,7 @@ Optimizer: PostProcess: name: VQASerTokenLayoutLMPostProcess - class_path: &class_path ppstructure/vqa/labels/labels_ser.txt + class_path: &class_path train_data/XFUND/class_list_xfun.txt Metric: name: VQASerTokenMetric @@ -54,7 +54,7 @@ Train: name: SimpleDataSet data_dir: train_data/XFUND/zh_train/image label_file_list: - - train_data/XFUND/zh_train/xfun_normalize_train.json + - train_data/XFUND/zh_train/train.json ratio_list: [ 1.0 ] transforms: - DecodeImage: # load image @@ -78,7 +78,7 @@ Train: order: 'hwc' - ToCHWImage: - KeepKeys: - keep_keys: [ 'input_ids','labels', 'bbox', 'image', 'attention_mask', 'token_type_ids'] # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] # dataloader will return list in this order loader: shuffle: True drop_last: False @@ -90,7 +90,7 @@ Eval: name: SimpleDataSet data_dir: train_data/XFUND/zh_val/image label_file_list: - - train_data/XFUND/zh_val/xfun_normalize_val.json + - train_data/XFUND/zh_val/val.json transforms: - DecodeImage: # load image img_mode: RGB @@ -113,7 +113,7 @@ Eval: order: 'hwc' - ToCHWImage: - KeepKeys: - keep_keys: [ 'input_ids', 'labels', 'bbox', 'image', 'attention_mask', 'token_type_ids'] # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] # dataloader will return list in this order loader: shuffle: False drop_last: False diff --git a/deploy/avh/README.md b/deploy/avh/README.md index 922fb4da920391d1baf98d82b94569321df9a2bc..0087103146584523c5f2f35e83a992204d93b542 100644 --- a/deploy/avh/README.md +++ b/deploy/avh/README.md @@ -15,20 +15,24 @@ -Running PaddleOCR text recognition model via TVM on bare metal Arm(R) Cortex(R)-M55 CPU and CMSIS-NN -=============================================================== +English | [简体中文](README_ch.md) -This folder contains an example of how to use TVM to run a PaddleOCR model -on bare metal Cortex(R)-M55 CPU and CMSIS-NN. +Running PaddleOCR text recognition model on bare metal Arm(R) Cortex(R)-M55 CPU using Arm Virtual Hardware +====================================================================== -Prerequisites +This folder contains an example of how to run a PaddleOCR model on bare metal [Cortex(R)-M55 CPU](https://www.arm.com/products/silicon-ip-cpu/cortex-m/cortex-m55) using [Arm Virtual Hardware](https://www.arm.com/products/development-tools/simulation/virtual-hardware). + + +Running environment and prerequisites ------------- -If the demo is run in the ci_cpu Docker container provided with TVM, then the following -software will already be installed. +Case 1: If the demo is run in Arm Virtual Hardware Amazon Machine Image(AMI) instance hosted by [AWS](https://aws.amazon.com/marketplace/pp/prodview-urbpq7yo5va7g?sr=0-1&ref_=beagle&applicationId=AWSMPContessa)/[AWS China](https://awsmarketplace.amazonaws.cn/marketplace/pp/prodview-2y7nefntbmybu), the following software will be installed through [configure_avh.sh](./configure_avh.sh) script. It will install automatically when you run the application through [run_demo.sh](./run_demo.sh) script. +You can refer to this [guide](https://arm-software.github.io/AVH/main/examples/html/MicroSpeech.html#amilaunch) to launch an Arm Virtual Hardware AMI instance. + +Case 2: If the demo is run in the [ci_cpu Docker container](https://github.com/apache/tvm/blob/main/docker/Dockerfile.ci_cpu) provided with [TVM](https://github.com/apache/tvm), then the following software will already be installed. -If the demo is not run in the ci_cpu Docker container, then you will need the following: +Case 3: If the demo is not run in the ci_cpu Docker container, then you will need the following: - Software required to build and run the demo (These can all be installed by running - https://github.com/apache/tvm/blob/main/docker/install/ubuntu_install_ethosu_driver_stack.sh .) + tvm/docker/install/ubuntu_install_ethosu_driver_stack.sh.) - [Fixed Virtual Platform (FVP) based on Arm(R) Corstone(TM)-300 software](https://developer.arm.com/tools-and-software/open-source-software/arm-platforms-software/arm-ecosystem-fvps) - [cmake 3.19.5](https://github.com/Kitware/CMake/releases/) - [GCC toolchain from Arm(R)](https://developer.arm.com/-/media/Files/downloads/gnu-rm/10-2020q4/gcc-arm-none-eabi-10-2020-q4-major-x86_64-linux.tar.bz2) @@ -40,19 +44,22 @@ If the demo is not run in the ci_cpu Docker container, then you will need the fo pip install -r ./requirements.txt ``` +In case2 and case3: + +You will need to update your PATH environment variable to include the path to cmake 3.19.5 and the FVP. +For example if you've installed these in ```/opt/arm``` , then you would do the following: +```bash +export PATH=/opt/arm/FVP_Corstone_SSE-300/models/Linux64_GCC-6.4:/opt/arm/cmake/bin:$PATH +``` + You will also need TVM which can either be: + - Installed from TLCPack(see [TLCPack](https://tlcpack.ai/)) - Built from source (see [Install from Source](https://tvm.apache.org/docs/install/from_source.html)) - When building from source, the following need to be set in config.cmake: - set(USE_CMSISNN ON) - set(USE_MICRO ON) - set(USE_LLVM ON) - - Installed from TLCPack nightly(see [TLCPack](https://tlcpack.ai/)) -You will need to update your PATH environment variable to include the path to cmake 3.19.5 and the FVP. -For example if you've installed these in ```/opt/arm``` , then you would do the following: -```bash -export PATH=/opt/arm/FVP_Corstone_SSE-300/models/Linux64_GCC-6.4:/opt/arm/cmake/bin:$PATH -``` Running the demo application ---------------------------- @@ -62,6 +69,12 @@ Type the following command to run the bare metal text recognition application ([ ./run_demo.sh ``` +If you are not able to use Arm Virtual Hardware Amazon Machine Image(AMI) instance hosted by AWS/AWS China, specify argument --enable_FVP to 1 to make the application run on local Fixed Virtual Platforms (FVPs) executables. + +```bash +./run_demo.sh --enable_FVP 1 +``` + If the Ethos(TM)-U platform and/or CMSIS have not been installed in /opt/arm/ethosu then the locations for these can be specified as arguments to run_demo.sh, for example: @@ -70,13 +83,14 @@ the locations for these can be specified as arguments to run_demo.sh, for exampl --ethosu_platform_path /home/tvm-user/ethosu/core_platform ``` -This will: +With [run_demo.sh](./run_demo.sh) to run the demo application, it will: +- Set up running environment by installing the required prerequisites automatically if running in Arm Virtual Hardware Amazon AMI instance(not specify --enable_FVP to 1) - Download a PaddleOCR text recognition model - Use tvmc to compile the text recognition model for Cortex(R)-M55 CPU and CMSIS-NN - Create a C header file inputs.c containing the image data as a C array - Create a C header file outputs.c containing a C array where the output of inference will be stored - Build the demo application -- Run the demo application on a Fixed Virtual Platform (FVP) based on Arm(R) Corstone(TM)-300 software +- Run the demo application on a Arm Virtual Hardware based on Arm(R) Corstone(TM)-300 software - The application will report the text on the image and the corresponding score. Using your own image @@ -92,9 +106,11 @@ python3 ./convert_image.py path/to/image Model description ----------------- -In this demo, the model we use is an English recognition model based on [PP-OCRv3](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_ch/PP-OCRv3_introduction.md). PP-OCRv3 is the third version of the PP-OCR series model released by [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR). This series of models has the following features: +The example is built on [PP-OCRv3](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_ch/PP-OCRv3_introduction.md) English recognition model released by [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR). Since Arm(R) Cortex(R)-M55 CPU does not support rnn operator, we delete the unsupported operator based on the PP-OCRv3 text recognition model to obtain the current 2.7M English recognition model. + +PP-OCRv3 is the third version of the PP-OCR series model. This series of models has the following features: - PP-OCRv3: ultra-lightweight OCR system: detection (3.6M) + direction classifier (1.4M) + recognition (12M) = 17.0M - Support more than 80 kinds of multi-language recognition models, including English, Chinese, French, German, Arabic, Korean, Japanese and so on. For details - Support vertical text recognition, and long text recognition -The text recognition model in PP-OCRv3 supports more than 80 languages. In the process of model development, since Arm(R) Cortex(R)-M55 CPU does not support rnn operator, we delete the unsupported operator based on the PP-OCRv3 text recognition model to obtain the current model. \ No newline at end of file + diff --git a/deploy/avh/README_ch.md b/deploy/avh/README_ch.md index 7e6272ae1258e11aeea7a21dd01400a5bff614ba..35cc9f2b7c141226b3c0a56c50368e09b2f0cdb6 100644 --- a/deploy/avh/README_ch.md +++ b/deploy/avh/README_ch.md @@ -14,6 +14,7 @@ +[English](README.md) | 简体中文 通过TVM在 Arm(R) Cortex(R)-M55 CPU 上运行 PaddleOCR文 本能识别模型 =============================================================== @@ -85,9 +86,9 @@ export PATH=/opt/arm/FVP_Corstone_SSE-300/models/Linux64_GCC-6.4:/opt/arm/cmake/ 模型描述 ----------------- -在这个demo中，我们使用的模型是基于[PP-OCRv3](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_ch/PP-OCRv3_introduction.md)的英文识别模型。 PP-OCRv3是[PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)发布的PP-OCR系列模型的第三个版本。 该系列模型具有以下特点： +在这个demo中，我们使用的模型是基于[PP-OCRv3](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_ch/PP-OCRv3_introduction.md)的英文识别模型。由于Arm(R) Cortex(R)-M55 CPU不支持rnn算子，我们在PP-OCRv3原始文本识别模型的基础上进行适配，最终模型大小为2.7M。 + +PP-OCRv3是[PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)发布的PP-OCR系列模型的第三个版本，该系列模型具有以下特点： - 超轻量级OCR系统：检测（3.6M）+方向分类器（1.4M）+识别（12M）=17.0M。 - 支持80多种多语言识别模型，包括英文、中文、法文、德文、阿拉伯文、韩文、日文等。 - 支持竖排文本识别，长文本识别。 - -PP-OCRv3 中的文本识别模型支持 80 多种语言。 在模型开发过程中，由于Arm(R) Cortex(R)-M55 CPU不支持rnn算子，我们在PP-OCRv3文本识别模型的基础上删除了不支持的算子，得到当前模型。 \ No newline at end of file diff --git a/deploy/avh/configure_avh.sh b/deploy/avh/configure_avh.sh new file mode 100755 index 0000000000000000000000000000000000000000..8042fd81d2379c6f7489d90372dffd2dc10e145e --- /dev/null +++ b/deploy/avh/configure_avh.sh @@ -0,0 +1,79 @@ +#!/bin/bash +# Copyright (c) 2022 Arm Limited and Contributors. All rights reserved. +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you 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. + +set -e +set -u +set -o pipefail + +# Show usage +function show_usage() { + cat <&2 + show_usage >&2 + exit 1 + fi + ;; -*|--*) echo "Error: Unknown flag: $1" >&2 @@ -100,17 +122,27 @@ while (( $# )); do esac done +# Choose running environment: cloud(default) or local environment +Platform="VHT_Corstone_SSE-300_Ethos-U55" +if [ $FVP_enable == "1" ]; then + Platform="FVP_Corstone_SSE-300_Ethos-U55" + echo -e "\e[36mRun application on local Fixed Virtual Platforms (FVPs)\e[0m" +else + if [ ! -d "/opt/arm/" ]; then + sudo ./configure_avh.sh + fi +fi # Directories script_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )" - # Make build directory -rm -rf build make cleanall mkdir -p build cd build +# Get PaddlePaddle inference model +echo -e "\e[36mDownload PaddlePaddle inference model\e[0m" wget https://paddleocr.bj.bcebos.com/tvm/ocr_en.tar tar -xf ocr_en.tar @@ -144,9 +176,9 @@ cd ${script_dir} echo ${script_dir} make -# Run demo executable on the FVP -FVP_Corstone_SSE-300_Ethos-U55 -C cpu0.CFGDTCMSZ=15 \ +# Run demo executable on the AVH +$Platform -C cpu0.CFGDTCMSZ=15 \ -C cpu0.CFGITCMSZ=15 -C mps3_board.uart0.out_file=\"-\" -C mps3_board.uart0.shutdown_tag=\"EXITTHESIM\" \ -C mps3_board.visualisation.disable-visualisation=1 -C mps3_board.telnetterminal0.start_telnet=0 \ -C mps3_board.telnetterminal1.start_telnet=0 -C mps3_board.telnetterminal2.start_telnet=0 -C mps3_board.telnetterminal5.start_telnet=0 \ -./build/demo \ No newline at end of file +./build/demo --stat diff --git a/deploy/lite/crnn_process.cc b/deploy/lite/crnn_process.cc index 6d5fc1504e7b1b3faa35a80662442f60d2e30499..7cd95cddb528e8571d8bab67517ae0140492c536 100644 --- a/deploy/lite/crnn_process.cc +++ b/deploy/lite/crnn_process.cc @@ -34,12 +34,13 @@ cv::Mat CrnnResizeImg(cv::Mat img, float wh_ratio, int rec_image_height) { resize_w = imgW; else resize_w = int(ceilf(imgH * ratio)); - + cv::Mat resize_img; cv::resize(img, resize_img, cv::Size(resize_w, imgH), 0.f, 0.f, cv::INTER_LINEAR); cv::copyMakeBorder(resize_img, resize_img, 0, 0, 0, int(imgW - resize_img.cols), cv::BORDER_CONSTANT, {127, 127, 127}); + return resize_img; } std::vector ReadDict(std::string path) { diff --git a/deploy/lite/ocr_db_crnn.cc b/deploy/lite/ocr_db_crnn.cc index cb2bf7791a4307d4e8d2167197d41d903410e0b4..fde0d07d6c1af769610865da1697e6eef0442899 100644 --- a/deploy/lite/ocr_db_crnn.cc +++ b/deploy/lite/ocr_db_crnn.cc @@ -474,7 +474,7 @@ void system(char **argv){ std::vector rec_times; RunRecModel(boxes, srcimg, rec_predictor, rec_text, rec_text_score, - charactor_dict, cls_predictor, use_direction_classify, &rec_times); + charactor_dict, cls_predictor, use_direction_classify, &rec_times, rec_image_height); //// visualization auto img_vis = Visualization(srcimg, boxes); diff --git a/doc/doc_ch/algorithm.md b/doc/doc_ch/algorithm.md index 3056f35d5260812686447367f7cbddc1e1cad531..d50a5aa4e80336036424bddace9579db98c699c3 100644 --- a/doc/doc_ch/algorithm.md +++ b/doc/doc_ch/algorithm.md @@ -5,9 +5,10 @@ PaddleOCR将**持续新增**支持OCR领域前沿算法与模型，已支持的 - [文本检测算法](./algorithm_overview.md#11-%E6%96%87%E6%9C%AC%E6%A3%80%E6%B5%8B%E7%AE%97%E6%B3%95) - [文本识别算法](./algorithm_overview.md#12-%E6%96%87%E6%9C%AC%E8%AF%86%E5%88%AB%E7%AE%97%E6%B3%95) - [端到端算法](./algorithm_overview.md#2-%E6%96%87%E6%9C%AC%E8%AF%86%E5%88%AB%E7%AE%97%E6%B3%95) +- [表格识别]](./algorithm_overview.md#3-%E8%A1%A8%E6%A0%BC%E8%AF%86%E5%88%AB%E7%AE%97%E6%B3%95) **欢迎广大开发者合作共建，贡献更多算法，合入有奖🎁！具体可查看[社区常规赛](https://github.com/PaddlePaddle/PaddleOCR/issues/4982)。** 新增算法可参考如下教程： -- [使用PaddleOCR架构添加新算法](./add_new_algorithm.md) \ No newline at end of file +- [使用PaddleOCR架构添加新算法](./add_new_algorithm.md) diff --git a/doc/doc_ch/algorithm_det_db.md b/doc/doc_ch/algorithm_det_db.md index 90837c2ac1ebbc04ee47cbb74ed6466352710e88..5401132061e507773ae77be49555ba754d1cba15 100644 --- a/doc/doc_ch/algorithm_det_db.md +++ b/doc/doc_ch/algorithm_det_db.md @@ -1,4 +1,4 @@ -# DB +# DB与DB++ - [1. 算法简介](#1) - [2. 环境配置](#2) @@ -21,12 +21,24 @@ > Liao, Minghui and Wan, Zhaoyi and Yao, Cong and Chen, Kai and Bai, Xiang > AAAI, 2020 +> [Real-Time Scene Text Detection with Differentiable Binarization and Adaptive Scale Fusion](https://arxiv.org/abs/2202.10304) +> Liao, Minghui and Zou, Zhisheng and Wan, Zhaoyi and Yao, Cong and Bai, Xiang +> TPAMI, 2022 + + 在ICDAR2015文本检测公开数据集上，算法复现效果如下： |模型|骨干网络|配置文件|precision|recall|Hmean|下载链接| | --- | --- | --- | --- | --- | --- | --- | |DB|ResNet50_vd|[configs/det/det_r50_vd_db.yml](../../configs/det/det_r50_vd_db.yml)|86.41%|78.72%|82.38%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_v2.0_train.tar)| |DB|MobileNetV3|[configs/det/det_mv3_db.yml](../../configs/det/det_mv3_db.yml)|77.29%|73.08%|75.12%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar)| +|DB++|ResNet50|[configs/det/det_r50_db++_icdar15.yml](../../configs/det/det_r50_db++_icdar15.yml)|90.89%|82.66%|86.58%|[合成数据预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/ResNet50_dcn_asf_synthtext_pretrained.pdparams)/[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_r50_db%2B%2B_icdar15_train.tar)| + +在TD_TR文本检测公开数据集上，算法复现效果如下： + +|模型|骨干网络|配置文件|precision|recall|Hmean|下载链接| +| --- | --- | --- | --- | --- | --- | --- | +|DB++|ResNet50|[configs/det/det_r50_db++_td_tr.yml](../../configs/det/det_r50_db++_td_tr.yml)|92.92%|86.48%|89.58%|[合成数据预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/ResNet50_dcn_asf_synthtext_pretrained.pdparams)/[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_r50_db%2B%2B_td_tr_train.tar)| @@ -54,7 +66,7 @@ python3 tools/export_model.py -c configs/det/det_r50_vd_db.yml -o Global.pretrai DB文本检测模型推理，可以执行如下命令： ```shell -python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_db/" +python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_db/" --det_algorithm="DB" ``` 可视化文本检测结果默认保存到`./inference_results`文件夹里面，结果文件的名称前缀为'det_res'。结果示例如下： @@ -96,4 +108,12 @@ DB模型还支持以下推理部署方式： pages={11474--11481}, year={2020} } -``` \ No newline at end of file + +@article{liao2022real, + title={Real-Time Scene Text Detection with Differentiable Binarization and Adaptive Scale Fusion}, + author={Liao, Minghui and Zou, Zhisheng and Wan, Zhaoyi and Yao, Cong and Bai, Xiang}, + journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, + year={2022}, + publisher={IEEE} +} +``` diff --git a/doc/doc_ch/algorithm_det_fcenet.md b/doc/doc_ch/algorithm_det_fcenet.md index bd2e734204d32bbf575ddea9f889953a72582c59..a70caa29fb590c7b7bf8d587a40676757a2ba4ce 100644 --- a/doc/doc_ch/algorithm_det_fcenet.md +++ b/doc/doc_ch/algorithm_det_fcenet.md @@ -1,17 +1,15 @@ # FCENet -- [1. 算法简介](#1) -- [2. 环境配置](#2) -- [3. 模型训练、评估、预测](#3) - - [3.1 训练](#3-1) - - [3.2 评估](#3-2) - - [3.3 预测](#3-3) -- [4. 推理部署](#4) - - [4.1 Python推理](#4-1) - - [4.2 C++推理](#4-2) - - [4.3 Serving服务化部署](#4-3) - - [4.4 更多推理部署](#4-4) -- [5. FAQ](#5) +- [1. 算法简介](#1-算法简介) +- [2. 环境配置](#2-环境配置) +- [3. 模型训练、评估、预测](#3-模型训练评估预测) +- [4. 推理部署](#4-推理部署) + - [4.1 Python推理](#41-python推理) + - [4.2 C++推理](#42-c推理) + - [4.3 Serving服务化部署](#43-serving服务化部署) + - [4.4 更多推理部署](#44-更多推理部署) +- [5. FAQ](#5-faq) +- [引用](#引用) ## 1. 算法简介 diff --git a/doc/doc_ch/algorithm_overview.md b/doc/doc_ch/algorithm_overview.md index eb81e4cd6dae2542dd07d0e25fe543419f798c9b..fbd3ce9ebccec0b1c2133b52e4aeb9d4d5e21114 100755 --- a/doc/doc_ch/algorithm_overview.md +++ b/doc/doc_ch/algorithm_overview.md @@ -1,9 +1,10 @@ # OCR算法 -- [1. 两阶段算法](#1-两阶段算法) - - [1.1 文本检测算法](#11-文本检测算法) - - [1.2 文本识别算法](#12-文本识别算法) -- [2. 端到端算法](#2-端到端算法) +- [1. 两阶段算法](#1) + - [1.1 文本检测算法](#11) + - [1.2 文本识别算法](#12) +- [2. 端到端算法](#2) +- [3. 表格识别算法](#3) 本文给出了PaddleOCR已支持的OCR算法列表，以及每个算法在**英文公开数据集**上的模型和指标，主要用于算法简介和算法性能对比，更多包括中文在内的其他数据集上的模型请参考[PP-OCR v2.0 系列模型下载](./models_list.md)。 @@ -68,6 +69,7 @@ - [x] [SVTR](./algorithm_rec_svtr.md) - [x] [ViTSTR](./algorithm_rec_vitstr.md) - [x] [ABINet](./algorithm_rec_abinet.md) +- [x] [SPIN](./algorithm_rec_spin.md) 参考[DTRB](https://arxiv.org/abs/1904.01906)[3]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： @@ -86,8 +88,10 @@ |SAR|Resnet31| 87.20% | rec_r31_sar | [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_r31_sar_train.tar) | |SEED|Aster_Resnet| 85.35% | rec_resnet_stn_bilstm_att | [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_resnet_stn_bilstm_att.tar) | |SVTR|SVTR-Tiny| 89.25% | rec_svtr_tiny_none_ctc_en | [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/rec_svtr_tiny_none_ctc_en_train.tar) | -|ViTSTR|ViTSTR| 79.82% | rec_vitstr_none_ce_en | [训练模型](https://paddleocr.bj.bcebos.com/rec_vitstr_none_ce_train.tar) | -|ABINet|Resnet45| 90.75% | rec_r45_abinet_en | [训练模型](https://paddleocr.bj.bcebos.com/rec_r45_abinet_train.tar) | +|ViTSTR|ViTSTR| 79.82% | rec_vitstr_none_ce | [训练模型](https://paddleocr.bj.bcebos.com/rec_vitstr_none_ce_train.tar) | +|ABINet|Resnet45| 90.75% | rec_r45_abinet | [训练模型](https://paddleocr.bj.bcebos.com/rec_r45_abinet_train.tar) | +|SPIN|ResNet32| 90.00% | rec_r32_gaspin_bilstm_att | coming soon | + @@ -95,3 +99,16 @@ 已支持的端到端OCR算法列表（戳链接获取使用教程）： - [x] [PGNet](./algorithm_e2e_pgnet.md) + + + +## 3. 表格识别算法 + +已支持的表格识别算法列表（戳链接获取使用教程）： +- [x] [TableMaster](./algorithm_table_master.md) + +在PubTabNet表格识别公开数据集上，算法效果如下： + +|模型|骨干网络|配置文件|acc|下载链接| +|---|---|---|---|---| +|TableMaster|TableResNetExtra|[configs/table/table_master.yml](../../configs/table/table_master.yml)|77.47%|[训练模型](https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_train.tar) / [推理模型](https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_infer.tar)| diff --git a/doc/doc_ch/algorithm_rec_spin.md b/doc/doc_ch/algorithm_rec_spin.md new file mode 100644 index 0000000000000000000000000000000000000000..c996992d2fa6297e6086ffae4bc36ad3e880873d --- /dev/null +++ b/doc/doc_ch/algorithm_rec_spin.md @@ -0,0 +1,112 @@ +# SPIN: Structure-Preserving Inner Offset Network for Scene Text Recognition + +- [1. 算法简介](#1) +- [2. 环境配置](#2) +- [3. 模型训练、评估、预测](#3) + - [3.1 训练](#3-1) + - [3.2 评估](#3-2) + - [3.3 预测](#3-3) +- [4. 推理部署](#4) + - [4.1 Python推理](#4-1) + - [4.2 C++推理](#4-2) + - [4.3 Serving服务化部署](#4-3) + - [4.4 更多推理部署](#4-4) +- [5. FAQ](#5) + + +## 1. 算法简介 + +论文信息： +> [SPIN: Structure-Preserving Inner Offset Network for Scene Text Recognition](https://arxiv.org/abs/2005.13117) +> Chengwei Zhang, Yunlu Xu, Zhanzhan Cheng, Shiliang Pu, Yi Niu, Fei Wu, Futai Zou +> AAAI, 2020 + +SPIN收录于AAAI2020。主要用于OCR识别任务。在任意形状文本识别中，矫正网络是一种较为常见的前置处理模块，但诸如RARE\ASTER\ESIR等只考虑了空间变换，并没有考虑色度变换。本文提出了一种结构Structure-Preserving Inner Offset Network (SPIN)，可以在色彩空间上进行变换。该模块是可微分的，可以加入到任意识别器中。 +使用MJSynth和SynthText两个合成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，算法复现效果如下： + +|模型|骨干网络|配置文件|Acc|下载链接| +| --- | --- | --- | --- | --- | +|SPIN|ResNet32|[rec_r32_gaspin_bilstm_att.yml](../../configs/rec/rec_r32_gaspin_bilstm_att.yml)|90.0%|coming soon| + + + +## 2. 环境配置 +请先参考[《运行环境准备》](./environment.md)配置PaddleOCR运行环境，参考[《项目克隆》](./clone.md)克隆项目代码。 + + + +## 3. 模型训练、评估、预测 + +请参考[文本识别教程](./recognition.md)。PaddleOCR对代码进行了模块化，训练不同的识别模型只需要**更换配置文件**即可。 + +训练 + +具体地，在完成数据准备后，便可以启动训练，训练命令如下： + +``` +#单卡训练（训练周期长，不建议） +python3 tools/train.py -c configs/rec/rec_r32_gaspin_bilstm_att.yml + +#多卡训练，通过--gpus参数指定卡号 +python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/rec/rec_r32_gaspin_bilstm_att.yml +``` + +评估 + +``` +# GPU 评估， Global.pretrained_model 为待测权重 +python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_r32_gaspin_bilstm_att.yml -o Global.pretrained_model={path/to/weights}/best_accuracy +``` + +预测： + +``` +# 预测使用的配置文件必须与训练一致 +python3 tools/infer_rec.py -c configs/rec/rec_r32_gaspin_bilstm_att.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/en/word_1.png +``` + + +## 4. 推理部署 + + +### 4.1 Python推理 +首先将SPIN文本识别训练过程中保存的模型，转换成inference model。可以使用如下命令进行转换： + +``` +python3 tools/export_model.py -c configs/rec/rec_r32_gaspin_bilstm_att.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.save_inference_dir=./inference/rec_r32_gaspin_bilstm_att +``` +SPIN文本识别模型推理，可以执行如下命令： + +``` +python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/en/word_1.png" --rec_model_dir="./inference/rec_r32_gaspin_bilstm_att/" --rec_image_shape="3, 32, 100" --rec_algorithm="SPIN" --rec_char_dict_path="/ppocr/utils/dict/spin_dict.txt" --use_space_char=Falsee +``` + + +### 4.2 C++推理 + +由于C++预处理后处理还未支持SPIN，所以暂未支持 + + +### 4.3 Serving服务化部署 + +暂不支持 + + +### 4.4 更多推理部署 + +暂不支持 + + +## 5. FAQ + + +## 引用 + +```bibtex +@article{2020SPIN, + title={SPIN: Structure-Preserving Inner Offset Network for Scene Text Recognition}, + author={Chengwei Zhang and Yunlu Xu and Zhanzhan Cheng and Shiliang Pu and Yi Niu and Fei Wu and Futai Zou}, + journal={AAAI2020}, + year={2020}, +} +``` diff --git a/doc/doc_ch/algorithm_table_master.md b/doc/doc_ch/algorithm_table_master.md new file mode 100644 index 0000000000000000000000000000000000000000..36455ed9f94581c31fb849ebe121a3d7ecdb7acb --- /dev/null +++ b/doc/doc_ch/algorithm_table_master.md @@ -0,0 +1,114 @@ +# 表格识别算法-TableMASTER + +- [1. 算法简介](#1-算法简介) +- [2. 环境配置](#2-环境配置) +- [3. 模型训练、评估、预测](#3-模型训练评估预测) +- [4. 推理部署](#4-推理部署) + - [4.1 Python推理](#41-python推理) + - [4.2 C++推理部署](#42-c推理部署) + - [4.3 Serving服务化部署](#43-serving服务化部署) + - [4.4 更多推理部署](#44-更多推理部署) +- [5. FAQ](#5-faq) +- [引用](#引用) + + +## 1. 算法简介 + +论文信息： +> [TableMaster: PINGAN-VCGROUP’S SOLUTION FOR ICDAR 2021 COMPETITION ON SCIENTIFIC LITERATURE PARSING TASK B: TABLE RECOGNITION TO HTML](https://arxiv.org/pdf/2105.01848.pdf) +> Ye, Jiaquan and Qi, Xianbiao and He, Yelin and Chen, Yihao and Gu, Dengyi and Gao, Peng and Xiao, Rong +> 2021 + +在PubTabNet表格识别公开数据集上，算法复现效果如下： + +|模型|骨干网络|配置文件|acc|下载链接| +| --- | --- | --- | --- | --- | +|TableMaster|TableResNetExtra|[configs/table/table_master.yml](../../configs/table/table_master.yml)|77.47%|[训练模型](https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_train.tar)/[推理模型](https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_infer.tar)| + + + +## 2. 环境配置 +请先参考[《运行环境准备》](./environment.md)配置PaddleOCR运行环境，参考[《项目克隆》](./clone.md)克隆项目代码。 + + + +## 3. 模型训练、评估、预测 + +上述TableMaster模型使用PubTabNet表格识别公开数据集训练得到，数据集下载可参考 [table_datasets](./dataset/table_datasets.md)。 + +数据下载完成后，请参考[文本识别教程](./recognition.md)进行训练。PaddleOCR对代码进行了模块化，训练不同的模型只需要**更换配置文件**即可。 + + +## 4. 推理部署 + + +### 4.1 Python推理 +首先将训练得到best模型，转换成inference model。以基于TableResNetExtra骨干网络，在PubTabNet数据集训练的模型为例([模型下载地址](https://paddleocr.bj.bcebos.com/contribution/table_master.tar))，可以使用如下命令进行转换： + +```shell +# 注意将pretrained_model的路径设置为本地路径。 +python3 tools/export_model.py -c configs/table/table_master.yml -o Global.pretrained_model=output/table_master/best_accuracy Global.save_inference_dir=./inference/table_master +``` + +**注意：** +- 如果您是在自己的数据集上训练的模型，并且调整了字典文件，请注意修改配置文件中的`character_dict_path`是否为所正确的字典文件。 + +转换成功后，在目录下有三个文件： +``` +./inference/table_master/ + ├── inference.pdiparams # 识别inference模型的参数文件 + ├── inference.pdiparams.info # 识别inference模型的参数信息，可忽略 + └── inference.pdmodel # 识别inference模型的program文件 +``` + + +执行如下命令进行模型推理： + +```shell +cd ppstructure/ +python3.7 table/predict_structure.py --table_model_dir=../output/table_master/table_structure_tablemaster_infer/ --table_algorithm=TableMaster --table_char_dict_path=../ppocr/utils/dict/table_master_structure_dict.txt --table_max_len=480 --image_dir=docs/table/table.jpg +# 预测文件夹下所有图像时，可修改image_dir为文件夹，如 --image_dir='docs/table'。 +``` + +执行命令后，上面图像的预测结果（结构信息和表格中每个单元格的坐标）会打印到屏幕上，同时会保存单元格坐标的可视化结果。示例如下： +结果如下： +```shell +[2022/06/16 13:06:54] ppocr INFO: result: ['', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '
', '', ''], [[72.17591094970703, 10.759100914001465, 60.29658508300781, 16.6805362701416], [161.85562133789062, 10.884308815002441, 14.9495210647583, 16.727018356323242], [277.79876708984375, 29.54340362548828, 31.490320205688477, 18.143272399902344], +... +[336.11724853515625, 280.3601989746094, 39.456939697265625, 18.121286392211914]] +[2022/06/16 13:06:54] ppocr INFO: save vis result to ./output/table.jpg +[2022/06/16 13:06:54] ppocr INFO: Predict time of docs/table/table.jpg: 17.36806297302246 +``` + +**注意**： + +- TableMaster在推理时比较慢，建议使用GPU进行使用。 + + +### 4.2 C++推理部署 + +由于C++预处理后处理还未支持TableMaster，所以暂未支持 + + +### 4.3 Serving服务化部署 + +暂不支持 + + +### 4.4 更多推理部署 + +暂不支持 + + +## 5. FAQ + +## 引用 + +```bibtex +@article{ye2021pingan, + title={PingAn-VCGroup's Solution for ICDAR 2021 Competition on Scientific Literature Parsing Task B: Table Recognition to HTML}, + author={Ye, Jiaquan and Qi, Xianbiao and He, Yelin and Chen, Yihao and Gu, Dengyi and Gao, Peng and Xiao, Rong}, + journal={arXiv preprint arXiv:2105.01848}, + year={2021} +} +``` diff --git a/doc/doc_ch/application.md b/doc/doc_ch/application.md new file mode 100644 index 0000000000000000000000000000000000000000..5135dfac10b0b49d1c91eb07202bd3929c7bbb7c --- /dev/null +++ b/doc/doc_ch/application.md @@ -0,0 +1,41 @@ +# 场景应用 + +PaddleOCR场景应用覆盖通用，制造、金融、交通行业的主要OCR垂类应用，在PP-OCR、PP-Structure的通用能力基础之上，以notebook的形式展示利用场景数据微调、模型优化方法、数据增广等内容，为开发者快速落地OCR应用提供示范与启发。 + +> 如需下载全部垂类模型，可以扫描下方二维码，关注公众号填写问卷后，加入PaddleOCR官方交流群获取20G OCR学习大礼包（内含《动手学OCR》电子书、课程回放视频、前沿论文等重磅资料） + +
+ +
+ + +> 如果您是企业开发者且未在下述场景中找到合适的方案，可以填写[OCR应用合作调研问卷](https://paddle.wjx.cn/vj/QwF7GKw.aspx)，免费与官方团队展开不同层次的合作，包括但不限于问题抽象、确定技术方案、项目答疑、共同研发等。如果您已经使用PaddleOCR落地项目，也可以填写此问卷，与飞桨平台共同宣传推广，提升企业技术品宣。期待您的提交！ + +## 通用 + +| 类别 | 亮点 | 类别 | 亮点 | +| ---------------------- | -------- | ---------- | ------------ | +| 高精度中文识别模型SVTR | 新增模型 | 手写体识别 | 新增字形支持 | + +## 制造 + +| 类别 | 亮点 | 类别 | 亮点 | +| -------------- | ------------------------------ | -------------- | -------------------- | +| 数码管识别 | 数码管数据合成、漏识别调优 | 电表识别 | 大分辨率图像检测调优 | +| 液晶屏读数识别 | 检测模型蒸馏、Serving部署 | PCB文字识别 | 小尺寸文本检测与识别 | +| 包装生产日期 | 点阵字符合成、过曝过暗文字识别 | 液晶屏缺陷检测 | 非文字形态识别 | + +## 金融 + +| 类别 | 亮点 | 类别 | 亮点 | +| -------------- | ------------------------ | ------------ | --------------------- | +| 表单VQA | 多模态通用表单结构化提取 | 通用卡证识别 | 通用结构化提取 | +| 增值税发票 | 尽请期待 | 身份证识别 | 结构化提取、图像阴影 | +| 印章检测与识别 | 端到端弯曲文本识别 | 合同比对 | 密集文本检测、NLP串联 | + +## 交通 + +| 类别 | 亮点 | 类别 | 亮点 | +| ----------------- | ------------------------------ | ---------- | -------- | +| 车牌识别 | 多角度图像、轻量模型、端侧部署 | 快递单识别 | 尽请期待 | +| 驾驶证/行驶证识别 | 尽请期待 | | | \ No newline at end of file diff --git a/doc/doc_ch/dataset/ocr_datasets.md b/doc/doc_ch/dataset/ocr_datasets.md index c6ff2e170f7c30a29e98ed2b1349cae2b84cf441..b7666fd63e6f17b734a17e2d11a0c8614d225964 100644 --- a/doc/doc_ch/dataset/ocr_datasets.md +++ b/doc/doc_ch/dataset/ocr_datasets.md @@ -34,6 +34,7 @@ json.dumps编码前的图像标注信息是包含多个字典的list，字典中 | ICDAR 2015 |https://rrc.cvc.uab.es/?ch=4&com=downloads| [train](https://paddleocr.bj.bcebos.com/dataset/train_icdar2015_label.txt) / [test](https://paddleocr.bj.bcebos.com/dataset/test_icdar2015_label.txt) | | ctw1500 |https://paddleocr.bj.bcebos.com/dataset/ctw1500.zip| 图片下载地址中已包含 | | total text |https://paddleocr.bj.bcebos.com/dataset/total_text.tar| 图片下载地址中已包含 | +| td tr |https://paddleocr.bj.bcebos.com/dataset/TD_TR.tar| 图片下载地址中已包含 | #### 1.2.1 ICDAR 2015 ICDAR 2015 数据集包含1000张训练图像和500张测试图像。ICDAR 2015 数据集可以从上表中链接下载，首次下载需注册。 diff --git a/doc/doc_ch/detection.md b/doc/doc_ch/detection.md index 2cf0732219ac9cd2309ae24896d7de1499986461..eba5213501ad82b198438af1ae69dd8c7ad1071e 100644 --- a/doc/doc_ch/detection.md +++ b/doc/doc_ch/detection.md @@ -65,7 +65,7 @@ python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/ ``` -上述指令中，通过-c 选择训练使用configs/det/det_db_mv3.yml配置文件。 +上述指令中，通过-c 选择训练使用configs/det/det_mv3_db.yml配置文件。 有关配置文件的详细解释，请参考[链接](./config.md)。 您也可以通过-o参数在不需要修改yml文件的情况下，改变训练的参数，比如，调整训练的学习率为0.0001 diff --git a/doc/doc_ch/inference_ppocr.md b/doc/doc_ch/inference_ppocr.md index 472c0003f963d6f65fa2d0babbf6b9c7d0ec9b80..622ac995d37ce290ee51af06164b0c2aef8b5a14 100644 --- a/doc/doc_ch/inference_ppocr.md +++ b/doc/doc_ch/inference_ppocr.md @@ -7,7 +7,8 @@ - [1. 文本检测模型推理](#1-文本检测模型推理) - [2. 文本识别模型推理](#2-文本识别模型推理) - [2.1 超轻量中文识别模型推理](#21-超轻量中文识别模型推理) - - [2.2 多语言模型的推理](#22-多语言模型的推理) + - [2.2 英文识别模型推理](#22-英文识别模型推理) + - [2.3 多语言模型的推理](#23-多语言模型的推理) - [3. 方向分类模型推理](#3-方向分类模型推理) - [4. 文本检测、方向分类和文字识别串联推理](#4-文本检测方向分类和文字识别串联推理) @@ -78,9 +79,29 @@ python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/ch/word_4.jpg" Predicts of ./doc/imgs_words/ch/word_4.jpg:('实力活力', 0.9956803321838379) ``` + + +### 2.2 英文识别模型推理 + +英文识别模型推理，可以执行如下命令， 注意修改字典路径： + +``` +# 下载英文数字识别模型： +wget https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_infer.tar +tar xf en_PP-OCRv3_det_infer.tar +python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/en/word_1.png" --rec_model_dir="./en_PP-OCRv3_det_infer/" --rec_char_dict_path="ppocr/utils/en_dict.txt" +``` + +![](../imgs_words/en/word_1.png) + +执行命令后，上图的预测结果为： + +``` +Predicts of ./doc/imgs_words/en/word_1.png: ('JOINT', 0.998160719871521) +``` -### 2.2 多语言模型的推理 +### 2.3 多语言模型的推理 如果您需要预测的是其他语言模型，可以在[此链接](./models_list.md#%E5%A4%9A%E8%AF%AD%E8%A8%80%E8%AF%86%E5%88%AB%E6%A8%A1%E5%9E%8B)中找到对应语言的inference模型，在使用inference模型预测时，需要通过`--rec_char_dict_path`指定使用的字典路径, 同时为了得到正确的可视化结果，需要通过 `--vis_font_path` 指定可视化的字体路径，`doc/fonts/` 路径下有默认提供的小语种字体，例如韩文识别： ``` diff --git a/doc/doc_en/algorithm_en.md b/doc/doc_en/algorithm_en.md index fa7887eb2681271f2b02296516221b00f9cf4626..c880336b4ad528eab2cce479edf11fce0b43f435 100644 --- a/doc/doc_en/algorithm_en.md +++ b/doc/doc_en/algorithm_en.md @@ -6,5 +6,6 @@ PaddleOCR will add cutting-edge OCR algorithms and models continuously. Check ou - [text detection algorithms](./algorithm_overview_en.md#11) - [text recognition algorithms](./algorithm_overview_en.md#12) - [end-to-end algorithms](./algorithm_overview_en.md#2) +- [table recognition algorithms](./algorithm_overview_en.md#3) -Developers are welcome to contribute more algorithms! Please refer to [add new algorithm](./add_new_algorithm_en.md) guideline. \ No newline at end of file +Developers are welcome to contribute more algorithms! Please refer to [add new algorithm](./add_new_algorithm_en.md) guideline. diff --git a/doc/doc_en/algorithm_overview_en.md b/doc/doc_en/algorithm_overview_en.md index 28aca7c0d171008156104fbcc786707538fd49ef..a579d2447c52067e05d16af5e9d6cf50defc2b1c 100755 --- a/doc/doc_en/algorithm_overview_en.md +++ b/doc/doc_en/algorithm_overview_en.md @@ -1,9 +1,10 @@ # OCR Algorithms - [1. Two-stage Algorithms](#1) - * [1.1 Text Detection Algorithms](#11) - * [1.2 Text Recognition Algorithms](#12) + - [1.1 Text Detection Algorithms](#11) + - [1.2 Text Recognition Algorithms](#12) - [2. End-to-end Algorithms](#2) +- [3. Table Recognition Algorithms](#3) This tutorial lists the OCR algorithms supported by PaddleOCR, as well as the models and metrics of each algorithm on **English public datasets**. It is mainly used for algorithm introduction and algorithm performance comparison. For more models on other datasets including Chinese, please refer to [PP-OCR v2.0 models list](./models_list_en.md). @@ -67,6 +68,7 @@ Supported text recognition algorithms (Click the link to get the tutorial): - [x] [SVTR](./algorithm_rec_svtr_en.md) - [x] [ViTSTR](./algorithm_rec_vitstr_en.md) - [x] [ABINet](./algorithm_rec_abinet_en.md) +- [x] [SPIN](./algorithm_rec_spin_en.md) 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: @@ -85,8 +87,10 @@ Refer to [DTRB](https://arxiv.org/abs/1904.01906), the training and evaluation r |SAR|Resnet31| 87.20% | rec_r31_sar | [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_r31_sar_train.tar) | |SEED|Aster_Resnet| 85.35% | rec_resnet_stn_bilstm_att | [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_resnet_stn_bilstm_att.tar) | |SVTR|SVTR-Tiny| 89.25% | rec_svtr_tiny_none_ctc_en | [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/rec_svtr_tiny_none_ctc_en_train.tar) | -|ViTSTR|ViTSTR| 79.82% | rec_vitstr_none_ce_en | [trained model](https://paddleocr.bj.bcebos.com/rec_vitstr_none_none_train.tar) | -|ABINet|Resnet45| 90.75% | rec_r45_abinet_en | [trained model](https://paddleocr.bj.bcebos.com/rec_r45_abinet_train.tar) | +|ViTSTR|ViTSTR| 79.82% | rec_vitstr_none_ce | [trained model](https://paddleocr.bj.bcebos.com/rec_vitstr_none_none_train.tar) | +|ABINet|Resnet45| 90.75% | rec_r45_abinet | [trained model](https://paddleocr.bj.bcebos.com/rec_r45_abinet_train.tar) | +|SPIN|ResNet32| 90.00% | rec_r32_gaspin_bilstm_att | coming soon | + @@ -94,3 +98,15 @@ Refer to [DTRB](https://arxiv.org/abs/1904.01906), the training and evaluation r Supported end-to-end algorithms (Click the link to get the tutorial): - [x] [PGNet](./algorithm_e2e_pgnet_en.md) + + +## 3. Table Recognition Algorithms + +Supported table recognition algorithms (Click the link to get the tutorial): +- [x] [TableMaster](./algorithm_table_master_en.md) + +On the PubTabNet dataset, the algorithm result is as follows: + +|Model|Backbone|Config|Acc|Download link| +|---|---|---|---|---| +|TableMaster|TableResNetExtra|[configs/table/table_master.yml](../../configs/table/table_master.yml)|77.47%|[trained](https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_train.tar) / [inference model](https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_infer.tar)| diff --git a/doc/doc_en/algorithm_rec_spin_en.md b/doc/doc_en/algorithm_rec_spin_en.md new file mode 100644 index 0000000000000000000000000000000000000000..43ab30ce7d96cbb64ddf87156fee3012d666b2bf --- /dev/null +++ b/doc/doc_en/algorithm_rec_spin_en.md @@ -0,0 +1,112 @@ +# SPIN: Structure-Preserving Inner Offset Network for Scene Text Recognition + +- [1. Introduction](#1) +- [2. Environment](#2) +- [3. Model Training / Evaluation / Prediction](#3) + - [3.1 Training](#3-1) + - [3.2 Evaluation](#3-2) + - [3.3 Prediction](#3-3) +- [4. Inference and Deployment](#4) + - [4.1 Python Inference](#4-1) + - [4.2 C++ Inference](#4-2) + - [4.3 Serving](#4-3) + - [4.4 More](#4-4) +- [5. FAQ](#5) + + +## 1. Introduction + +Paper: +> [SPIN: Structure-Preserving Inner Offset Network for Scene Text Recognition](https://arxiv.org/abs/2005.13117) +> Chengwei Zhang, Yunlu Xu, Zhanzhan Cheng, Shiliang Pu, Yi Niu, Fei Wu, Futai Zou +> AAAI, 2020 + +Using MJSynth and SynthText two text recognition datasets for training, and evaluating on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE datasets. The algorithm reproduction effect is as follows: + +|Model|Backbone|config|Acc|Download link| +| --- | --- | --- | --- | --- | +|SPIN|ResNet32|[rec_r32_gaspin_bilstm_att.yml](../../configs/rec/rec_r32_gaspin_bilstm_att.yml)|90.0%|coming soon| + + + +## 2. Environment +Please refer to ["Environment Preparation"](./environment_en.md) to configure the PaddleOCR environment, and refer to ["Project Clone"](./clone_en.md) to clone the project code. + + + +## 3. Model Training / Evaluation / Prediction + +Please refer to [Text Recognition Tutorial](./recognition_en.md). PaddleOCR modularizes the code, and training different recognition models only requires **changing the configuration file**. + +Training: + +Specifically, after the data preparation is completed, the training can be started. The training command is as follows: + +``` +#Single GPU training (long training period, not recommended) +python3 tools/train.py -c configs/rec/rec_r32_gaspin_bilstm_att.yml + +#Multi GPU training, specify the gpu number through the --gpus parameter +python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/rec/rec_r32_gaspin_bilstm_att.yml +``` + +Evaluation: + +``` +# GPU evaluation +python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_r32_gaspin_bilstm_att.yml -o Global.pretrained_model={path/to/weights}/best_accuracy +``` + +Prediction: + +``` +# The configuration file used for prediction must match the training +python3 tools/infer_rec.py -c configs/rec/rec_r32_gaspin_bilstm_att.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/en/word_1.png +``` + + +## 4. Inference and Deployment + + +### 4.1 Python Inference +First, the model saved during the SPIN text recognition training process is converted into an inference model. you can use the following command to convert: + +``` +python3 tools/export_model.py -c configs/rec/rec_r32_gaspin_bilstm_att.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.save_inference_dir=./inference/rec_r32_gaspin_bilstm_att +``` + +For SPIN text recognition model inference, the following commands can be executed: + +``` +python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/en/word_1.png" --rec_model_dir="./inference/rec_r32_gaspin_bilstm_att/" --rec_image_shape="3, 32, 100" --rec_algorithm="SPIN" --rec_char_dict_path="/ppocr/utils/dict/spin_dict.txt" --use_space_char=False +``` + + +### 4.2 C++ Inference + +Not supported + + +### 4.3 Serving + +Not supported + + +### 4.4 More + +Not supported + + +## 5. FAQ + + +## Citation + +```bibtex +@article{2020SPIN, + title={SPIN: Structure-Preserving Inner Offset Network for Scene Text Recognition}, + author={Chengwei Zhang and Yunlu Xu and Zhanzhan Cheng and Shiliang Pu and Yi Niu and Fei Wu and Futai Zou}, + journal={AAAI2020}, + year={2020}, +} +``` diff --git a/doc/doc_en/algorithm_table_master_en.md b/doc/doc_en/algorithm_table_master_en.md new file mode 100644 index 0000000000000000000000000000000000000000..e9249a2a05d3e79f4358366d46cf02a14a223f5f --- /dev/null +++ b/doc/doc_en/algorithm_table_master_en.md @@ -0,0 +1,112 @@ +# Table Recognition Algorithm-TableMASTER + +- [1. Introduction](#1-introduction) +- [2. Environment](#2-environment) +- [3. Model Training / Evaluation / Prediction](#3-model-training--evaluation--prediction) +- [4. Inference and Deployment](#4-inference-and-deployment) + - [4.1 Python Inference](#41-python-inference) + - [4.2 C++ Inference](#42-c-inference) + - [4.3 Serving](#43-serving) + - [4.4 More](#44-more) +- [5. FAQ](#5-faq) +- [Citation](#citation) + + +## 1. Introduction + +Paper: +> [TableMaster: PINGAN-VCGROUP’S SOLUTION FOR ICDAR 2021 COMPETITION ON SCIENTIFIC LITERATURE PARSING TASK B: TABLE RECOGNITION TO HTML](https://arxiv.org/pdf/2105.01848.pdf) +> Ye, Jiaquan and Qi, Xianbiao and He, Yelin and Chen, Yihao and Gu, Dengyi and Gao, Peng and Xiao, Rong +> 2021 + + +On the PubTabNet table recognition public data set, the algorithm reproduction acc is as follows: + +|Model|Backbone|Cnnfig|Acc|Download link| +| --- | --- | --- | --- | --- | +|TableMaster|TableResNetExtra|[configs/table/table_master.yml](../../configs/table/table_master.yml)|77.47%|[trained model](https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_train.tar)/[inference model](https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_infer.tar)| + + + +## 2. Environment +Please refer to ["Environment Preparation"](./environment_en.md) to configure the PaddleOCR environment, and refer to ["Project Clone"](./clone_en.md) to clone the project code. + + + +## 3. Model Training / Evaluation / Prediction + +The above TableMaster model is trained using the PubTabNet table recognition public dataset. For the download of the dataset, please refer to [table_datasets](./dataset/table_datasets_en.md). + +After the data download is complete, please refer to [Text Recognition Training Tutorial](./recognition_en.md) for training. PaddleOCR has modularized the code structure, so that you only need to **replace the configuration file** to train different models. + + + +## 4. Inference and Deployment + + +### 4.1 Python Inference + +First, convert the model saved in the TableMaster table recognition training process into an inference model. Taking the model based on the TableResNetExtra backbone network and trained on the PubTabNet dataset as example ([model download link](https://paddleocr.bj.bcebos.com/contribution/table_master.tar)), you can use the following command to convert: + + +```shell +python3 tools/export_model.py -c configs/table/table_master.yml -o Global.pretrained_model=output/table_master/best_accuracy Global.save_inference_dir=./inference/table_master +``` + +**Note: ** +- If you trained the model on your own dataset and adjusted the dictionary file, please pay attention to whether the `character_dict_path` in the modified configuration file is the correct dictionary file + + +Execute the following command for model inference: + +```shell +cd ppstructure/ +# When predicting all images in a folder, you can modify image_dir to a folder, such as --image_dir='docs/table'. +python3.7 table/predict_structure.py --table_model_dir=../output/table_master/table_structure_tablemaster_infer/ --table_algorithm=TableMaster --table_char_dict_path=../ppocr/utils/dict/table_master_structure_dict.txt --table_max_len=480 --image_dir=docs/table/table.jpg + +``` + +After executing the command, the prediction results of the above image (structural information and the coordinates of each cell in the table) are printed to the screen, and the visualization of the cell coordinates is also saved. An example is as follows: + +result： +```shell +[2022/06/16 13:06:54] ppocr INFO: result: ['', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '
', '', ''], [[72.17591094970703, 10.759100914001465, 60.29658508300781, 16.6805362701416], [161.85562133789062, 10.884308815002441, 14.9495210647583, 16.727018356323242], [277.79876708984375, 29.54340362548828, 31.490320205688477, 18.143272399902344], +... +[336.11724853515625, 280.3601989746094, 39.456939697265625, 18.121286392211914]] +[2022/06/16 13:06:54] ppocr INFO: save vis result to ./output/table.jpg +[2022/06/16 13:06:54] ppocr INFO: Predict time of docs/table/table.jpg: 17.36806297302246 +``` + +**Note**: + +- TableMaster is relatively slow during inference, and it is recommended to use GPU for use. + + +### 4.2 C++ Inference + +Since the post-processing is not written in CPP, the TableMaster does not support CPP inference. + + + +### 4.3 Serving + +Not supported + + +### 4.4 More + +Not supported + + +## 5. FAQ + +## Citation + +```bibtex +@article{ye2021pingan, + title={PingAn-VCGroup's Solution for ICDAR 2021 Competition on Scientific Literature Parsing Task B: Table Recognition to HTML}, + author={Ye, Jiaquan and Qi, Xianbiao and He, Yelin and Chen, Yihao and Gu, Dengyi and Gao, Peng and Xiao, Rong}, + journal={arXiv preprint arXiv:2105.01848}, + year={2021} +} +``` diff --git a/doc/doc_en/detection_en.md b/doc/doc_en/detection_en.md index f85bf585cb66332d90de8d66ed315cb04ece7636..c215e1a46636a84d372245097b460c095e9cb7fd 100644 --- a/doc/doc_en/detection_en.md +++ b/doc/doc_en/detection_en.md @@ -51,7 +51,7 @@ python3 tools/train.py -c configs/det/det_mv3_db.yml \ -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained ``` -In the above instruction, use `-c` to select the training to use the `configs/det/det_db_mv3.yml` configuration file. +In the above instruction, use `-c` to select the training to use the `configs/det/det_mv3_db.yml` configuration file. For a detailed explanation of the configuration file, please refer to [config](./config_en.md). You can also use `-o` to change the training parameters without modifying the yml file. For example, adjust the training learning rate to 0.0001 diff --git a/doc/doc_en/inference_ppocr_en.md b/doc/doc_en/inference_ppocr_en.md index 935f92f5144f582630a45edcc886b609ecdc82da..0f57b0ba6b226c19ecb1e0b60afdfa34302b8e78 100755 --- a/doc/doc_en/inference_ppocr_en.md +++ b/doc/doc_en/inference_ppocr_en.md @@ -8,7 +8,8 @@ This article introduces the use of the Python inference engine for the PP-OCR mo - [Text Detection Model Inference](#text-detection-model-inference) - [Text Recognition Model Inference](#text-recognition-model-inference) - [1. Lightweight Chinese Recognition Model Inference](#1-lightweight-chinese-recognition-model-inference) - - [2. Multilingual Model Inference](#2-multilingual-model-inference) + - [2. English Recognition Model Inference](#2-english-recognition-model-inference) + - [3. Multilingual Model Inference](#3-multilingual-model-inference) - [Angle Classification Model Inference](#angle-classification-model-inference) - [Text Detection Angle Classification and Recognition Inference Concatenation](#text-detection-angle-classification-and-recognition-inference-concatenation) @@ -76,10 +77,31 @@ After executing the command, the prediction results (recognized text and score) ```bash Predicts of ./doc/imgs_words_en/word_10.png:('PAIN', 0.988671) ``` + +### 2. English Recognition Model Inference - +For English recognition model inference, you can execute the following commands,you need to specify the dictionary path used by `--rec_char_dict_path`: -### 2. Multilingual Model Inference +``` +# download en model： +wget https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_infer.tar +tar xf en_PP-OCRv3_det_infer.tar +python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/en/word_1.png" --rec_model_dir="./en_PP-OCRv3_det_infer/" --rec_char_dict_path="ppocr/utils/en_dict.txt" +``` + +![](../imgs_words/en/word_1.png) + + +After executing the command, the prediction result of the above figure is: + +``` +Predicts of ./doc/imgs_words/en/word_1.png: ('JOINT', 0.998160719871521) +``` + + + + +### 3. Multilingual Model Inference If you need to predict [other language models](./models_list_en.md#Multilingual), when using inference model prediction, you need to specify the dictionary path used by `--rec_char_dict_path`. At the same time, in order to get the correct visualization results, You need to specify the visual font path through `--vis_font_path`. There are small language fonts provided by default under the `doc/fonts` path, such as Korean recognition: diff --git a/ppocr/data/imaug/__init__.py b/ppocr/data/imaug/__init__.py index 63dfda91f8d0eb200d3c635fda43670039375784..d41eed9dfbd2980242e76fa8d8aae380a6594cd4 100644 --- a/ppocr/data/imaug/__init__.py +++ b/ppocr/data/imaug/__init__.py @@ -23,9 +23,10 @@ from .random_crop_data import EastRandomCropData, RandomCropImgMask from .make_pse_gt import MakePseGt -from .rec_img_aug import RecAug, RecConAug, RecResizeImg, ClsResizeImg, \ - SRNRecResizeImg, GrayRecResizeImg, SARRecResizeImg, PRENResizeImg, \ - ABINetRecResizeImg, SVTRRecResizeImg, ABINetRecAug + +from .rec_img_aug import BaseDataAugmentation, RecAug, RecConAug, RecResizeImg, ClsResizeImg, \ + SRNRecResizeImg, GrayRecResizeImg, SARRecResizeImg, PRENResizeImg, \ + ABINetRecResizeImg, SVTRRecResizeImg, ABINetRecAug, SPINRecResizeImg from .ssl_img_aug import SSLRotateResize from .randaugment import RandAugment from .copy_paste import CopyPaste @@ -36,7 +37,7 @@ from .label_ops import * from .east_process import * from .sast_process import * from .pg_process import * -from .gen_table_mask import * +from .table_ops import * from .vqa import * diff --git a/ppocr/data/imaug/label_ops.py b/ppocr/data/imaug/label_ops.py index 312d6dc9ad25bfa73aa9009f932fe6f3d3ca7644..d84f966201ca4c38f5b71c4df7e95485cf953fc1 100644 --- a/ppocr/data/imaug/label_ops.py +++ b/ppocr/data/imaug/label_ops.py @@ -259,15 +259,26 @@ class E2ELabelEncodeTrain(object): class KieLabelEncode(object): - def __init__(self, character_dict_path, norm=10, directed=False, **kwargs): + def __init__(self, + character_dict_path, + class_path, + norm=10, + directed=False, + **kwargs): super(KieLabelEncode, self).__init__() self.dict = dict({'': 0}) + self.label2classid_map = dict() with open(character_dict_path, 'r', encoding='utf-8') as fr: idx = 1 for line in fr: char = line.strip() self.dict[char] = idx idx += 1 + with open(class_path, "r") as fin: + lines = fin.readlines() + for idx, line in enumerate(lines): + line = line.strip("\n") + self.label2classid_map[line] = idx self.norm = norm self.directed = directed @@ -408,7 +419,7 @@ class KieLabelEncode(object): text_ind = [self.dict[c] for c in text if c in self.dict] text_inds.append(text_ind) if 'label' in ann.keys(): - labels.append(ann['label']) + labels.append(self.label2classid_map[ann['label']]) elif 'key_cls' in ann.keys(): labels.append(ann['key_cls']) else: @@ -551,171 +562,210 @@ class SRNLabelEncode(BaseRecLabelEncode): return idx -class TableLabelEncode(object): +class TableLabelEncode(AttnLabelEncode): """ Convert between text-label and text-index """ def __init__(self, max_text_length, - max_elem_length, - max_cell_num, character_dict_path, - span_weight=1.0, + replace_empty_cell_token=False, + merge_no_span_structure=False, + learn_empty_box=False, + point_num=2, **kwargs): - self.max_text_length = max_text_length - self.max_elem_length = max_elem_length - self.max_cell_num = max_cell_num - list_character, list_elem = self.load_char_elem_dict( - character_dict_path) - list_character = self.add_special_char(list_character) - list_elem = self.add_special_char(list_elem) - self.dict_character = {} - for i, char in enumerate(list_character): - self.dict_character[char] = i - self.dict_elem = {} - for i, elem in enumerate(list_elem): - self.dict_elem[elem] = i - self.span_weight = span_weight - - def load_char_elem_dict(self, character_dict_path): - list_character = [] - list_elem = [] + self.max_text_len = max_text_length + self.lower = False + self.learn_empty_box = learn_empty_box + self.merge_no_span_structure = merge_no_span_structure + self.replace_empty_cell_token = replace_empty_cell_token + + dict_character = [] with open(character_dict_path, "rb") as fin: lines = fin.readlines() - substr = lines[0].decode('utf-8').strip("\r\n").split("\t") - character_num = int(substr[0]) - elem_num = int(substr[1]) - for cno in range(1, 1 + character_num): - character = lines[cno].decode('utf-8').strip("\r\n") - list_character.append(character) - for eno in range(1 + character_num, 1 + character_num + elem_num): - elem = lines[eno].decode('utf-8').strip("\r\n") - list_elem.append(elem) - return list_character, list_elem - - def add_special_char(self, list_character): - self.beg_str = "sos" - self.end_str = "eos" - list_character = [self.beg_str] + list_character + [self.end_str] - return list_character + for line in lines: + line = line.decode('utf-8').strip("\n").strip("\r\n") + dict_character.append(line) + + dict_character = self.add_special_char(dict_character) + self.dict = {} + for i, char in enumerate(dict_character): + self.dict[char] = i + self.idx2char = {v: k for k, v in self.dict.items()} + + self.character = dict_character + self.point_num = point_num + self.pad_idx = self.dict[self.beg_str] + self.start_idx = self.dict[self.beg_str] + self.end_idx = self.dict[self.end_str] + + self.td_token = ['', '', ''] + self.empty_bbox_token_dict = { + "[]": '', + "[' ']": '', + "['', ' ', '']": '', + "['\\u2028', '\\u2028']": '', + "['', ' ', '']": '', + "['', '']": '', + "['', ' ', '']": '', + "['', '', '', '']": '', + "['', '', ' ', '', '']": '', + "['', '']": '', + "['', ' ', '\\u2028', ' ', '\\u2028', ' ', '']": + '', + } - def get_span_idx_list(self): - span_idx_list = [] - for elem in self.dict_elem: - if 'span' in elem: - span_idx_list.append(self.dict_elem[elem]) - return span_idx_list + @property + def _max_text_len(self): + return self.max_text_len + 2 def __call__(self, data): cells = data['cells'] - structure = data['structure']['tokens'] - structure = self.encode(structure, 'elem') + structure = data['structure'] + if self.merge_no_span_structure: + structure = self._merge_no_span_structure(structure) + if self.replace_empty_cell_token: + structure = self._replace_empty_cell_token(structure, cells) + # remove empty token and add " " to span token + new_structure = [] + for token in structure: + if token != '': + if 'span' in token and token[0] != ' ': + token = ' ' + token + new_structure.append(token) + # encode structure + structure = self.encode(new_structure) if structure is None: return None - elem_num = len(structure) - structure = [0] + structure + [len(self.dict_elem) - 1] - structure = structure + [0] * (self.max_elem_length + 2 - len(structure) - ) + + structure = [self.start_idx] + structure + [self.end_idx + ] # add sos abd eos + structure = structure + [self.pad_idx] * (self._max_text_len - + len(structure)) # pad structure = np.array(structure) data['structure'] = structure - elem_char_idx1 = self.dict_elem[''] - elem_char_idx2 = self.dict_elem[' 0: - span_weight = len(td_idx_list) * 1.0 / len(span_idx_list) - span_weight = min(max(span_weight, 1.0), self.span_weight) - for cno in range(len(cells)): - if 'bbox' in cells[cno]: - bbox = cells[cno]['bbox'].copy() - bbox[0] = bbox[0] * 1.0 / img_width - bbox[1] = bbox[1] * 1.0 / img_height - bbox[2] = bbox[2] * 1.0 / img_width - bbox[3] = bbox[3] * 1.0 / img_height - td_idx = td_idx_list[cno] - bbox_list[td_idx] = bbox - bbox_list_mask[td_idx] = 1.0 - cand_span_idx = td_idx + 1 - if cand_span_idx < (self.max_elem_length + 2): - if structure[cand_span_idx] in span_idx_list: - structure_mask[cand_span_idx] = span_weight - - data['bbox_list'] = bbox_list - data['bbox_list_mask'] = bbox_list_mask - data['structure_mask'] = structure_mask - char_beg_idx = self.get_beg_end_flag_idx('beg', 'char') - char_end_idx = self.get_beg_end_flag_idx('end', 'char') - elem_beg_idx = self.get_beg_end_flag_idx('beg', 'elem') - elem_end_idx = self.get_beg_end_flag_idx('end', 'elem') - data['sp_tokens'] = np.array([ - char_beg_idx, char_end_idx, elem_beg_idx, elem_end_idx, - elem_char_idx1, elem_char_idx2, self.max_text_length, - self.max_elem_length, self.max_cell_num, elem_num - ]) + + if len(structure) > self._max_text_len: + return None + + # encode box + bboxes = np.zeros( + (self._max_text_len, self.point_num * 2), dtype=np.float32) + bbox_masks = np.zeros((self._max_text_len, 1), dtype=np.float32) + + bbox_idx = 0 + + for i, token in enumerate(structure): + if self.idx2char[token] in self.td_token: + if 'bbox' in cells[bbox_idx] and len(cells[bbox_idx][ + 'tokens']) > 0: + bbox = cells[bbox_idx]['bbox'].copy() + bbox = np.array(bbox, dtype=np.float32).reshape(-1) + bboxes[i] = bbox + bbox_masks[i] = 1.0 + if self.learn_empty_box: + bbox_masks[i] = 1.0 + bbox_idx += 1 + data['bboxes'] = bboxes + data['bbox_masks'] = bbox_masks return data - def encode(self, text, char_or_elem): - """convert text-label into text-index. + def _merge_no_span_structure(self, structure): """ - if char_or_elem == "char": - max_len = self.max_text_length - current_dict = self.dict_character - else: - max_len = self.max_elem_length - current_dict = self.dict_elem - if len(text) > max_len: - return None - if len(text) == 0: - if char_or_elem == "char": - return [self.dict_character['space']] - else: - return None - text_list = [] - for char in text: - if char not in current_dict: - return None - text_list.append(current_dict[char]) - if len(text_list) == 0: - if char_or_elem == "char": - return [self.dict_character['space']] + This code is refer from: + https://github.com/JiaquanYe/TableMASTER-mmocr/blob/master/table_recognition/data_preprocess.py + """ + new_structure = [] + i = 0 + while i < len(structure): + token = structure[i] + if token == '': + token = '' + i += 1 + new_structure.append(token) + i += 1 + return new_structure + + def _replace_empty_cell_token(self, token_list, cells): + """ + This fun code is refer from: + https://github.com/JiaquanYe/TableMASTER-mmocr/blob/master/table_recognition/data_preprocess.py + """ + + bbox_idx = 0 + add_empty_bbox_token_list = [] + for token in token_list: + if token in ['', '']: + if 'bbox' not in cells[bbox_idx].keys(): + content = str(cells[bbox_idx]['tokens']) + token = self.empty_bbox_token_dict[content] + add_empty_bbox_token_list.append(token) + bbox_idx += 1 else: - return None - return text_list + add_empty_bbox_token_list.append(token) + return add_empty_bbox_token_list - def get_ignored_tokens(self, char_or_elem): - beg_idx = self.get_beg_end_flag_idx("beg", char_or_elem) - end_idx = self.get_beg_end_flag_idx("end", char_or_elem) - return [beg_idx, end_idx] - def get_beg_end_flag_idx(self, beg_or_end, char_or_elem): - if char_or_elem == "char": - if beg_or_end == "beg": - idx = np.array(self.dict_character[self.beg_str]) - elif beg_or_end == "end": - idx = np.array(self.dict_character[self.end_str]) - else: - assert False, "Unsupport type %s in get_beg_end_flag_idx of char" \ - % beg_or_end - elif char_or_elem == "elem": - if beg_or_end == "beg": - idx = np.array(self.dict_elem[self.beg_str]) - elif beg_or_end == "end": - idx = np.array(self.dict_elem[self.end_str]) - else: - assert False, "Unsupport type %s in get_beg_end_flag_idx of elem" \ - % beg_or_end - else: - assert False, "Unsupport type %s in char_or_elem" \ - % char_or_elem - return idx +class TableMasterLabelEncode(TableLabelEncode): + """ Convert between text-label and text-index """ + + def __init__(self, + max_text_length, + character_dict_path, + replace_empty_cell_token=False, + merge_no_span_structure=False, + learn_empty_box=False, + point_num=2, + **kwargs): + super(TableMasterLabelEncode, self).__init__( + max_text_length, character_dict_path, replace_empty_cell_token, + merge_no_span_structure, learn_empty_box, point_num, **kwargs) + self.pad_idx = self.dict[self.pad_str] + self.unknown_idx = self.dict[self.unknown_str] + + @property + def _max_text_len(self): + return self.max_text_len + + def add_special_char(self, dict_character): + self.beg_str = '' + self.end_str = '' + self.unknown_str = '' + self.pad_str = '' + dict_character = dict_character + dict_character = dict_character + [ + self.unknown_str, self.beg_str, self.end_str, self.pad_str + ] + return dict_character + + +class TableBoxEncode(object): + def __init__(self, use_xywh=False, **kwargs): + self.use_xywh = use_xywh + + def __call__(self, data): + img_height, img_width = data['image'].shape[:2] + bboxes = data['bboxes'] + if self.use_xywh and bboxes.shape[1] == 4: + bboxes = self.xyxy2xywh(bboxes) + bboxes[:, 0::2] /= img_width + bboxes[:, 1::2] /= img_height + data['bboxes'] = bboxes + return data + + def xyxy2xywh(self, bboxes): + """ + Convert coord (x1,y1,x2,y2) to (x,y,w,h). + where (x1,y1) is top-left, (x2,y2) is bottom-right. + (x,y) is bbox center and (w,h) is width and height. + :param bboxes: (x1, y1, x2, y2) + :return: + """ + new_bboxes = np.empty_like(bboxes) + new_bboxes[:, 0] = (bboxes[:, 0] + bboxes[:, 2]) / 2 # x center + new_bboxes[:, 1] = (bboxes[:, 1] + bboxes[:, 3]) / 2 # y center + new_bboxes[:, 2] = bboxes[:, 2] - bboxes[:, 0] # width + new_bboxes[:, 3] = bboxes[:, 3] - bboxes[:, 1] # height + return new_bboxes class SARLabelEncode(BaseRecLabelEncode): @@ -819,6 +869,7 @@ class VQATokenLabelEncode(object): contains_re=False, add_special_ids=False, algorithm='LayoutXLM', + use_textline_bbox_info=True, infer_mode=False, ocr_engine=None, **kwargs): @@ -847,11 +898,51 @@ class VQATokenLabelEncode(object): self.add_special_ids = add_special_ids self.infer_mode = infer_mode self.ocr_engine = ocr_engine + self.use_textline_bbox_info = use_textline_bbox_info + + def split_bbox(self, bbox, text, tokenizer): + words = text.split() + token_bboxes = [] + curr_word_idx = 0 + x1, y1, x2, y2 = bbox + unit_w = (x2 - x1) / len(text) + for idx, word in enumerate(words): + curr_w = len(word) * unit_w + word_bbox = [x1, y1, x1 + curr_w, y2] + token_bboxes.extend([word_bbox] * len(tokenizer.tokenize(word))) + x1 += (len(word) + 1) * unit_w + return token_bboxes + + def filter_empty_contents(self, ocr_info): + """ + find out the empty texts and remove the links + """ + new_ocr_info = [] + empty_index = [] + for idx, info in enumerate(ocr_info): + if len(info["transcription"]) > 0: + new_ocr_info.append(copy.deepcopy(info)) + else: + empty_index.append(info["id"]) + + for idx, info in enumerate(new_ocr_info): + new_link = [] + for link in info["linking"]: + if link[0] in empty_index or link[1] in empty_index: + continue + new_link.append(link) + new_ocr_info[idx]["linking"] = new_link + return new_ocr_info def __call__(self, data): # load bbox and label info ocr_info = self._load_ocr_info(data) + # for re + train_re = self.contains_re and not self.infer_mode + if train_re: + ocr_info = self.filter_empty_contents(ocr_info) + height, width, _ = data['image'].shape words_list = [] @@ -863,8 +954,6 @@ class VQATokenLabelEncode(object): entities = [] - # for re - train_re = self.contains_re and not self.infer_mode if train_re: relations = [] id2label = {} @@ -874,19 +963,24 @@ class VQATokenLabelEncode(object): data['ocr_info'] = copy.deepcopy(ocr_info) for info in ocr_info: + text = info["transcription"] + if len(text) <= 0: + continue if train_re: # for re - if len(info["text"]) == 0: + if len(text) == 0: empty_entity.add(info["id"]) continue id2label[info["id"]] = info["label"] relations.extend([tuple(sorted(l)) for l in info["linking"]]) # smooth_box - bbox = self._smooth_box(info["bbox"], height, width) + info["bbox"] = self.trans_poly_to_bbox(info["points"]) - text = info["text"] encode_res = self.tokenizer.encode( - text, pad_to_max_seq_len=False, return_attention_mask=True) + text, + pad_to_max_seq_len=False, + return_attention_mask=True, + return_token_type_ids=True) if not self.add_special_ids: # TODO: use tok.all_special_ids to remove @@ -895,6 +989,19 @@ class VQATokenLabelEncode(object): -1] encode_res["attention_mask"] = encode_res["attention_mask"][1: -1] + + if self.use_textline_bbox_info: + bbox = [info["bbox"]] * len(encode_res["input_ids"]) + else: + bbox = self.split_bbox(info["bbox"], info["transcription"], + self.tokenizer) + if len(bbox) <= 0: + continue + bbox = self._smooth_box(bbox, height, width) + if self.add_special_ids: + bbox.insert(0, [0, 0, 0, 0]) + bbox.append([0, 0, 0, 0]) + # parse label if not self.infer_mode: label = info['label'] @@ -919,7 +1026,7 @@ class VQATokenLabelEncode(object): }) input_ids_list.extend(encode_res["input_ids"]) token_type_ids_list.extend(encode_res["token_type_ids"]) - bbox_list.extend([bbox] * len(encode_res["input_ids"])) + bbox_list.extend(bbox) words_list.append(text) segment_offset_id.append(len(input_ids_list)) if not self.infer_mode: @@ -944,40 +1051,42 @@ class VQATokenLabelEncode(object): data['entity_id_to_index_map'] = entity_id_to_index_map return data - def _load_ocr_info(self, data): - def trans_poly_to_bbox(poly): - x1 = np.min([p[0] for p in poly]) - x2 = np.max([p[0] for p in poly]) - y1 = np.min([p[1] for p in poly]) - y2 = np.max([p[1] for p in poly]) - return [x1, y1, x2, y2] + def trans_poly_to_bbox(self, poly): + x1 = np.min([p[0] for p in poly]) + x2 = np.max([p[0] for p in poly]) + y1 = np.min([p[1] for p in poly]) + y2 = np.max([p[1] for p in poly]) + return [x1, y1, x2, y2] + def _load_ocr_info(self, data): if self.infer_mode: ocr_result = self.ocr_engine.ocr(data['image'], cls=False) ocr_info = [] for res in ocr_result: ocr_info.append({ - "text": res[1][0], - "bbox": trans_poly_to_bbox(res[0]), - "poly": res[0], + "transcription": res[1][0], + "bbox": self.trans_poly_to_bbox(res[0]), + "points": res[0], }) return ocr_info else: info = data['label'] # read text info info_dict = json.loads(info) - return info_dict["ocr_info"] + return info_dict - def _smooth_box(self, bbox, height, width): - bbox[0] = int(bbox[0] * 1000.0 / width) - bbox[2] = int(bbox[2] * 1000.0 / width) - bbox[1] = int(bbox[1] * 1000.0 / height) - bbox[3] = int(bbox[3] * 1000.0 / height) - return bbox + def _smooth_box(self, bboxes, height, width): + bboxes = np.array(bboxes) + bboxes[:, 0] = bboxes[:, 0] * 1000 / width + bboxes[:, 2] = bboxes[:, 2] * 1000 / width + bboxes[:, 1] = bboxes[:, 1] * 1000 / height + bboxes[:, 3] = bboxes[:, 3] * 1000 / height + bboxes = bboxes.astype("int64").tolist() + return bboxes def _parse_label(self, label, encode_res): gt_label = [] - if label.lower() == "other": + if label.lower() in ["other", "others", "ignore"]: gt_label.extend([0] * len(encode_res["input_ids"])) else: gt_label.append(self.label2id_map[("b-" + label).upper()]) @@ -1001,7 +1110,6 @@ class MultiLabelEncode(BaseRecLabelEncode): use_space_char, **kwargs) def __call__(self, data): - data_ctc = copy.deepcopy(data) data_sar = copy.deepcopy(data) data_out = dict() @@ -1111,3 +1219,38 @@ class ABINetLabelEncode(BaseRecLabelEncode): def add_special_char(self, dict_character): dict_character = [''] + dict_character return dict_character + + +class SPINLabelEncode(AttnLabelEncode): + """ Convert between text-label and text-index """ + + def __init__(self, + max_text_length, + character_dict_path=None, + use_space_char=False, + lower=True, + **kwargs): + super(SPINLabelEncode, self).__init__( + max_text_length, character_dict_path, use_space_char) + self.lower = lower + + def add_special_char(self, dict_character): + self.beg_str = "sos" + self.end_str = "eos" + dict_character = [self.beg_str] + [self.end_str] + dict_character + return dict_character + + def __call__(self, data): + text = data['label'] + text = self.encode(text) + if text is None: + return None + if len(text) > self.max_text_len: + return None + data['length'] = np.array(len(text)) + target = [0] + text + [1] + padded_text = [0 for _ in range(self.max_text_len + 2)] + + padded_text[:len(target)] = target + data['label'] = np.array(padded_text) + return data diff --git a/ppocr/data/imaug/operators.py b/ppocr/data/imaug/operators.py index 5397d71ccb466235e64f85e1eb9365ba03d2aa17..04cc2848fb4d25baaf553c6eda235ddb0e86511f 100644 --- a/ppocr/data/imaug/operators.py +++ b/ppocr/data/imaug/operators.py @@ -205,9 +205,12 @@ class DetResizeForTest(object): def __init__(self, **kwargs): super(DetResizeForTest, self).__init__() self.resize_type = 0 + self.keep_ratio = False if 'image_shape' in kwargs: self.image_shape = kwargs['image_shape'] self.resize_type = 1 + if 'keep_ratio' in kwargs: + self.keep_ratio = kwargs['keep_ratio'] elif 'limit_side_len' in kwargs: self.limit_side_len = kwargs['limit_side_len'] self.limit_type = kwargs.get('limit_type', 'min') @@ -237,6 +240,10 @@ class DetResizeForTest(object): def resize_image_type1(self, img): resize_h, resize_w = self.image_shape ori_h, ori_w = img.shape[:2] # (h, w, c) + if self.keep_ratio is True: + resize_w = ori_w * resize_h / ori_h + N = math.ceil(resize_w / 32) + resize_w = N * 32 ratio_h = float(resize_h) / ori_h ratio_w = float(resize_w) / ori_w img = cv2.resize(img, (int(resize_w), int(resize_h))) diff --git a/ppocr/data/imaug/rec_img_aug.py b/ppocr/data/imaug/rec_img_aug.py index 26773d0a516dfb0877453c7a5c8c8a2b5da92045..c5d8a3b2fd773a1877a788401a926d7fbca07adf 100644 --- a/ppocr/data/imaug/rec_img_aug.py +++ b/ppocr/data/imaug/rec_img_aug.py @@ -259,6 +259,49 @@ class PRENResizeImg(object): data['image'] = resized_img.astype(np.float32) return data +class SPINRecResizeImg(object): + def __init__(self, + image_shape, + interpolation=2, + mean=(127.5, 127.5, 127.5), + std=(127.5, 127.5, 127.5), + **kwargs): + self.image_shape = image_shape + + self.mean = np.array(mean, dtype=np.float32) + self.std = np.array(std, dtype=np.float32) + self.interpolation = interpolation + + def __call__(self, data): + img = data['image'] + img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) + # different interpolation type corresponding the OpenCV + if self.interpolation == 0: + interpolation = cv2.INTER_NEAREST + elif self.interpolation == 1: + interpolation = cv2.INTER_LINEAR + elif self.interpolation == 2: + interpolation = cv2.INTER_CUBIC + elif self.interpolation == 3: + interpolation = cv2.INTER_AREA + else: + raise Exception("Unsupported interpolation type !!!") + # Deal with the image error during image loading + if img is None: + return None + + img = cv2.resize(img, tuple(self.image_shape), interpolation) + img = np.array(img, np.float32) + img = np.expand_dims(img, -1) + img = img.transpose((2, 0, 1)) + # normalize the image + img = img.copy().astype(np.float32) + mean = np.float64(self.mean.reshape(1, -1)) + stdinv = 1 / np.float64(self.std.reshape(1, -1)) + img -= mean + img *= stdinv + data['image'] = img + return data class GrayRecResizeImg(object): def __init__(self, diff --git a/ppocr/data/imaug/gen_table_mask.py b/ppocr/data/imaug/table_ops.py similarity index 77% rename from ppocr/data/imaug/gen_table_mask.py rename to ppocr/data/imaug/table_ops.py index 08e35d5d1df7f9663b4e008451308d0ee409cf5a..8d139190ab4b22c553036ddc8e31cfbc7ec3423d 100644 --- a/ppocr/data/imaug/gen_table_mask.py +++ b/ppocr/data/imaug/table_ops.py @@ -32,7 +32,7 @@ class GenTableMask(object): self.shrink_h_max = 5 self.shrink_w_max = 5 self.mask_type = mask_type - + def projection(self, erosion, h, w, spilt_threshold=0): # 水平投影 projection_map = np.ones_like(erosion) @@ -48,10 +48,12 @@ class GenTableMask(object): in_text = False # 是否遍历到了字符区内 box_list = [] for i in range(len(project_val_array)): - if in_text == False and project_val_array[i] > spilt_threshold: # 进入字符区了 + if in_text == False and project_val_array[ + i] > spilt_threshold: # 进入字符区了 in_text = True start_idx = i - elif project_val_array[i] <= spilt_threshold and in_text == True: # 进入空白区了 + elif project_val_array[ + i] <= spilt_threshold and in_text == True: # 进入空白区了 end_idx = i in_text = False if end_idx - start_idx <= 2: @@ -70,7 +72,8 @@ class GenTableMask(object): box_gray_img = cv2.cvtColor(box_img, cv2.COLOR_BGR2GRAY) h, w = box_gray_img.shape # 灰度图片进行二值化处理 - ret, thresh1 = cv2.threshold(box_gray_img, 200, 255, cv2.THRESH_BINARY_INV) + ret, thresh1 = cv2.threshold(box_gray_img, 200, 255, + cv2.THRESH_BINARY_INV) # 纵向腐蚀 if h < w: kernel = np.ones((2, 1), np.uint8) @@ -95,10 +98,12 @@ class GenTableMask(object): box_list = [] spilt_threshold = 0 for i in range(len(project_val_array)): - if in_text == False and project_val_array[i] > spilt_threshold: # 进入字符区了 + if in_text == False and project_val_array[ + i] > spilt_threshold: # 进入字符区了 in_text = True start_idx = i - elif project_val_array[i] <= spilt_threshold and in_text == True: # 进入空白区了 + elif project_val_array[ + i] <= spilt_threshold and in_text == True: # 进入空白区了 end_idx = i in_text = False if end_idx - start_idx <= 2: @@ -120,7 +125,8 @@ class GenTableMask(object): h_end = h word_img = erosion[h_start:h_end + 1, :] word_h, word_w = word_img.shape - w_split_list, w_projection_map = self.projection(word_img.T, word_w, word_h) + w_split_list, w_projection_map = self.projection(word_img.T, + word_w, word_h) w_start, w_end = w_split_list[0][0], w_split_list[-1][1] if h_start > 0: h_start -= 1 @@ -170,75 +176,54 @@ class GenTableMask(object): for sno in range(len(split_bbox_list)): left, top, right, bottom = split_bbox_list[sno] - left, top, right, bottom = self.shrink_bbox([left, top, right, bottom]) + left, top, right, bottom = self.shrink_bbox( + [left, top, right, bottom]) if self.mask_type == 1: mask_img[top:bottom, left:right] = 1.0 data['mask_img'] = mask_img else: - mask_img[top:bottom, left:right, :] = (255, 255, 255) + mask_img[top:bottom, left:right, :] = (255, 255, 255) data['image'] = mask_img return data + class ResizeTableImage(object): - def __init__(self, max_len, **kwargs): + def __init__(self, max_len, resize_bboxes=False, infer_mode=False, + **kwargs): super(ResizeTableImage, self).__init__() self.max_len = max_len + self.resize_bboxes = resize_bboxes + self.infer_mode = infer_mode - def get_img_bbox(self, cells): - bbox_list = [] - if len(cells) == 0: - return bbox_list - cell_num = len(cells) - for cno in range(cell_num): - if "bbox" in cells[cno]: - bbox = cells[cno]['bbox'] - bbox_list.append(bbox) - return bbox_list - - def resize_img_table(self, img, bbox_list, max_len): + def __call__(self, data): + img = data['image'] height, width = img.shape[0:2] - ratio = max_len / (max(height, width) * 1.0) + ratio = self.max_len / (max(height, width) * 1.0) resize_h = int(height * ratio) resize_w = int(width * ratio) - img_new = cv2.resize(img, (resize_w, resize_h)) - bbox_list_new = [] - for bno in range(len(bbox_list)): - left, top, right, bottom = bbox_list[bno].copy() - left = int(left * ratio) - top = int(top * ratio) - right = int(right * ratio) - bottom = int(bottom * ratio) - bbox_list_new.append([left, top, right, bottom]) - return img_new, bbox_list_new - - def __call__(self, data): - img = data['image'] - if 'cells' not in data: - cells = [] - else: - cells = data['cells'] - bbox_list = self.get_img_bbox(cells) - img_new, bbox_list_new = self.resize_img_table(img, bbox_list, self.max_len) - data['image'] = img_new - cell_num = len(cells) - bno = 0 - for cno in range(cell_num): - if "bbox" in data['cells'][cno]: - data['cells'][cno]['bbox'] = bbox_list_new[bno] - bno += 1 + resize_img = cv2.resize(img, (resize_w, resize_h)) + if self.resize_bboxes and not self.infer_mode: + data['bboxes'] = data['bboxes'] * ratio + data['image'] = resize_img + data['src_img'] = img + data['shape'] = np.array([resize_h, resize_w, ratio, ratio]) data['max_len'] = self.max_len return data + class PaddingTableImage(object): - def __init__(self, **kwargs): + def __init__(self, size, **kwargs): super(PaddingTableImage, self).__init__() - + self.size = size + def __call__(self, data): img = data['image'] - max_len = data['max_len'] - padding_img = np.zeros((max_len, max_len, 3), dtype=np.float32) + pad_h, pad_w = self.size + padding_img = np.zeros((pad_h, pad_w, 3), dtype=np.float32) height, width = img.shape[0:2] padding_img[0:height, 0:width, :] = img.copy() data['image'] = padding_img + shape = data['shape'].tolist() + shape.extend([pad_h, pad_w]) + data['shape'] = np.array(shape) return data - \ No newline at end of file diff --git a/ppocr/data/imaug/vqa/__init__.py b/ppocr/data/imaug/vqa/__init__.py index a5025e7985198e7ee40d6c92d8e1814eb1797032..bde175115536a3f644750260082204fe5f10dc05 100644 --- a/ppocr/data/imaug/vqa/__init__.py +++ b/ppocr/data/imaug/vqa/__init__.py @@ -13,7 +13,12 @@ # limitations under the License. from .token import VQATokenPad, VQASerTokenChunk, VQAReTokenChunk, VQAReTokenRelation +from .augment import DistortBBox __all__ = [ - 'VQATokenPad', 'VQASerTokenChunk', 'VQAReTokenChunk', 'VQAReTokenRelation' + 'VQATokenPad', + 'VQASerTokenChunk', + 'VQAReTokenChunk', + 'VQAReTokenRelation', + 'DistortBBox', ] diff --git a/ppocr/data/imaug/vqa/augment.py b/ppocr/data/imaug/vqa/augment.py new file mode 100644 index 0000000000000000000000000000000000000000..fcdc9685e9855c3a2d8e9f6f5add270f95f15a6c --- /dev/null +++ b/ppocr/data/imaug/vqa/augment.py @@ -0,0 +1,37 @@ +# copyright (c) 2022 PaddlePaddle Authors. All Rights Reserve. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import sys +import numpy as np +import random + + +class DistortBBox: + def __init__(self, prob=0.5, max_scale=1, **kwargs): + """Random distort bbox + """ + self.prob = prob + self.max_scale = max_scale + + def __call__(self, data): + if random.random() > self.prob: + return data + bbox = np.array(data['bbox']) + rnd_scale = (np.random.rand(*bbox.shape) - 0.5) * 2 * self.max_scale + bbox = np.round(bbox + rnd_scale).astype(bbox.dtype) + data['bbox'] = np.clip(data['bbox'], 0, 1000) + data['bbox'] = bbox.tolist() + sys.stdout.flush() + return data diff --git a/ppocr/data/pubtab_dataset.py b/ppocr/data/pubtab_dataset.py index 671cda76fb4c36f3ac6bcc7da5a7fc4de241c0e2..642d3eb1961cbf0e829e6fb122f38c6af99df1c5 100644 --- a/ppocr/data/pubtab_dataset.py +++ b/ppocr/data/pubtab_dataset.py @@ -16,6 +16,7 @@ import os import random from paddle.io import Dataset import json +from copy import deepcopy from .imaug import transform, create_operators @@ -29,33 +30,63 @@ class PubTabDataSet(Dataset): dataset_config = config[mode]['dataset'] loader_config = config[mode]['loader'] - label_file_path = dataset_config.pop('label_file_path') + label_file_list = dataset_config.pop('label_file_list') + data_source_num = len(label_file_list) + ratio_list = dataset_config.get("ratio_list", [1.0]) + if isinstance(ratio_list, (float, int)): + ratio_list = [float(ratio_list)] * int(data_source_num) + + assert len( + ratio_list + ) == data_source_num, "The length of ratio_list should be the same as the file_list." self.data_dir = dataset_config['data_dir'] self.do_shuffle = loader_config['shuffle'] - self.do_hard_select = False - if 'hard_select' in loader_config: - self.do_hard_select = loader_config['hard_select'] - self.hard_prob = loader_config['hard_prob'] - if self.do_hard_select: - self.img_select_prob = self.load_hard_select_prob() - self.table_select_type = None - if 'table_select_type' in loader_config: - self.table_select_type = loader_config['table_select_type'] - self.table_select_prob = loader_config['table_select_prob'] self.seed = seed - logger.info("Initialize indexs of datasets:%s" % label_file_path) - with open(label_file_path, "rb") as f: - self.data_lines = f.readlines() - self.data_idx_order_list = list(range(len(self.data_lines))) - if mode.lower() == "train": + self.mode = mode.lower() + logger.info("Initialize indexs of datasets:%s" % label_file_list) + self.data_lines = self.get_image_info_list(label_file_list, ratio_list) + # self.check(config['Global']['max_text_length']) + + if mode.lower() == "train" and self.do_shuffle: self.shuffle_data_random() self.ops = create_operators(dataset_config['transforms'], global_config) - - ratio_list = dataset_config.get("ratio_list", [1.0]) self.need_reset = True in [x < 1 for x in ratio_list] + def get_image_info_list(self, file_list, ratio_list): + if isinstance(file_list, str): + file_list = [file_list] + data_lines = [] + for idx, file in enumerate(file_list): + with open(file, "rb") as f: + lines = f.readlines() + if self.mode == "train" or ratio_list[idx] < 1.0: + random.seed(self.seed) + lines = random.sample(lines, + round(len(lines) * ratio_list[idx])) + data_lines.extend(lines) + return data_lines + + def check(self, max_text_length): + data_lines = [] + for line in self.data_lines: + data_line = line.decode('utf-8').strip("\n") + info = json.loads(data_line) + file_name = info['filename'] + cells = info['html']['cells'].copy() + structure = info['html']['structure']['tokens'].copy() + + img_path = os.path.join(self.data_dir, file_name) + if not os.path.exists(img_path): + self.logger.warning("{} does not exist!".format(img_path)) + continue + if len(structure) == 0 or len(structure) > max_text_length: + continue + # data = {'img_path': img_path, 'cells': cells, 'structure':structure,'file_name':file_name} + data_lines.append(line) + self.data_lines = data_lines + def shuffle_data_random(self): if self.do_shuffle: random.seed(self.seed) @@ -68,47 +99,35 @@ class PubTabDataSet(Dataset): data_line = data_line.decode('utf-8').strip("\n") info = json.loads(data_line) file_name = info['filename'] - select_flag = True - if self.do_hard_select: - prob = self.img_select_prob[file_name] - if prob < random.uniform(0, 1): - select_flag = False - - if self.table_select_type: - structure = info['html']['structure']['tokens'].copy() - structure_str = ''.join(structure) - table_type = "simple" - if 'colspan' in structure_str or 'rowspan' in structure_str: - table_type = "complex" - if table_type == "complex": - if self.table_select_prob < random.uniform(0, 1): - select_flag = False - - if select_flag: - cells = info['html']['cells'].copy() - structure = info['html']['structure'].copy() - img_path = os.path.join(self.data_dir, file_name) - data = { - 'img_path': img_path, - 'cells': cells, - 'structure': structure - } - if not os.path.exists(img_path): - raise Exception("{} does not exist!".format(img_path)) - with open(data['img_path'], 'rb') as f: - img = f.read() - data['image'] = img - outs = transform(data, self.ops) - else: - outs = None - except Exception as e: + cells = info['html']['cells'].copy() + structure = info['html']['structure']['tokens'].copy() + + img_path = os.path.join(self.data_dir, file_name) + if not os.path.exists(img_path): + raise Exception("{} does not exist!".format(img_path)) + data = { + 'img_path': img_path, + 'cells': cells, + 'structure': structure, + 'file_name': file_name + } + + with open(data['img_path'], 'rb') as f: + img = f.read() + data['image'] = img + outs = transform(data, self.ops) + except: + import traceback + err = traceback.format_exc() self.logger.error( "When parsing line {}, error happened with msg: {}".format( - data_line, e)) + data_line, err)) outs = None if outs is None: - return self.__getitem__(np.random.randint(self.__len__())) + rnd_idx = np.random.randint(self.__len__( + )) if self.mode == "train" else (idx + 1) % self.__len__() + return self.__getitem__(rnd_idx) return outs def __len__(self): - return len(self.data_idx_order_list) + return len(self.data_lines) diff --git a/ppocr/losses/__init__.py b/ppocr/losses/__init__.py index 7bea87f62f335a9a47c881d4bc789ce34aaa734a..30120ac56756edd38676c40c39f0130f1b07c3ef 100755 --- a/ppocr/losses/__init__.py +++ b/ppocr/losses/__init__.py @@ -35,6 +35,7 @@ from .rec_sar_loss import SARLoss from .rec_aster_loss import AsterLoss from .rec_pren_loss import PRENLoss from .rec_multi_loss import MultiLoss +from .rec_spin_att_loss import SPINAttentionLoss # cls loss from .cls_loss import ClsLoss @@ -51,7 +52,7 @@ from .combined_loss import CombinedLoss # table loss from .table_att_loss import TableAttentionLoss - +from .table_master_loss import TableMasterLoss # vqa token loss from .vqa_token_layoutlm_loss import VQASerTokenLayoutLMLoss @@ -61,7 +62,8 @@ def build_loss(config): 'DBLoss', 'PSELoss', 'EASTLoss', 'SASTLoss', 'FCELoss', 'CTCLoss', 'ClsLoss', 'AttentionLoss', 'SRNLoss', 'PGLoss', 'CombinedLoss', 'CELoss', 'TableAttentionLoss', 'SARLoss', 'AsterLoss', 'SDMGRLoss', - 'VQASerTokenLayoutLMLoss', 'LossFromOutput', 'PRENLoss', 'MultiLoss' + 'VQASerTokenLayoutLMLoss', 'LossFromOutput', 'PRENLoss', 'MultiLoss', + 'TableMasterLoss', 'SPINAttentionLoss' ] config = copy.deepcopy(config) module_name = config.pop('name') diff --git a/ppocr/losses/basic_loss.py b/ppocr/losses/basic_loss.py index 2df96ea2642d10a50eb892d738f89318dc5e0f4c..74490791c2af0be54dab8ab30ac323790fcac657 100644 --- a/ppocr/losses/basic_loss.py +++ b/ppocr/losses/basic_loss.py @@ -57,17 +57,24 @@ class CELoss(nn.Layer): class KLJSLoss(object): def __init__(self, mode='kl'): assert mode in ['kl', 'js', 'KL', 'JS' - ], "mode can only be one of ['kl', 'js', 'KL', 'JS']" + ], "mode can only be one of ['kl', 'KL', 'js', 'JS']" self.mode = mode def __call__(self, p1, p2, reduction="mean"): - loss = paddle.multiply(p2, paddle.log((p2 + 1e-5) / (p1 + 1e-5) + 1e-5)) - - if self.mode.lower() == "js": + if self.mode.lower() == 'kl': + loss = paddle.multiply(p2, paddle.log((p2 + 1e-5) / (p1 + 1e-5) + 1e-5)) + loss += paddle.multiply( + p1, paddle.log((p1 + 1e-5) / (p2 + 1e-5) + 1e-5)) + loss *= 0.5 + elif self.mode.lower() == "js": + loss = paddle.multiply(p2, paddle.log((2*p2 + 1e-5) / (p1 + p2 + 1e-5) + 1e-5)) loss += paddle.multiply( - p1, paddle.log((p1 + 1e-5) / (p2 + 1e-5) + 1e-5)) + p1, paddle.log((2*p1 + 1e-5) / (p1 + p2 + 1e-5) + 1e-5)) loss *= 0.5 + else: + raise ValueError("The mode.lower() if KLJSLoss should be one of ['kl', 'js']") + if reduction == "mean": loss = paddle.mean(loss, axis=[1, 2]) elif reduction == "none" or reduction is None: @@ -95,7 +102,7 @@ class DMLLoss(nn.Layer): self.act = None self.use_log = use_log - self.jskl_loss = KLJSLoss(mode="js") + self.jskl_loss = KLJSLoss(mode="kl") def _kldiv(self, x, target): eps = 1.0e-10 diff --git a/ppocr/losses/rec_spin_att_loss.py b/ppocr/losses/rec_spin_att_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..195780c7bfaf4aae5dd23bd72ace268bed9c1d4f --- /dev/null +++ b/ppocr/losses/rec_spin_att_loss.py @@ -0,0 +1,45 @@ +# copyright (c) 2022 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 + +'''This code is refer from: +https://github.com/hikopensource/DAVAR-Lab-OCR +''' + +class SPINAttentionLoss(nn.Layer): + def __init__(self, reduction='mean', ignore_index=-100, **kwargs): + super(SPINAttentionLoss, self).__init__() + self.loss_func = nn.CrossEntropyLoss(weight=None, reduction=reduction, ignore_index=ignore_index) + + def forward(self, predicts, batch): + targets = batch[1].astype("int64") + targets = targets[:, 1:] # remove [eos] in label + + label_lengths = batch[2].astype('int64') + batch_size, num_steps, num_classes = predicts.shape[0], predicts.shape[ + 1], predicts.shape[2] + assert len(targets.shape) == len(list(predicts.shape)) - 1, \ + "The target's shape and inputs's shape is [N, d] and [N, num_steps]" + + inputs = paddle.reshape(predicts, [-1, predicts.shape[-1]]) + targets = paddle.reshape(targets, [-1]) + + return {'loss': self.loss_func(inputs, targets)} diff --git a/ppocr/losses/table_att_loss.py b/ppocr/losses/table_att_loss.py index 51377efa2b5e802fe9f9fc1973c74deb00fc4816..3496c9072553d839017eaa017fe47dfb66fb9d3b 100644 --- a/ppocr/losses/table_att_loss.py +++ b/ppocr/losses/table_att_loss.py @@ -20,15 +20,21 @@ import paddle from paddle import nn from paddle.nn import functional as F + class TableAttentionLoss(nn.Layer): - def __init__(self, structure_weight, loc_weight, use_giou=False, giou_weight=1.0, **kwargs): + def __init__(self, + structure_weight, + loc_weight, + use_giou=False, + giou_weight=1.0, + **kwargs): super(TableAttentionLoss, self).__init__() self.loss_func = nn.CrossEntropyLoss(weight=None, reduction='none') self.structure_weight = structure_weight self.loc_weight = loc_weight self.use_giou = use_giou self.giou_weight = giou_weight - + def giou_loss(self, preds, bbox, eps=1e-7, reduction='mean'): ''' :param preds:[[x1,y1,x2,y2], [x1,y1,x2,y2],,,] @@ -47,9 +53,10 @@ class TableAttentionLoss(nn.Layer): inters = iw * ih # union - uni = (preds[:, 2] - preds[:, 0] + 1e-3) * (preds[:, 3] - preds[:, 1] + 1e-3 - ) + (bbox[:, 2] - bbox[:, 0] + 1e-3) * ( - bbox[:, 3] - bbox[:, 1] + 1e-3) - inters + eps + uni = (preds[:, 2] - preds[:, 0] + 1e-3) * ( + preds[:, 3] - preds[:, 1] + 1e-3) + (bbox[:, 2] - bbox[:, 0] + 1e-3 + ) * (bbox[:, 3] - bbox[:, 1] + + 1e-3) - inters + eps # ious ious = inters / uni @@ -79,30 +86,34 @@ class TableAttentionLoss(nn.Layer): structure_probs = predicts['structure_probs'] structure_targets = batch[1].astype("int64") structure_targets = structure_targets[:, 1:] - if len(batch) == 6: - structure_mask = batch[5].astype("int64") - structure_mask = structure_mask[:, 1:] - structure_mask = paddle.reshape(structure_mask, [-1]) - structure_probs = paddle.reshape(structure_probs, [-1, structure_probs.shape[-1]]) + structure_probs = paddle.reshape(structure_probs, + [-1, structure_probs.shape[-1]]) structure_targets = paddle.reshape(structure_targets, [-1]) structure_loss = self.loss_func(structure_probs, structure_targets) - - if len(batch) == 6: - structure_loss = structure_loss * structure_mask - -# structure_loss = paddle.sum(structure_loss) * self.structure_weight + structure_loss = paddle.mean(structure_loss) * self.structure_weight - + loc_preds = predicts['loc_preds'] loc_targets = batch[2].astype("float32") - loc_targets_mask = batch[4].astype("float32") + loc_targets_mask = batch[3].astype("float32") loc_targets = loc_targets[:, 1:, :] loc_targets_mask = loc_targets_mask[:, 1:, :] - loc_loss = F.mse_loss(loc_preds * loc_targets_mask, loc_targets) * self.loc_weight + loc_loss = F.mse_loss(loc_preds * loc_targets_mask, + loc_targets) * self.loc_weight if self.use_giou: - loc_loss_giou = self.giou_loss(loc_preds * loc_targets_mask, loc_targets) * self.giou_weight + loc_loss_giou = self.giou_loss(loc_preds * loc_targets_mask, + loc_targets) * self.giou_weight total_loss = structure_loss + loc_loss + loc_loss_giou - return {'loss':total_loss, "structure_loss":structure_loss, "loc_loss":loc_loss, "loc_loss_giou":loc_loss_giou} + return { + 'loss': total_loss, + "structure_loss": structure_loss, + "loc_loss": loc_loss, + "loc_loss_giou": loc_loss_giou + } else: - total_loss = structure_loss + loc_loss - return {'loss':total_loss, "structure_loss":structure_loss, "loc_loss":loc_loss} \ No newline at end of file + total_loss = structure_loss + loc_loss + return { + 'loss': total_loss, + "structure_loss": structure_loss, + "loc_loss": loc_loss + } diff --git a/ppocr/losses/table_master_loss.py b/ppocr/losses/table_master_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..dca982dbd43e2c14f15503e1e98d6fe6c18878c5 --- /dev/null +++ b/ppocr/losses/table_master_loss.py @@ -0,0 +1,70 @@ +# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +This code is refer from: +https://github.com/JiaquanYe/TableMASTER-mmocr/tree/master/mmocr/models/textrecog/losses +""" + +import paddle +from paddle import nn + + +class TableMasterLoss(nn.Layer): + def __init__(self, ignore_index=-1): + super(TableMasterLoss, self).__init__() + self.structure_loss = nn.CrossEntropyLoss( + ignore_index=ignore_index, reduction='mean') + self.box_loss = nn.L1Loss(reduction='sum') + self.eps = 1e-12 + + def forward(self, predicts, batch): + # structure_loss + structure_probs = predicts['structure_probs'] + structure_targets = batch[1] + structure_targets = structure_targets[:, 1:] + structure_probs = structure_probs.reshape( + [-1, structure_probs.shape[-1]]) + structure_targets = structure_targets.reshape([-1]) + + structure_loss = self.structure_loss(structure_probs, structure_targets) + structure_loss = structure_loss.mean() + losses = dict(structure_loss=structure_loss) + + # box loss + bboxes_preds = predicts['loc_preds'] + bboxes_targets = batch[2][:, 1:, :] + bbox_masks = batch[3][:, 1:] + # mask empty-bbox or non-bbox structure token's bbox. + + masked_bboxes_preds = bboxes_preds * bbox_masks + masked_bboxes_targets = bboxes_targets * bbox_masks + + # horizon loss (x and width) + horizon_sum_loss = self.box_loss(masked_bboxes_preds[:, :, 0::2], + masked_bboxes_targets[:, :, 0::2]) + horizon_loss = horizon_sum_loss / (bbox_masks.sum() + self.eps) + # vertical loss (y and height) + vertical_sum_loss = self.box_loss(masked_bboxes_preds[:, :, 1::2], + masked_bboxes_targets[:, :, 1::2]) + vertical_loss = vertical_sum_loss / (bbox_masks.sum() + self.eps) + + horizon_loss = horizon_loss.mean() + vertical_loss = vertical_loss.mean() + all_loss = structure_loss + horizon_loss + vertical_loss + losses.update({ + 'loss': all_loss, + 'horizon_bbox_loss': horizon_loss, + 'vertical_bbox_loss': vertical_loss + }) + return losses diff --git a/ppocr/losses/vqa_token_layoutlm_loss.py b/ppocr/losses/vqa_token_layoutlm_loss.py index 244893d97d0e422c5ca270bdece689e13aba2b07..f9cd4634731a26dd990d6ffac3d8defc8cdf7e97 100755 --- a/ppocr/losses/vqa_token_layoutlm_loss.py +++ b/ppocr/losses/vqa_token_layoutlm_loss.py @@ -27,8 +27,8 @@ class VQASerTokenLayoutLMLoss(nn.Layer): self.ignore_index = self.loss_class.ignore_index def forward(self, predicts, batch): - labels = batch[1] - attention_mask = batch[4] + labels = batch[5] + attention_mask = batch[2] if attention_mask is not None: active_loss = attention_mask.reshape([-1, ]) == 1 active_outputs = predicts.reshape( diff --git a/ppocr/metrics/eval_det_iou.py b/ppocr/metrics/eval_det_iou.py index bc05e7df7d1d21abfb9d9fbd224ecd7254d9f393..c144886b3f84a458a88931d6beb2153054eba7d0 100644 --- a/ppocr/metrics/eval_det_iou.py +++ b/ppocr/metrics/eval_det_iou.py @@ -83,14 +83,10 @@ class DetectionIoUEvaluator(object): evaluationLog = "" - # print(len(gt)) for n in range(len(gt)): points = gt[n]['points'] - # transcription = gt[n]['text'] dontCare = gt[n]['ignore'] - # points = Polygon(points) - # points = points.buffer(0) - if not Polygon(points).is_valid or not Polygon(points).is_simple: + if not Polygon(points).is_valid: continue gtPol = points @@ -105,9 +101,7 @@ class DetectionIoUEvaluator(object): for n in range(len(pred)): points = pred[n]['points'] - # points = Polygon(points) - # points = points.buffer(0) - if not Polygon(points).is_valid or not Polygon(points).is_simple: + if not Polygon(points).is_valid: continue detPol = points @@ -191,8 +185,6 @@ class DetectionIoUEvaluator(object): methodHmean = 0 if methodRecall + methodPrecision == 0 else 2 * \ methodRecall * methodPrecision / ( methodRecall + methodPrecision) - # print(methodRecall, methodPrecision, methodHmean) - # sys.exit(-1) methodMetrics = { 'precision': methodPrecision, 'recall': methodRecall, diff --git a/ppocr/metrics/table_metric.py b/ppocr/metrics/table_metric.py index ca4d6474202b4e85cadf86ccb2fe2726c7fa9aeb..fd2631e442b8d111c64d5cf4b34ea9063d8c60dd 100644 --- a/ppocr/metrics/table_metric.py +++ b/ppocr/metrics/table_metric.py @@ -12,29 +12,30 @@ # See the License for the specific language governing permissions and # limitations under the License. import numpy as np +from ppocr.metrics.det_metric import DetMetric -class TableMetric(object): - def __init__(self, main_indicator='acc', **kwargs): +class TableStructureMetric(object): + def __init__(self, main_indicator='acc', eps=1e-6, **kwargs): self.main_indicator = main_indicator - self.eps = 1e-5 + self.eps = eps self.reset() - def __call__(self, pred, batch, *args, **kwargs): - structure_probs = pred['structure_probs'].numpy() - structure_labels = batch[1] + def __call__(self, pred_label, batch=None, *args, **kwargs): + preds, labels = pred_label + pred_structure_batch_list = preds['structure_batch_list'] + gt_structure_batch_list = labels['structure_batch_list'] correct_num = 0 all_num = 0 - structure_probs = np.argmax(structure_probs, axis=2) - structure_labels = structure_labels[:, 1:] - batch_size = structure_probs.shape[0] - for bno in range(batch_size): - all_num += 1 - if (structure_probs[bno] == structure_labels[bno]).all(): + for (pred, pred_conf), target in zip(pred_structure_batch_list, + gt_structure_batch_list): + pred_str = ''.join(pred) + target_str = ''.join(target) + if pred_str == target_str: correct_num += 1 + all_num += 1 self.correct_num += correct_num self.all_num += all_num - return {'acc': correct_num * 1.0 / (all_num + self.eps), } def get_metric(self): """ @@ -49,3 +50,89 @@ class TableMetric(object): def reset(self): self.correct_num = 0 self.all_num = 0 + self.len_acc_num = 0 + self.token_nums = 0 + self.anys_dict = dict() + + +class TableMetric(object): + def __init__(self, + main_indicator='acc', + compute_bbox_metric=False, + point_num=2, + **kwargs): + """ + + @param sub_metrics: configs of sub_metric + @param main_matric: main_matric for save best_model + @param kwargs: + """ + self.structure_metric = TableStructureMetric() + self.bbox_metric = DetMetric() if compute_bbox_metric else None + self.main_indicator = main_indicator + self.point_num = point_num + self.reset() + + def __call__(self, pred_label, batch=None, *args, **kwargs): + self.structure_metric(pred_label) + if self.bbox_metric is not None: + self.bbox_metric(*self.prepare_bbox_metric_input(pred_label)) + + def prepare_bbox_metric_input(self, pred_label): + pred_bbox_batch_list = [] + gt_ignore_tags_batch_list = [] + gt_bbox_batch_list = [] + preds, labels = pred_label + + batch_num = len(preds['bbox_batch_list']) + for batch_idx in range(batch_num): + # pred + pred_bbox_list = [ + self.format_box(pred_box) + for pred_box in preds['bbox_batch_list'][batch_idx] + ] + pred_bbox_batch_list.append({'points': pred_bbox_list}) + + # gt + gt_bbox_list = [] + gt_ignore_tags_list = [] + for gt_box in labels['bbox_batch_list'][batch_idx]: + gt_bbox_list.append(self.format_box(gt_box)) + gt_ignore_tags_list.append(0) + gt_bbox_batch_list.append(gt_bbox_list) + gt_ignore_tags_batch_list.append(gt_ignore_tags_list) + + return [ + pred_bbox_batch_list, + [0, 0, gt_bbox_batch_list, gt_ignore_tags_batch_list] + ] + + def get_metric(self): + structure_metric = self.structure_metric.get_metric() + if self.bbox_metric is None: + return structure_metric + bbox_metric = self.bbox_metric.get_metric() + if self.main_indicator == self.bbox_metric.main_indicator: + output = bbox_metric + for sub_key in structure_metric: + output["structure_metric_{}".format( + sub_key)] = structure_metric[sub_key] + else: + output = structure_metric + for sub_key in bbox_metric: + output["bbox_metric_{}".format(sub_key)] = bbox_metric[sub_key] + return output + + def reset(self): + self.structure_metric.reset() + if self.bbox_metric is not None: + self.bbox_metric.reset() + + def format_box(self, box): + if self.point_num == 2: + x1, y1, x2, y2 = box + box = [[x1, y1], [x2, y1], [x2, y2], [x1, y2]] + elif self.point_num == 4: + x1, y1, x2, y2, x3, y3, x4, y4 = box + box = [[x1, y1], [x2, y2], [x3, y3], [x4, y4]] + return box diff --git a/ppocr/metrics/vqa_token_re_metric.py b/ppocr/metrics/vqa_token_re_metric.py index 8a13bc081298284194d365933cd67d5633957ee8..f84387d8beb729bcc4b420ceea24a5e9b2993c64 100644 --- a/ppocr/metrics/vqa_token_re_metric.py +++ b/ppocr/metrics/vqa_token_re_metric.py @@ -37,23 +37,26 @@ class VQAReTokenMetric(object): gt_relations = [] for b in range(len(self.relations_list)): rel_sent = [] - for head, tail in zip(self.relations_list[b]["head"], - self.relations_list[b]["tail"]): - rel = {} - rel["head_id"] = head - rel["head"] = (self.entities_list[b]["start"][rel["head_id"]], - self.entities_list[b]["end"][rel["head_id"]]) - rel["head_type"] = self.entities_list[b]["label"][rel[ - "head_id"]] - - rel["tail_id"] = tail - rel["tail"] = (self.entities_list[b]["start"][rel["tail_id"]], - self.entities_list[b]["end"][rel["tail_id"]]) - rel["tail_type"] = self.entities_list[b]["label"][rel[ - "tail_id"]] - - rel["type"] = 1 - rel_sent.append(rel) + if "head" in self.relations_list[b]: + for head, tail in zip(self.relations_list[b]["head"], + self.relations_list[b]["tail"]): + rel = {} + rel["head_id"] = head + rel["head"] = ( + self.entities_list[b]["start"][rel["head_id"]], + self.entities_list[b]["end"][rel["head_id"]]) + rel["head_type"] = self.entities_list[b]["label"][rel[ + "head_id"]] + + rel["tail_id"] = tail + rel["tail"] = ( + self.entities_list[b]["start"][rel["tail_id"]], + self.entities_list[b]["end"][rel["tail_id"]]) + rel["tail_type"] = self.entities_list[b]["label"][rel[ + "tail_id"]] + + rel["type"] = 1 + rel_sent.append(rel) gt_relations.append(rel_sent) re_metrics = self.re_score( self.pred_relations_list, gt_relations, mode="boundaries") diff --git a/ppocr/modeling/backbones/__init__.py b/ppocr/modeling/backbones/__init__.py index f8959e263ecffb301dff227ff22e5e913375f919..d4f5b15f56d34a9f6a6501058179a643ac7e8318 100755 --- a/ppocr/modeling/backbones/__init__.py +++ b/ppocr/modeling/backbones/__init__.py @@ -18,9 +18,13 @@ __all__ = ["build_backbone"] def build_backbone(config, model_type): if model_type == "det" or model_type == "table": from .det_mobilenet_v3 import MobileNetV3 - from .det_resnet_vd import ResNet + from .det_resnet import ResNet + from .det_resnet_vd import ResNet_vd from .det_resnet_vd_sast import ResNet_SAST - support_dict = ["MobileNetV3", "ResNet", "ResNet_SAST"] + support_dict = ["MobileNetV3", "ResNet", "ResNet_vd", "ResNet_SAST"] + if model_type == "table": + from .table_master_resnet import TableResNetExtra + support_dict.append('TableResNetExtra') elif model_type == "rec" or model_type == "cls": from .rec_mobilenet_v3 import MobileNetV3 from .rec_resnet_vd import ResNet @@ -28,6 +32,7 @@ def build_backbone(config, model_type): from .rec_mv1_enhance import MobileNetV1Enhance from .rec_nrtr_mtb import MTB from .rec_resnet_31 import ResNet31 + from .rec_resnet_32 import ResNet32 from .rec_resnet_45 import ResNet45 from .rec_resnet_aster import ResNet_ASTER from .rec_micronet import MicroNet @@ -37,7 +42,7 @@ def build_backbone(config, model_type): support_dict = [ 'MobileNetV1Enhance', 'MobileNetV3', 'ResNet', 'ResNetFPN', 'MTB', 'ResNet31', 'ResNet45', 'ResNet_ASTER', 'MicroNet', - 'EfficientNetb3_PREN', 'SVTRNet', 'ViTSTR' + 'EfficientNetb3_PREN', 'SVTRNet', 'ViTSTR', 'ResNet32' ] elif model_type == 'e2e': from .e2e_resnet_vd_pg import ResNet diff --git a/ppocr/modeling/backbones/det_resnet.py b/ppocr/modeling/backbones/det_resnet.py new file mode 100644 index 0000000000000000000000000000000000000000..87eef11cf0e33c24c0f539c8074b21f589345282 --- /dev/null +++ b/ppocr/modeling/backbones/det_resnet.py @@ -0,0 +1,236 @@ +# copyright (c) 2022 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 numpy as np +import paddle +from paddle import ParamAttr +import paddle.nn as nn +import paddle.nn.functional as F +from paddle.nn import Conv2D, BatchNorm, Linear, Dropout +from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D +from paddle.nn.initializer import Uniform + +import math + +from paddle.vision.ops import DeformConv2D +from paddle.regularizer import L2Decay +from paddle.nn.initializer import Normal, Constant, XavierUniform +from .det_resnet_vd import DeformableConvV2, ConvBNLayer + + +class BottleneckBlock(nn.Layer): + def __init__(self, + num_channels, + num_filters, + stride, + shortcut=True, + is_dcn=False): + super(BottleneckBlock, self).__init__() + + self.conv0 = ConvBNLayer( + in_channels=num_channels, + out_channels=num_filters, + kernel_size=1, + act="relu", ) + self.conv1 = ConvBNLayer( + in_channels=num_filters, + out_channels=num_filters, + kernel_size=3, + stride=stride, + act="relu", + is_dcn=is_dcn, + dcn_groups=1, ) + self.conv2 = ConvBNLayer( + in_channels=num_filters, + out_channels=num_filters * 4, + kernel_size=1, + act=None, ) + + if not shortcut: + self.short = ConvBNLayer( + in_channels=num_channels, + out_channels=num_filters * 4, + kernel_size=1, + stride=stride, ) + + self.shortcut = shortcut + + self._num_channels_out = num_filters * 4 + + 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, + num_channels, + num_filters, + stride, + shortcut=True, + name=None): + super(BasicBlock, self).__init__() + self.stride = stride + self.conv0 = ConvBNLayer( + in_channels=num_channels, + out_channels=num_filters, + kernel_size=3, + stride=stride, + act="relu") + self.conv1 = ConvBNLayer( + in_channels=num_filters, + out_channels=num_filters, + kernel_size=3, + act=None) + + if not shortcut: + self.short = ConvBNLayer( + in_channels=num_channels, + out_channels=num_filters, + kernel_size=1, + stride=stride) + + 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, + out_indices=None, + dcn_stage=None): + super(ResNet, self).__init__() + + self.layers = layers + self.input_image_channel = in_channels + + supported_layers = [18, 34, 50, 101, 152] + 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] + num_channels = [64, 256, 512, + 1024] if layers >= 50 else [64, 64, 128, 256] + num_filters = [64, 128, 256, 512] + + self.dcn_stage = dcn_stage if dcn_stage is not None else [ + False, False, False, False + ] + self.out_indices = out_indices if out_indices is not None else [ + 0, 1, 2, 3 + ] + + self.conv = ConvBNLayer( + in_channels=self.input_image_channel, + out_channels=64, + kernel_size=7, + stride=2, + act="relu", ) + self.pool2d_max = MaxPool2D( + kernel_size=3, + stride=2, + padding=1, ) + + self.stages = [] + self.out_channels = [] + if layers >= 50: + for block in range(len(depth)): + shortcut = False + block_list = [] + is_dcn = self.dcn_stage[block] + 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( + conv_name, + BottleneckBlock( + num_channels=num_channels[block] + if i == 0 else num_filters[block] * 4, + num_filters=num_filters[block], + stride=2 if i == 0 and block != 0 else 1, + shortcut=shortcut, + is_dcn=is_dcn)) + block_list.append(bottleneck_block) + shortcut = True + if block in self.out_indices: + self.out_channels.append(num_filters[block] * 4) + self.stages.append(nn.Sequential(*block_list)) + else: + for block in range(len(depth)): + shortcut = False + block_list = [] + for i in range(depth[block]): + conv_name = "res" + str(block + 2) + chr(97 + i) + basic_block = self.add_sublayer( + conv_name, + BasicBlock( + num_channels=num_channels[block] + if i == 0 else num_filters[block], + num_filters=num_filters[block], + stride=2 if i == 0 and block != 0 else 1, + shortcut=shortcut)) + block_list.append(basic_block) + shortcut = True + if block in self.out_indices: + self.out_channels.append(num_filters[block]) + self.stages.append(nn.Sequential(*block_list)) + + def forward(self, inputs): + y = self.conv(inputs) + y = self.pool2d_max(y) + out = [] + for i, block in enumerate(self.stages): + y = block(y) + if i in self.out_indices: + out.append(y) + return out diff --git a/ppocr/modeling/backbones/det_resnet_vd.py b/ppocr/modeling/backbones/det_resnet_vd.py index 8c955a4af377374f21e7c09f0d10952f2fe1ceed..a421da0ab440e9b87c1c7efc7d2448f8f76ad205 100644 --- a/ppocr/modeling/backbones/det_resnet_vd.py +++ b/ppocr/modeling/backbones/det_resnet_vd.py @@ -25,7 +25,7 @@ from paddle.vision.ops import DeformConv2D from paddle.regularizer import L2Decay from paddle.nn.initializer import Normal, Constant, XavierUniform -__all__ = ["ResNet"] +__all__ = ["ResNet_vd", "ConvBNLayer", "DeformableConvV2"] class DeformableConvV2(nn.Layer): @@ -104,6 +104,7 @@ class ConvBNLayer(nn.Layer): kernel_size, stride=1, groups=1, + dcn_groups=1, is_vd_mode=False, act=None, is_dcn=False): @@ -128,7 +129,7 @@ class ConvBNLayer(nn.Layer): kernel_size=kernel_size, stride=stride, padding=(kernel_size - 1) // 2, - groups=2, #groups, + groups=dcn_groups, #groups, bias_attr=False) self._batch_norm = nn.BatchNorm(out_channels, act=act) @@ -162,7 +163,8 @@ class BottleneckBlock(nn.Layer): kernel_size=3, stride=stride, act='relu', - is_dcn=is_dcn) + is_dcn=is_dcn, + dcn_groups=2) self.conv2 = ConvBNLayer( in_channels=out_channels, out_channels=out_channels * 4, @@ -238,14 +240,14 @@ class BasicBlock(nn.Layer): return y -class ResNet(nn.Layer): +class ResNet_vd(nn.Layer): def __init__(self, in_channels=3, layers=50, dcn_stage=None, out_indices=None, **kwargs): - super(ResNet, self).__init__() + super(ResNet_vd, self).__init__() self.layers = layers supported_layers = [18, 34, 50, 101, 152, 200] @@ -321,7 +323,6 @@ class ResNet(nn.Layer): for block in range(len(depth)): block_list = [] shortcut = False - # is_dcn = self.dcn_stage[block] for i in range(depth[block]): basic_block = self.add_sublayer( 'bb_%d_%d' % (block, i), diff --git a/ppocr/modeling/backbones/rec_resnet_32.py b/ppocr/modeling/backbones/rec_resnet_32.py new file mode 100644 index 0000000000000000000000000000000000000000..cbd19251a3ed43a472d49f03743ead1491aa86ac --- /dev/null +++ b/ppocr/modeling/backbones/rec_resnet_32.py @@ -0,0 +1,269 @@ +# copyright (c) 2022 PaddlePaddle Authors. All Rights Reserve. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +This code is refer from: +https://github.com/hikopensource/DAVAR-Lab-OCR/davarocr/davar_rcg/models/backbones/ResNet32.py +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import paddle.nn as nn + +__all__ = ["ResNet32"] + +conv_weight_attr = nn.initializer.KaimingNormal() + +class ResNet32(nn.Layer): + """ + Feature Extractor is proposed in FAN Ref [1] + + Ref [1]: Focusing Attention: Towards Accurate Text Recognition in Neural Images ICCV-2017 + """ + + def __init__(self, in_channels, out_channels=512): + """ + + Args: + in_channels (int): input channel + output_channel (int): output channel + """ + super(ResNet32, self).__init__() + self.out_channels = out_channels + self.ConvNet = ResNet(in_channels, out_channels, BasicBlock, [1, 2, 5, 3]) + + def forward(self, inputs): + """ + Args: + inputs: input feature + + Returns: + output feature + + """ + return self.ConvNet(inputs) + +class BasicBlock(nn.Layer): + """Res-net Basic Block""" + expansion = 1 + + def __init__(self, inplanes, planes, + stride=1, downsample=None, + norm_type='BN', **kwargs): + """ + Args: + inplanes (int): input channel + planes (int): channels of the middle feature + stride (int): stride of the convolution + downsample (int): type of the down_sample + norm_type (str): type of the normalization + **kwargs (None): backup parameter + """ + super(BasicBlock, self).__init__() + self.conv1 = self._conv3x3(inplanes, planes) + self.bn1 = nn.BatchNorm2D(planes) + self.conv2 = self._conv3x3(planes, planes) + self.bn2 = nn.BatchNorm2D(planes) + self.relu = nn.ReLU() + self.downsample = downsample + self.stride = stride + + def _conv3x3(self, in_planes, out_planes, stride=1): + """ + + Args: + in_planes (int): input channel + out_planes (int): channels of the middle feature + stride (int): stride of the convolution + Returns: + nn.Layer: Conv2D with kernel = 3 + + """ + + return nn.Conv2D(in_planes, out_planes, + kernel_size=3, stride=stride, + padding=1, weight_attr=conv_weight_attr, + bias_attr=False) + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + + if self.downsample is not None: + residual = self.downsample(x) + out += residual + out = self.relu(out) + + return out + +class ResNet(nn.Layer): + """Res-Net network structure""" + def __init__(self, input_channel, + output_channel, block, layers): + """ + + Args: + input_channel (int): input channel + output_channel (int): output channel + block (BasicBlock): convolution block + layers (list): layers of the block + """ + super(ResNet, self).__init__() + + self.output_channel_block = [int(output_channel / 4), + int(output_channel / 2), + output_channel, + output_channel] + + self.inplanes = int(output_channel / 8) + self.conv0_1 = nn.Conv2D(input_channel, int(output_channel / 16), + kernel_size=3, stride=1, + padding=1, + weight_attr=conv_weight_attr, + bias_attr=False) + self.bn0_1 = nn.BatchNorm2D(int(output_channel / 16)) + self.conv0_2 = nn.Conv2D(int(output_channel / 16), self.inplanes, + kernel_size=3, stride=1, + padding=1, + weight_attr=conv_weight_attr, + bias_attr=False) + self.bn0_2 = nn.BatchNorm2D(self.inplanes) + self.relu = nn.ReLU() + + self.maxpool1 = nn.MaxPool2D(kernel_size=2, stride=2, padding=0) + self.layer1 = self._make_layer(block, + self.output_channel_block[0], + layers[0]) + self.conv1 = nn.Conv2D(self.output_channel_block[0], + self.output_channel_block[0], + kernel_size=3, stride=1, + padding=1, + weight_attr=conv_weight_attr, + bias_attr=False) + self.bn1 = nn.BatchNorm2D(self.output_channel_block[0]) + + self.maxpool2 = nn.MaxPool2D(kernel_size=2, stride=2, padding=0) + self.layer2 = self._make_layer(block, + self.output_channel_block[1], + layers[1], stride=1) + self.conv2 = nn.Conv2D(self.output_channel_block[1], + self.output_channel_block[1], + kernel_size=3, stride=1, + padding=1, + weight_attr=conv_weight_attr, + bias_attr=False,) + self.bn2 = nn.BatchNorm2D(self.output_channel_block[1]) + + self.maxpool3 = nn.MaxPool2D(kernel_size=2, + stride=(2, 1), + padding=(0, 1)) + self.layer3 = self._make_layer(block, self.output_channel_block[2], + layers[2], stride=1) + self.conv3 = nn.Conv2D(self.output_channel_block[2], + self.output_channel_block[2], + kernel_size=3, stride=1, + padding=1, + weight_attr=conv_weight_attr, + bias_attr=False) + self.bn3 = nn.BatchNorm2D(self.output_channel_block[2]) + + self.layer4 = self._make_layer(block, self.output_channel_block[3], + layers[3], stride=1) + self.conv4_1 = nn.Conv2D(self.output_channel_block[3], + self.output_channel_block[3], + kernel_size=2, stride=(2, 1), + padding=(0, 1), + weight_attr=conv_weight_attr, + bias_attr=False) + self.bn4_1 = nn.BatchNorm2D(self.output_channel_block[3]) + self.conv4_2 = nn.Conv2D(self.output_channel_block[3], + self.output_channel_block[3], + kernel_size=2, stride=1, + padding=0, + weight_attr=conv_weight_attr, + bias_attr=False) + self.bn4_2 = nn.BatchNorm2D(self.output_channel_block[3]) + + def _make_layer(self, block, planes, blocks, stride=1): + """ + + Args: + block (block): convolution block + planes (int): input channels + blocks (list): layers of the block + stride (int): stride of the convolution + + Returns: + nn.Sequential: the combination of the convolution block + + """ + downsample = None + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + nn.Conv2D(self.inplanes, planes * block.expansion, + kernel_size=1, stride=stride, + weight_attr=conv_weight_attr, + bias_attr=False), + nn.BatchNorm2D(planes * block.expansion), + ) + + layers = list() + layers.append(block(self.inplanes, planes, stride, downsample)) + self.inplanes = planes * block.expansion + for _ in range(1, blocks): + layers.append(block(self.inplanes, planes)) + + return nn.Sequential(*layers) + + def forward(self, x): + x = self.conv0_1(x) + x = self.bn0_1(x) + x = self.relu(x) + x = self.conv0_2(x) + x = self.bn0_2(x) + x = self.relu(x) + + x = self.maxpool1(x) + x = self.layer1(x) + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + + x = self.maxpool2(x) + x = self.layer2(x) + x = self.conv2(x) + x = self.bn2(x) + x = self.relu(x) + + x = self.maxpool3(x) + x = self.layer3(x) + x = self.conv3(x) + x = self.bn3(x) + x = self.relu(x) + + x = self.layer4(x) + x = self.conv4_1(x) + x = self.bn4_1(x) + x = self.relu(x) + x = self.conv4_2(x) + x = self.bn4_2(x) + x = self.relu(x) + return x diff --git a/ppocr/modeling/backbones/table_master_resnet.py b/ppocr/modeling/backbones/table_master_resnet.py new file mode 100644 index 0000000000000000000000000000000000000000..dacf5ed26e5374b3c93c1a983be1d7b5b4c471fc --- /dev/null +++ b/ppocr/modeling/backbones/table_master_resnet.py @@ -0,0 +1,369 @@ +# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +This code is refer from: +https://github.com/JiaquanYe/TableMASTER-mmocr/blob/master/mmocr/models/textrecog/backbones/table_resnet_extra.py +""" + +import paddle +import paddle.nn as nn +import paddle.nn.functional as F + + +class BasicBlock(nn.Layer): + expansion = 1 + + def __init__(self, + inplanes, + planes, + stride=1, + downsample=None, + gcb_config=None): + super(BasicBlock, self).__init__() + self.conv1 = nn.Conv2D( + inplanes, + planes, + kernel_size=3, + stride=stride, + padding=1, + bias_attr=False) + self.bn1 = nn.BatchNorm2D(planes, momentum=0.9) + self.relu = nn.ReLU() + self.conv2 = nn.Conv2D( + planes, planes, kernel_size=3, stride=1, padding=1, bias_attr=False) + self.bn2 = nn.BatchNorm2D(planes, momentum=0.9) + self.downsample = downsample + self.stride = stride + self.gcb_config = gcb_config + + if self.gcb_config is not None: + gcb_ratio = gcb_config['ratio'] + gcb_headers = gcb_config['headers'] + att_scale = gcb_config['att_scale'] + fusion_type = gcb_config['fusion_type'] + self.context_block = MultiAspectGCAttention( + inplanes=planes, + ratio=gcb_ratio, + headers=gcb_headers, + att_scale=att_scale, + fusion_type=fusion_type) + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + + if self.gcb_config is not None: + out = self.context_block(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + + +def get_gcb_config(gcb_config, layer): + if gcb_config is None or not gcb_config['layers'][layer]: + return None + else: + return gcb_config + + +class TableResNetExtra(nn.Layer): + def __init__(self, layers, in_channels=3, gcb_config=None): + assert len(layers) >= 4 + + super(TableResNetExtra, self).__init__() + self.inplanes = 128 + self.conv1 = nn.Conv2D( + in_channels, + 64, + kernel_size=3, + stride=1, + padding=1, + bias_attr=False) + self.bn1 = nn.BatchNorm2D(64) + self.relu1 = nn.ReLU() + + self.conv2 = nn.Conv2D( + 64, 128, kernel_size=3, stride=1, padding=1, bias_attr=False) + self.bn2 = nn.BatchNorm2D(128) + self.relu2 = nn.ReLU() + + self.maxpool1 = nn.MaxPool2D(kernel_size=2, stride=2) + + self.layer1 = self._make_layer( + BasicBlock, + 256, + layers[0], + stride=1, + gcb_config=get_gcb_config(gcb_config, 0)) + + self.conv3 = nn.Conv2D( + 256, 256, kernel_size=3, stride=1, padding=1, bias_attr=False) + self.bn3 = nn.BatchNorm2D(256) + self.relu3 = nn.ReLU() + + self.maxpool2 = nn.MaxPool2D(kernel_size=2, stride=2) + + self.layer2 = self._make_layer( + BasicBlock, + 256, + layers[1], + stride=1, + gcb_config=get_gcb_config(gcb_config, 1)) + + self.conv4 = nn.Conv2D( + 256, 256, kernel_size=3, stride=1, padding=1, bias_attr=False) + self.bn4 = nn.BatchNorm2D(256) + self.relu4 = nn.ReLU() + + self.maxpool3 = nn.MaxPool2D(kernel_size=2, stride=2) + + self.layer3 = self._make_layer( + BasicBlock, + 512, + layers[2], + stride=1, + gcb_config=get_gcb_config(gcb_config, 2)) + + self.conv5 = nn.Conv2D( + 512, 512, kernel_size=3, stride=1, padding=1, bias_attr=False) + self.bn5 = nn.BatchNorm2D(512) + self.relu5 = nn.ReLU() + + self.layer4 = self._make_layer( + BasicBlock, + 512, + layers[3], + stride=1, + gcb_config=get_gcb_config(gcb_config, 3)) + + self.conv6 = nn.Conv2D( + 512, 512, kernel_size=3, stride=1, padding=1, bias_attr=False) + self.bn6 = nn.BatchNorm2D(512) + self.relu6 = nn.ReLU() + + self.out_channels = [256, 256, 512] + + def _make_layer(self, block, planes, blocks, stride=1, gcb_config=None): + downsample = None + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + nn.Conv2D( + self.inplanes, + planes * block.expansion, + kernel_size=1, + stride=stride, + bias_attr=False), + nn.BatchNorm2D(planes * block.expansion), ) + + layers = [] + layers.append( + block( + self.inplanes, + planes, + stride, + downsample, + gcb_config=gcb_config)) + self.inplanes = planes * block.expansion + for _ in range(1, blocks): + layers.append(block(self.inplanes, planes)) + + return nn.Sequential(*layers) + + def forward(self, x): + f = [] + x = self.conv1(x) + + x = self.bn1(x) + x = self.relu1(x) + + x = self.conv2(x) + x = self.bn2(x) + x = self.relu2(x) + + x = self.maxpool1(x) + x = self.layer1(x) + + x = self.conv3(x) + x = self.bn3(x) + x = self.relu3(x) + f.append(x) + + x = self.maxpool2(x) + x = self.layer2(x) + + x = self.conv4(x) + x = self.bn4(x) + x = self.relu4(x) + f.append(x) + + x = self.maxpool3(x) + + x = self.layer3(x) + x = self.conv5(x) + x = self.bn5(x) + x = self.relu5(x) + + x = self.layer4(x) + x = self.conv6(x) + x = self.bn6(x) + x = self.relu6(x) + f.append(x) + return f + + +class MultiAspectGCAttention(nn.Layer): + def __init__(self, + inplanes, + ratio, + headers, + pooling_type='att', + att_scale=False, + fusion_type='channel_add'): + super(MultiAspectGCAttention, self).__init__() + assert pooling_type in ['avg', 'att'] + + assert fusion_type in ['channel_add', 'channel_mul', 'channel_concat'] + assert inplanes % headers == 0 and inplanes >= 8 # inplanes must be divided by headers evenly + + self.headers = headers + self.inplanes = inplanes + self.ratio = ratio + self.planes = int(inplanes * ratio) + self.pooling_type = pooling_type + self.fusion_type = fusion_type + self.att_scale = False + + self.single_header_inplanes = int(inplanes / headers) + + if pooling_type == 'att': + self.conv_mask = nn.Conv2D( + self.single_header_inplanes, 1, kernel_size=1) + self.softmax = nn.Softmax(axis=2) + else: + self.avg_pool = nn.AdaptiveAvgPool2D(1) + + if fusion_type == 'channel_add': + self.channel_add_conv = nn.Sequential( + nn.Conv2D( + self.inplanes, self.planes, kernel_size=1), + nn.LayerNorm([self.planes, 1, 1]), + nn.ReLU(), + nn.Conv2D( + self.planes, self.inplanes, kernel_size=1)) + elif fusion_type == 'channel_concat': + self.channel_concat_conv = nn.Sequential( + nn.Conv2D( + self.inplanes, self.planes, kernel_size=1), + nn.LayerNorm([self.planes, 1, 1]), + nn.ReLU(), + nn.Conv2D( + self.planes, self.inplanes, kernel_size=1)) + # for concat + self.cat_conv = nn.Conv2D( + 2 * self.inplanes, self.inplanes, kernel_size=1) + elif fusion_type == 'channel_mul': + self.channel_mul_conv = nn.Sequential( + nn.Conv2D( + self.inplanes, self.planes, kernel_size=1), + nn.LayerNorm([self.planes, 1, 1]), + nn.ReLU(), + nn.Conv2D( + self.planes, self.inplanes, kernel_size=1)) + + def spatial_pool(self, x): + batch, channel, height, width = x.shape + if self.pooling_type == 'att': + # [N*headers, C', H , W] C = headers * C' + x = x.reshape([ + batch * self.headers, self.single_header_inplanes, height, width + ]) + input_x = x + + # [N*headers, C', H * W] C = headers * C' + # input_x = input_x.view(batch, channel, height * width) + input_x = input_x.reshape([ + batch * self.headers, self.single_header_inplanes, + height * width + ]) + + # [N*headers, 1, C', H * W] + input_x = input_x.unsqueeze(1) + # [N*headers, 1, H, W] + context_mask = self.conv_mask(x) + # [N*headers, 1, H * W] + context_mask = context_mask.reshape( + [batch * self.headers, 1, height * width]) + + # scale variance + if self.att_scale and self.headers > 1: + context_mask = context_mask / paddle.sqrt( + self.single_header_inplanes) + + # [N*headers, 1, H * W] + context_mask = self.softmax(context_mask) + + # [N*headers, 1, H * W, 1] + context_mask = context_mask.unsqueeze(-1) + # [N*headers, 1, C', 1] = [N*headers, 1, C', H * W] * [N*headers, 1, H * W, 1] + context = paddle.matmul(input_x, context_mask) + + # [N, headers * C', 1, 1] + context = context.reshape( + [batch, self.headers * self.single_header_inplanes, 1, 1]) + else: + # [N, C, 1, 1] + context = self.avg_pool(x) + + return context + + def forward(self, x): + # [N, C, 1, 1] + context = self.spatial_pool(x) + + out = x + + if self.fusion_type == 'channel_mul': + # [N, C, 1, 1] + channel_mul_term = F.sigmoid(self.channel_mul_conv(context)) + out = out * channel_mul_term + elif self.fusion_type == 'channel_add': + # [N, C, 1, 1] + channel_add_term = self.channel_add_conv(context) + out = out + channel_add_term + else: + # [N, C, 1, 1] + channel_concat_term = self.channel_concat_conv(context) + + # use concat + _, C1, _, _ = channel_concat_term.shape + N, C2, H, W = out.shape + + out = paddle.concat( + [out, channel_concat_term.expand([-1, -1, H, W])], axis=1) + out = self.cat_conv(out) + out = F.layer_norm(out, [self.inplanes, H, W]) + out = F.relu(out) + + return out diff --git a/ppocr/modeling/backbones/vqa_layoutlm.py b/ppocr/modeling/backbones/vqa_layoutlm.py index ede5b7a35af65fac351277cefccd89b251f5cdb7..34dd9d10ea36758059448d96674d4d2c249d3ad0 100644 --- a/ppocr/modeling/backbones/vqa_layoutlm.py +++ b/ppocr/modeling/backbones/vqa_layoutlm.py @@ -43,9 +43,11 @@ class NLPBaseModel(nn.Layer): super(NLPBaseModel, self).__init__() if checkpoints is not None: self.model = model_class.from_pretrained(checkpoints) + elif isinstance(pretrained, (str, )) and os.path.exists(pretrained): + self.model = model_class.from_pretrained(pretrained) else: pretrained_model_name = pretrained_model_dict[base_model_class] - if pretrained: + if pretrained is True: base_model = base_model_class.from_pretrained( pretrained_model_name) else: @@ -74,9 +76,9 @@ class LayoutLMForSer(NLPBaseModel): def forward(self, x): x = self.model( input_ids=x[0], - bbox=x[2], - attention_mask=x[4], - token_type_ids=x[5], + bbox=x[1], + attention_mask=x[2], + token_type_ids=x[3], position_ids=None, output_hidden_states=False) return x @@ -96,13 +98,15 @@ class LayoutLMv2ForSer(NLPBaseModel): def forward(self, x): x = self.model( input_ids=x[0], - bbox=x[2], - image=x[3], - attention_mask=x[4], - token_type_ids=x[5], + bbox=x[1], + attention_mask=x[2], + token_type_ids=x[3], + image=x[4], position_ids=None, head_mask=None, labels=None) + if not self.training: + return x return x[0] @@ -120,13 +124,15 @@ class LayoutXLMForSer(NLPBaseModel): def forward(self, x): x = self.model( input_ids=x[0], - bbox=x[2], - image=x[3], - attention_mask=x[4], - token_type_ids=x[5], + bbox=x[1], + attention_mask=x[2], + token_type_ids=x[3], + image=x[4], position_ids=None, head_mask=None, labels=None) + if not self.training: + return x return x[0] @@ -140,12 +146,12 @@ class LayoutLMv2ForRe(NLPBaseModel): x = self.model( input_ids=x[0], bbox=x[1], - labels=None, - image=x[2], - attention_mask=x[3], - token_type_ids=x[4], + attention_mask=x[2], + token_type_ids=x[3], + image=x[4], position_ids=None, head_mask=None, + labels=None, entities=x[5], relations=x[6]) return x @@ -161,12 +167,12 @@ class LayoutXLMForRe(NLPBaseModel): x = self.model( input_ids=x[0], bbox=x[1], - labels=None, - image=x[2], - attention_mask=x[3], - token_type_ids=x[4], + attention_mask=x[2], + token_type_ids=x[3], + image=x[4], position_ids=None, head_mask=None, + labels=None, entities=x[5], relations=x[6]) return x diff --git a/ppocr/modeling/heads/__init__.py b/ppocr/modeling/heads/__init__.py index 14e6aab854d8942083f1e3466f554c0876bcf403..b4f18b372058c539ae5949ced333ec7be122211f 100755 --- a/ppocr/modeling/heads/__init__.py +++ b/ppocr/modeling/heads/__init__.py @@ -33,6 +33,7 @@ def build_head(config): from .rec_aster_head import AsterHead from .rec_pren_head import PRENHead from .rec_multi_head import MultiHead + from .rec_spin_att_head import SPINAttentionHead from .rec_abinet_head import ABINetHead # cls head @@ -42,12 +43,13 @@ def build_head(config): from .kie_sdmgr_head import SDMGRHead from .table_att_head import TableAttentionHead + from .table_master_head import TableMasterHead support_dict = [ 'DBHead', 'PSEHead', 'FCEHead', 'EASTHead', 'SASTHead', 'CTCHead', 'ClsHead', 'AttentionHead', 'SRNHead', 'PGHead', 'Transformer', 'TableAttentionHead', 'SARHead', 'AsterHead', 'SDMGRHead', 'PRENHead', - 'MultiHead', 'ABINetHead' + 'MultiHead', 'ABINetHead', 'TableMasterHead', 'SPINAttentionHead' ] #table head diff --git a/ppocr/modeling/heads/rec_abinet_head.py b/ppocr/modeling/heads/rec_abinet_head.py index a0f60f1be1727e85380eedb7d311ce9445f88b8e..2309ad65e6ebd32592df19eed0ce1fbd11cb9a81 100644 --- a/ppocr/modeling/heads/rec_abinet_head.py +++ b/ppocr/modeling/heads/rec_abinet_head.py @@ -273,7 +273,8 @@ def _get_length(logit): out = out.cast('int32') out = out.argmax(-1) out = out + 1 - out = paddle.where(abn, out, paddle.to_tensor(logit.shape[1])) + len_seq = paddle.zeros_like(out) + logit.shape[1] + out = paddle.where(abn, out, len_seq) return out diff --git a/ppocr/modeling/heads/rec_spin_att_head.py b/ppocr/modeling/heads/rec_spin_att_head.py new file mode 100644 index 0000000000000000000000000000000000000000..86e35e4339d8e1006cfe43d6cf4f2f7d231082c4 --- /dev/null +++ b/ppocr/modeling/heads/rec_spin_att_head.py @@ -0,0 +1,115 @@ +# copyright (c) 2022 PaddlePaddle Authors. All Rights Reserve. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +This code is refer from: +https://github.com/hikopensource/DAVAR-Lab-OCR/davarocr/davar_rcg/models/sequence_heads/att_head.py +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import paddle +import paddle.nn as nn +import paddle.nn.functional as F + + +class SPINAttentionHead(nn.Layer): + def __init__(self, in_channels, out_channels, hidden_size, **kwargs): + super(SPINAttentionHead, self).__init__() + self.input_size = in_channels + self.hidden_size = hidden_size + self.num_classes = out_channels + + self.attention_cell = AttentionLSTMCell( + in_channels, hidden_size, out_channels, use_gru=False) + self.generator = nn.Linear(hidden_size, out_channels) + + def _char_to_onehot(self, input_char, onehot_dim): + input_ont_hot = F.one_hot(input_char, onehot_dim) + return input_ont_hot + + def forward(self, inputs, targets=None, batch_max_length=25): + batch_size = paddle.shape(inputs)[0] + num_steps = batch_max_length + 1 # +1 for [sos] at end of sentence + + hidden = (paddle.zeros((batch_size, self.hidden_size)), + paddle.zeros((batch_size, self.hidden_size))) + output_hiddens = [] + if self.training: # for train + targets = targets[0] + for i in range(num_steps): + char_onehots = self._char_to_onehot( + targets[:, i], onehot_dim=self.num_classes) + (outputs, hidden), alpha = self.attention_cell(hidden, inputs, + char_onehots) + 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 + char_onehots = None + outputs = None + alpha = None + + for i in range(num_steps): + char_onehots = self._char_to_onehot( + targets, onehot_dim=self.num_classes) + (outputs, hidden), alpha = self.attention_cell(hidden, inputs, + char_onehots) + probs_step = self.generator(outputs) + if probs is None: + probs = paddle.unsqueeze(probs_step, axis=1) + else: + probs = paddle.concat( + [probs, paddle.unsqueeze( + 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 + + +class AttentionLSTMCell(nn.Layer): + def __init__(self, input_size, hidden_size, num_embeddings, use_gru=False): + super(AttentionLSTMCell, self).__init__() + self.i2h = nn.Linear(input_size, hidden_size, bias_attr=False) + self.h2h = nn.Linear(hidden_size, hidden_size) + self.score = nn.Linear(hidden_size, 1, bias_attr=False) + if not use_gru: + self.rnn = nn.LSTMCell( + input_size=input_size + num_embeddings, hidden_size=hidden_size) + else: + self.rnn = nn.GRUCell( + input_size=input_size + num_embeddings, hidden_size=hidden_size) + + self.hidden_size = hidden_size + + def forward(self, prev_hidden, batch_H, char_onehots): + batch_H_proj = self.i2h(batch_H) + prev_hidden_proj = paddle.unsqueeze(self.h2h(prev_hidden[0]), axis=1) + res = paddle.add(batch_H_proj, prev_hidden_proj) + res = paddle.tanh(res) + e = self.score(res) + + alpha = F.softmax(e, axis=1) + alpha = paddle.transpose(alpha, [0, 2, 1]) + context = paddle.squeeze(paddle.mm(alpha, batch_H), axis=1) + concat_context = paddle.concat([context, char_onehots], 1) + cur_hidden = self.rnn(concat_context, prev_hidden) + + return cur_hidden, alpha diff --git a/ppocr/modeling/heads/table_att_head.py b/ppocr/modeling/heads/table_att_head.py index e354f40d6518c1f7ca22e93694b1c6668fc003d2..4f39d6253d8d596fecdc4736666a6d3106601a82 100644 --- a/ppocr/modeling/heads/table_att_head.py +++ b/ppocr/modeling/heads/table_att_head.py @@ -21,6 +21,8 @@ import paddle.nn as nn import paddle.nn.functional as F import numpy as np +from .rec_att_head import AttentionGRUCell + class TableAttentionHead(nn.Layer): def __init__(self, @@ -28,21 +30,19 @@ class TableAttentionHead(nn.Layer): hidden_size, loc_type, in_max_len=488, - max_text_length=100, - max_elem_length=800, - max_cell_num=500, + max_text_length=800, + out_channels=30, + point_num=2, **kwargs): super(TableAttentionHead, self).__init__() self.input_size = in_channels[-1] self.hidden_size = hidden_size - self.elem_num = 30 + self.out_channels = out_channels self.max_text_length = max_text_length - self.max_elem_length = max_elem_length - self.max_cell_num = max_cell_num self.structure_attention_cell = AttentionGRUCell( - self.input_size, hidden_size, self.elem_num, use_gru=False) - self.structure_generator = nn.Linear(hidden_size, self.elem_num) + self.input_size, hidden_size, self.out_channels, use_gru=False) + self.structure_generator = nn.Linear(hidden_size, self.out_channels) self.loc_type = loc_type self.in_max_len = in_max_len @@ -50,12 +50,13 @@ class TableAttentionHead(nn.Layer): self.loc_generator = nn.Linear(hidden_size, 4) else: if self.in_max_len == 640: - self.loc_fea_trans = nn.Linear(400, self.max_elem_length + 1) + self.loc_fea_trans = nn.Linear(400, self.max_text_length + 1) elif self.in_max_len == 800: - self.loc_fea_trans = nn.Linear(625, self.max_elem_length + 1) + self.loc_fea_trans = nn.Linear(625, self.max_text_length + 1) else: - self.loc_fea_trans = nn.Linear(256, self.max_elem_length + 1) - self.loc_generator = nn.Linear(self.input_size + hidden_size, 4) + self.loc_fea_trans = nn.Linear(256, self.max_text_length + 1) + self.loc_generator = nn.Linear(self.input_size + hidden_size, + point_num * 2) def _char_to_onehot(self, input_char, onehot_dim): input_ont_hot = F.one_hot(input_char, onehot_dim) @@ -77,9 +78,9 @@ class TableAttentionHead(nn.Layer): output_hiddens = [] if self.training and targets is not None: structure = targets[0] - for i in range(self.max_elem_length + 1): + for i in range(self.max_text_length + 1): elem_onehots = self._char_to_onehot( - structure[:, i], onehot_dim=self.elem_num) + structure[:, i], onehot_dim=self.out_channels) (outputs, hidden), alpha = self.structure_attention_cell( hidden, fea, elem_onehots) output_hiddens.append(paddle.unsqueeze(outputs, axis=1)) @@ -102,11 +103,11 @@ class TableAttentionHead(nn.Layer): elem_onehots = None outputs = None alpha = None - max_elem_length = paddle.to_tensor(self.max_elem_length) + max_text_length = paddle.to_tensor(self.max_text_length) i = 0 - while i < max_elem_length + 1: + while i < max_text_length + 1: elem_onehots = self._char_to_onehot( - temp_elem, onehot_dim=self.elem_num) + temp_elem, onehot_dim=self.out_channels) (outputs, hidden), alpha = self.structure_attention_cell( hidden, fea, elem_onehots) output_hiddens.append(paddle.unsqueeze(outputs, axis=1)) @@ -128,119 +129,3 @@ class TableAttentionHead(nn.Layer): loc_preds = self.loc_generator(loc_concat) loc_preds = F.sigmoid(loc_preds) return {'structure_probs': structure_probs, 'loc_preds': loc_preds} - - -class AttentionGRUCell(nn.Layer): - def __init__(self, input_size, hidden_size, num_embeddings, use_gru=False): - super(AttentionGRUCell, self).__init__() - self.i2h = nn.Linear(input_size, hidden_size, bias_attr=False) - self.h2h = nn.Linear(hidden_size, hidden_size) - self.score = nn.Linear(hidden_size, 1, bias_attr=False) - self.rnn = nn.GRUCell( - input_size=input_size + num_embeddings, hidden_size=hidden_size) - self.hidden_size = hidden_size - - def forward(self, prev_hidden, batch_H, char_onehots): - batch_H_proj = self.i2h(batch_H) - prev_hidden_proj = paddle.unsqueeze(self.h2h(prev_hidden), axis=1) - res = paddle.add(batch_H_proj, prev_hidden_proj) - res = paddle.tanh(res) - e = self.score(res) - alpha = F.softmax(e, axis=1) - alpha = paddle.transpose(alpha, [0, 2, 1]) - context = paddle.squeeze(paddle.mm(alpha, batch_H), axis=1) - concat_context = paddle.concat([context, char_onehots], 1) - cur_hidden = self.rnn(concat_context, prev_hidden) - return cur_hidden, alpha - - -class AttentionLSTM(nn.Layer): - def __init__(self, in_channels, out_channels, hidden_size, **kwargs): - super(AttentionLSTM, self).__init__() - self.input_size = in_channels - self.hidden_size = hidden_size - self.num_classes = out_channels - - self.attention_cell = AttentionLSTMCell( - in_channels, hidden_size, out_channels, use_gru=False) - self.generator = nn.Linear(hidden_size, out_channels) - - def _char_to_onehot(self, input_char, onehot_dim): - input_ont_hot = F.one_hot(input_char, onehot_dim) - return input_ont_hot - - def forward(self, inputs, targets=None, batch_max_length=25): - batch_size = inputs.shape[0] - num_steps = batch_max_length - - hidden = (paddle.zeros((batch_size, self.hidden_size)), paddle.zeros( - (batch_size, self.hidden_size))) - output_hiddens = [] - - if targets is not None: - for i in range(num_steps): - # one-hot vectors for a i-th char - char_onehots = self._char_to_onehot( - targets[:, i], onehot_dim=self.num_classes) - hidden, alpha = self.attention_cell(hidden, inputs, - char_onehots) - - hidden = (hidden[1][0], hidden[1][1]) - output_hiddens.append(paddle.unsqueeze(hidden[0], axis=1)) - output = paddle.concat(output_hiddens, axis=1) - probs = self.generator(output) - - else: - targets = paddle.zeros(shape=[batch_size], dtype="int32") - probs = None - - for i in range(num_steps): - char_onehots = self._char_to_onehot( - targets, onehot_dim=self.num_classes) - hidden, alpha = self.attention_cell(hidden, inputs, - char_onehots) - probs_step = self.generator(hidden[0]) - hidden = (hidden[1][0], hidden[1][1]) - if probs is None: - probs = paddle.unsqueeze(probs_step, axis=1) - else: - probs = paddle.concat( - [probs, paddle.unsqueeze( - probs_step, axis=1)], axis=1) - - next_input = probs_step.argmax(axis=1) - - targets = next_input - - return probs - - -class AttentionLSTMCell(nn.Layer): - def __init__(self, input_size, hidden_size, num_embeddings, use_gru=False): - super(AttentionLSTMCell, self).__init__() - self.i2h = nn.Linear(input_size, hidden_size, bias_attr=False) - self.h2h = nn.Linear(hidden_size, hidden_size) - self.score = nn.Linear(hidden_size, 1, bias_attr=False) - if not use_gru: - self.rnn = nn.LSTMCell( - input_size=input_size + num_embeddings, hidden_size=hidden_size) - else: - self.rnn = nn.GRUCell( - input_size=input_size + num_embeddings, hidden_size=hidden_size) - - self.hidden_size = hidden_size - - def forward(self, prev_hidden, batch_H, char_onehots): - batch_H_proj = self.i2h(batch_H) - prev_hidden_proj = paddle.unsqueeze(self.h2h(prev_hidden[0]), axis=1) - res = paddle.add(batch_H_proj, prev_hidden_proj) - res = paddle.tanh(res) - e = self.score(res) - - alpha = F.softmax(e, axis=1) - alpha = paddle.transpose(alpha, [0, 2, 1]) - context = paddle.squeeze(paddle.mm(alpha, batch_H), axis=1) - concat_context = paddle.concat([context, char_onehots], 1) - cur_hidden = self.rnn(concat_context, prev_hidden) - - return cur_hidden, alpha diff --git a/ppocr/modeling/heads/table_master_head.py b/ppocr/modeling/heads/table_master_head.py new file mode 100644 index 0000000000000000000000000000000000000000..fddbcc63fcd6d5380f9fdd96f9ca85756d666442 --- /dev/null +++ b/ppocr/modeling/heads/table_master_head.py @@ -0,0 +1,281 @@ +# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +This code is refer from: +https://github.com/JiaquanYe/TableMASTER-mmocr/blob/master/mmocr/models/textrecog/decoders/master_decoder.py +""" + +import copy +import math +import paddle +from paddle import nn +from paddle.nn import functional as F + + +class TableMasterHead(nn.Layer): + """ + Split to two transformer header at the last layer. + Cls_layer is used to structure token classification. + Bbox_layer is used to regress bbox coord. + """ + + def __init__(self, + in_channels, + out_channels=30, + headers=8, + d_ff=2048, + dropout=0, + max_text_length=500, + point_num=2, + **kwargs): + super(TableMasterHead, self).__init__() + hidden_size = in_channels[-1] + self.layers = clones( + DecoderLayer(headers, hidden_size, dropout, d_ff), 2) + self.cls_layer = clones( + DecoderLayer(headers, hidden_size, dropout, d_ff), 1) + self.bbox_layer = clones( + DecoderLayer(headers, hidden_size, dropout, d_ff), 1) + self.cls_fc = nn.Linear(hidden_size, out_channels) + self.bbox_fc = nn.Sequential( + # nn.Linear(hidden_size, hidden_size), + nn.Linear(hidden_size, point_num * 2), + nn.Sigmoid()) + self.norm = nn.LayerNorm(hidden_size) + self.embedding = Embeddings(d_model=hidden_size, vocab=out_channels) + self.positional_encoding = PositionalEncoding(d_model=hidden_size) + + self.SOS = out_channels - 3 + self.PAD = out_channels - 1 + self.out_channels = out_channels + self.point_num = point_num + self.max_text_length = max_text_length + + def make_mask(self, tgt): + """ + Make mask for self attention. + :param src: [b, c, h, l_src] + :param tgt: [b, l_tgt] + :return: + """ + trg_pad_mask = (tgt != self.PAD).unsqueeze(1).unsqueeze(3) + + tgt_len = paddle.shape(tgt)[1] + trg_sub_mask = paddle.tril( + paddle.ones( + ([tgt_len, tgt_len]), dtype=paddle.float32)) + + tgt_mask = paddle.logical_and( + trg_pad_mask.astype(paddle.float32), trg_sub_mask) + return tgt_mask.astype(paddle.float32) + + def decode(self, input, feature, src_mask, tgt_mask): + # main process of transformer decoder. + x = self.embedding(input) # x: 1*x*512, feature: 1*3600,512 + x = self.positional_encoding(x) + + # origin transformer layers + for i, layer in enumerate(self.layers): + x = layer(x, feature, src_mask, tgt_mask) + + # cls head + for layer in self.cls_layer: + cls_x = layer(x, feature, src_mask, tgt_mask) + cls_x = self.norm(cls_x) + + # bbox head + for layer in self.bbox_layer: + bbox_x = layer(x, feature, src_mask, tgt_mask) + bbox_x = self.norm(bbox_x) + return self.cls_fc(cls_x), self.bbox_fc(bbox_x) + + def greedy_forward(self, SOS, feature): + input = SOS + output = paddle.zeros( + [input.shape[0], self.max_text_length + 1, self.out_channels]) + bbox_output = paddle.zeros( + [input.shape[0], self.max_text_length + 1, self.point_num * 2]) + max_text_length = paddle.to_tensor(self.max_text_length) + for i in range(max_text_length + 1): + target_mask = self.make_mask(input) + out_step, bbox_output_step = self.decode(input, feature, None, + target_mask) + prob = F.softmax(out_step, axis=-1) + next_word = prob.argmax(axis=2, dtype="int64") + input = paddle.concat( + [input, next_word[:, -1].unsqueeze(-1)], axis=1) + if i == self.max_text_length: + output = out_step + bbox_output = bbox_output_step + return output, bbox_output + + def forward_train(self, out_enc, targets): + # x is token of label + # feat is feature after backbone before pe. + # out_enc is feature after pe. + padded_targets = targets[0] + src_mask = None + tgt_mask = self.make_mask(padded_targets[:, :-1]) + output, bbox_output = self.decode(padded_targets[:, :-1], out_enc, + src_mask, tgt_mask) + return {'structure_probs': output, 'loc_preds': bbox_output} + + def forward_test(self, out_enc): + batch_size = out_enc.shape[0] + SOS = paddle.zeros([batch_size, 1], dtype='int64') + self.SOS + output, bbox_output = self.greedy_forward(SOS, out_enc) + output = F.softmax(output) + return {'structure_probs': output, 'loc_preds': bbox_output} + + def forward(self, feat, targets=None): + feat = feat[-1] + b, c, h, w = feat.shape + feat = feat.reshape([b, c, h * w]) # flatten 2D feature map + feat = feat.transpose((0, 2, 1)) + out_enc = self.positional_encoding(feat) + if self.training: + return self.forward_train(out_enc, targets) + + return self.forward_test(out_enc) + + +class DecoderLayer(nn.Layer): + """ + Decoder is made of self attention, srouce attention and feed forward. + """ + + def __init__(self, headers, d_model, dropout, d_ff): + super(DecoderLayer, self).__init__() + self.self_attn = MultiHeadAttention(headers, d_model, dropout) + self.src_attn = MultiHeadAttention(headers, d_model, dropout) + self.feed_forward = FeedForward(d_model, d_ff, dropout) + self.sublayer = clones(SubLayerConnection(d_model, dropout), 3) + + def forward(self, x, feature, src_mask, tgt_mask): + x = self.sublayer[0](x, lambda x: self.self_attn(x, x, x, tgt_mask)) + x = self.sublayer[1]( + x, lambda x: self.src_attn(x, feature, feature, src_mask)) + return self.sublayer[2](x, self.feed_forward) + + +class MultiHeadAttention(nn.Layer): + def __init__(self, headers, d_model, dropout): + super(MultiHeadAttention, self).__init__() + + assert d_model % headers == 0 + self.d_k = int(d_model / headers) + self.headers = headers + self.linears = clones(nn.Linear(d_model, d_model), 4) + self.attn = None + self.dropout = nn.Dropout(dropout) + + def forward(self, query, key, value, mask=None): + B = query.shape[0] + + # 1) Do all the linear projections in batch from d_model => h x d_k + query, key, value = \ + [l(x).reshape([B, 0, self.headers, self.d_k]).transpose([0, 2, 1, 3]) + for l, x in zip(self.linears, (query, key, value))] + # 2) Apply attention on all the projected vectors in batch + x, self.attn = self_attention( + query, key, value, mask=mask, dropout=self.dropout) + x = x.transpose([0, 2, 1, 3]).reshape([B, 0, self.headers * self.d_k]) + return self.linears[-1](x) + + +class FeedForward(nn.Layer): + def __init__(self, d_model, d_ff, dropout): + super(FeedForward, self).__init__() + self.w_1 = nn.Linear(d_model, d_ff) + self.w_2 = nn.Linear(d_ff, d_model) + self.dropout = nn.Dropout(dropout) + + def forward(self, x): + return self.w_2(self.dropout(F.relu(self.w_1(x)))) + + +class SubLayerConnection(nn.Layer): + """ + A residual connection followed by a layer norm. + Note for code simplicity the norm is first as opposed to last. + """ + + def __init__(self, size, dropout): + super(SubLayerConnection, self).__init__() + self.norm = nn.LayerNorm(size) + self.dropout = nn.Dropout(dropout) + + def forward(self, x, sublayer): + return x + self.dropout(sublayer(self.norm(x))) + + +def masked_fill(x, mask, value): + mask = mask.astype(x.dtype) + return x * paddle.logical_not(mask).astype(x.dtype) + mask * value + + +def self_attention(query, key, value, mask=None, dropout=None): + """ + Compute 'Scale Dot Product Attention' + """ + d_k = value.shape[-1] + + score = paddle.matmul(query, key.transpose([0, 1, 3, 2]) / math.sqrt(d_k)) + if mask is not None: + # score = score.masked_fill(mask == 0, -1e9) # b, h, L, L + score = masked_fill(score, mask == 0, -6.55e4) # for fp16 + + p_attn = F.softmax(score, axis=-1) + + if dropout is not None: + p_attn = dropout(p_attn) + return paddle.matmul(p_attn, value), p_attn + + +def clones(module, N): + """ Produce N identical layers """ + return nn.LayerList([copy.deepcopy(module) for _ in range(N)]) + + +class Embeddings(nn.Layer): + def __init__(self, d_model, vocab): + super(Embeddings, self).__init__() + self.lut = nn.Embedding(vocab, d_model) + self.d_model = d_model + + def forward(self, *input): + x = input[0] + return self.lut(x) * math.sqrt(self.d_model) + + +class PositionalEncoding(nn.Layer): + """ Implement the PE function. """ + + def __init__(self, d_model, dropout=0., max_len=5000): + super(PositionalEncoding, self).__init__() + self.dropout = nn.Dropout(p=dropout) + + # Compute the positional encodings once in log space. + pe = paddle.zeros([max_len, d_model]) + position = paddle.arange(0, max_len).unsqueeze(1).astype('float32') + div_term = paddle.exp( + paddle.arange(0, d_model, 2) * -math.log(10000.0) / d_model) + pe[:, 0::2] = paddle.sin(position * div_term) + pe[:, 1::2] = paddle.cos(position * div_term) + pe = pe.unsqueeze(0) + self.register_buffer('pe', pe) + + def forward(self, feat, **kwargs): + feat = feat + self.pe[:, :paddle.shape(feat)[1]] # pe 1*5000*512 + return self.dropout(feat) diff --git a/ppocr/modeling/necks/db_fpn.py b/ppocr/modeling/necks/db_fpn.py index 93ed2dbfd1fac9bf2d163c54d23a20e16b537981..8c3f52a331db5daafab2a38c0a441edd44eb141d 100644 --- a/ppocr/modeling/necks/db_fpn.py +++ b/ppocr/modeling/necks/db_fpn.py @@ -105,9 +105,10 @@ class DSConv(nn.Layer): class DBFPN(nn.Layer): - def __init__(self, in_channels, out_channels, **kwargs): + def __init__(self, in_channels, out_channels, use_asf=False, **kwargs): super(DBFPN, self).__init__() self.out_channels = out_channels + self.use_asf = use_asf weight_attr = paddle.nn.initializer.KaimingUniform() self.in2_conv = nn.Conv2D( @@ -163,6 +164,9 @@ class DBFPN(nn.Layer): weight_attr=ParamAttr(initializer=weight_attr), bias_attr=False) + if self.use_asf is True: + self.asf = ASFBlock(self.out_channels, self.out_channels // 4) + def forward(self, x): c2, c3, c4, c5 = x @@ -187,6 +191,10 @@ class DBFPN(nn.Layer): p3 = F.upsample(p3, scale_factor=2, mode="nearest", align_mode=1) fuse = paddle.concat([p5, p4, p3, p2], axis=1) + + if self.use_asf is True: + fuse = self.asf(fuse, [p5, p4, p3, p2]) + return fuse @@ -356,3 +364,64 @@ class LKPAN(nn.Layer): fuse = paddle.concat([p5, p4, p3, p2], axis=1) return fuse + + +class ASFBlock(nn.Layer): + """ + This code is refered from: + https://github.com/MhLiao/DB/blob/master/decoders/feature_attention.py + """ + + def __init__(self, in_channels, inter_channels, out_features_num=4): + """ + Adaptive Scale Fusion (ASF) block of DBNet++ + Args: + in_channels: the number of channels in the input data + inter_channels: the number of middle channels + out_features_num: the number of fused stages + """ + super(ASFBlock, self).__init__() + weight_attr = paddle.nn.initializer.KaimingUniform() + self.in_channels = in_channels + self.inter_channels = inter_channels + self.out_features_num = out_features_num + self.conv = nn.Conv2D(in_channels, inter_channels, 3, padding=1) + + self.spatial_scale = nn.Sequential( + #Nx1xHxW + nn.Conv2D( + in_channels=1, + out_channels=1, + kernel_size=3, + bias_attr=False, + padding=1, + weight_attr=ParamAttr(initializer=weight_attr)), + nn.ReLU(), + nn.Conv2D( + in_channels=1, + out_channels=1, + kernel_size=1, + bias_attr=False, + weight_attr=ParamAttr(initializer=weight_attr)), + nn.Sigmoid()) + + self.channel_scale = nn.Sequential( + nn.Conv2D( + in_channels=inter_channels, + out_channels=out_features_num, + kernel_size=1, + bias_attr=False, + weight_attr=ParamAttr(initializer=weight_attr)), + nn.Sigmoid()) + + def forward(self, fuse_features, features_list): + fuse_features = self.conv(fuse_features) + spatial_x = paddle.mean(fuse_features, axis=1, keepdim=True) + attention_scores = self.spatial_scale(spatial_x) + fuse_features + attention_scores = self.channel_scale(attention_scores) + assert len(features_list) == self.out_features_num + + out_list = [] + for i in range(self.out_features_num): + out_list.append(attention_scores[:, i:i + 1] * features_list[i]) + return paddle.concat(out_list, axis=1) diff --git a/ppocr/modeling/necks/rnn.py b/ppocr/modeling/necks/rnn.py index c8a774b8c543b9ccc14223c52f1b79ce690592f6..33be9400b34cb535d260881748e179c3df106caa 100644 --- a/ppocr/modeling/necks/rnn.py +++ b/ppocr/modeling/necks/rnn.py @@ -47,6 +47,56 @@ class EncoderWithRNN(nn.Layer): x, _ = self.lstm(x) return x +class BidirectionalLSTM(nn.Layer): + def __init__(self, input_size, + hidden_size, + output_size=None, + num_layers=1, + dropout=0, + direction=False, + time_major=False, + with_linear=False): + super(BidirectionalLSTM, self).__init__() + self.with_linear = with_linear + self.rnn = nn.LSTM(input_size, + hidden_size, + num_layers=num_layers, + dropout=dropout, + direction=direction, + time_major=time_major) + + # text recognition the specified structure LSTM with linear + if self.with_linear: + self.linear = nn.Linear(hidden_size * 2, output_size) + + def forward(self, input_feature): + recurrent, _ = self.rnn(input_feature) # batch_size x T x input_size -> batch_size x T x (2*hidden_size) + if self.with_linear: + output = self.linear(recurrent) # batch_size x T x output_size + return output + return recurrent + +class EncoderWithCascadeRNN(nn.Layer): + def __init__(self, in_channels, hidden_size, out_channels, num_layers=2, with_linear=False): + super(EncoderWithCascadeRNN, self).__init__() + self.out_channels = out_channels[-1] + self.encoder = nn.LayerList( + [BidirectionalLSTM( + in_channels if i == 0 else out_channels[i - 1], + hidden_size, + output_size=out_channels[i], + num_layers=1, + direction='bidirectional', + with_linear=with_linear) + for i in range(num_layers)] + ) + + + def forward(self, x): + for i, l in enumerate(self.encoder): + x = l(x) + return x + class EncoderWithFC(nn.Layer): def __init__(self, in_channels, hidden_size): @@ -166,13 +216,17 @@ class SequenceEncoder(nn.Layer): 'reshape': Im2Seq, 'fc': EncoderWithFC, 'rnn': EncoderWithRNN, - 'svtr': EncoderWithSVTR + 'svtr': EncoderWithSVTR, + 'cascadernn': EncoderWithCascadeRNN } assert encoder_type in support_encoder_dict, '{} must in {}'.format( encoder_type, support_encoder_dict.keys()) if encoder_type == "svtr": self.encoder = support_encoder_dict[encoder_type]( self.encoder_reshape.out_channels, **kwargs) + elif encoder_type == 'cascadernn': + self.encoder = support_encoder_dict[encoder_type]( + self.encoder_reshape.out_channels, hidden_size, **kwargs) else: self.encoder = support_encoder_dict[encoder_type]( self.encoder_reshape.out_channels, hidden_size) diff --git a/ppocr/modeling/transforms/__init__.py b/ppocr/modeling/transforms/__init__.py index 405ab3cc6c0380654f61e42e523ddc85839139b3..7e4ffdf46854416f71e1c8f4e131d1f0283bb725 100755 --- a/ppocr/modeling/transforms/__init__.py +++ b/ppocr/modeling/transforms/__init__.py @@ -18,8 +18,10 @@ __all__ = ['build_transform'] def build_transform(config): from .tps import TPS from .stn import STN_ON + from .gaspin_transformer import GA_SPIN_Transformer as GA_SPIN - support_dict = ['TPS', 'STN_ON'] + + support_dict = ['TPS', 'STN_ON', 'GA_SPIN'] module_name = config.pop('name') assert module_name in support_dict, Exception( diff --git a/ppocr/modeling/transforms/gaspin_transformer.py b/ppocr/modeling/transforms/gaspin_transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..f4719eb2162a02141620586bcb6a849ae16f3b62 --- /dev/null +++ b/ppocr/modeling/transforms/gaspin_transformer.py @@ -0,0 +1,284 @@ +# copyright (c) 2022 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 math +import paddle +from paddle import nn, ParamAttr +from paddle.nn import functional as F +import numpy as np +import functools +from .tps import GridGenerator + +'''This code is refer from: +https://github.com/hikopensource/DAVAR-Lab-OCR/davarocr/davar_rcg/models/transformations/gaspin_transformation.py +''' + +class SP_TransformerNetwork(nn.Layer): + """ + Sturture-Preserving Transformation (SPT) as Equa. (2) in Ref. [1] + Ref: [1] SPIN: Structure-Preserving Inner Offset Network for Scene Text Recognition. AAAI-2021. + """ + + def __init__(self, nc=1, default_type=5): + """ Based on SPIN + Args: + nc (int): number of input channels (usually in 1 or 3) + default_type (int): the complexity of transformation intensities (by default set to 6 as the paper) + """ + super(SP_TransformerNetwork, self).__init__() + self.power_list = self.cal_K(default_type) + self.sigmoid = nn.Sigmoid() + self.bn = nn.InstanceNorm2D(nc) + + def cal_K(self, k=5): + """ + + Args: + k (int): the complexity of transformation intensities (by default set to 6 as the paper) + + Returns: + List: the normalized intensity of each pixel in [0,1], denoted as \beta [1x(2K+1)] + + """ + from math import log + x = [] + if k != 0: + for i in range(1, k+1): + lower = round(log(1-(0.5/(k+1))*i)/log((0.5/(k+1))*i), 2) + upper = round(1/lower, 2) + x.append(lower) + x.append(upper) + x.append(1.00) + return x + + def forward(self, batch_I, weights, offsets, lambda_color=None): + """ + + Args: + batch_I (Tensor): batch of input images [batch_size x nc x I_height x I_width] + weights: + offsets: the predicted offset by AIN, a scalar + lambda_color: the learnable update gate \alpha in Equa. (5) as + g(x) = (1 - \alpha) \odot x + \alpha \odot x_{offsets} + + Returns: + Tensor: transformed images by SPN as Equa. (4) in Ref. [1] + [batch_size x I_channel_num x I_r_height x I_r_width] + + """ + batch_I = (batch_I + 1) * 0.5 + if offsets is not None: + batch_I = batch_I*(1-lambda_color) + offsets*lambda_color + batch_weight_params = paddle.unsqueeze(paddle.unsqueeze(weights, -1), -1) + batch_I_power = paddle.stack([batch_I.pow(p) for p in self.power_list], axis=1) + + batch_weight_sum = paddle.sum(batch_I_power * batch_weight_params, axis=1) + batch_weight_sum = self.bn(batch_weight_sum) + batch_weight_sum = self.sigmoid(batch_weight_sum) + batch_weight_sum = batch_weight_sum * 2 - 1 + return batch_weight_sum + +class GA_SPIN_Transformer(nn.Layer): + """ + Geometric-Absorbed SPIN Transformation (GA-SPIN) proposed in Ref. [1] + + + Ref: [1] SPIN: Structure-Preserving Inner Offset Network for Scene Text Recognition. AAAI-2021. + """ + + def __init__(self, in_channels=1, + I_r_size=(32, 100), + offsets=False, + norm_type='BN', + default_type=6, + loc_lr=1, + stn=True): + """ + Args: + in_channels (int): channel of input features, + set it to 1 if the grayscale images and 3 if RGB input + I_r_size (tuple): size of rectified images (used in STN transformations) + offsets (bool): set it to False if use SPN w.o. AIN, + and set it to True if use SPIN (both with SPN and AIN) + norm_type (str): the normalization type of the module, + set it to 'BN' by default, 'IN' optionally + default_type (int): the K chromatic space, + set it to 3/5/6 depend on the complexity of transformation intensities + loc_lr (float): learning rate of location network + stn (bool): whther to use stn. + + """ + super(GA_SPIN_Transformer, self).__init__() + self.nc = in_channels + self.spt = True + self.offsets = offsets + self.stn = stn # set to True in GA-SPIN, while set it to False in SPIN + self.I_r_size = I_r_size + self.out_channels = in_channels + if norm_type == 'BN': + norm_layer = functools.partial(nn.BatchNorm2D, use_global_stats=True) + elif norm_type == 'IN': + norm_layer = functools.partial(nn.InstanceNorm2D, weight_attr=False, + use_global_stats=False) + else: + raise NotImplementedError('normalization layer [%s] is not found' % norm_type) + + if self.spt: + self.sp_net = SP_TransformerNetwork(in_channels, + default_type) + self.spt_convnet = nn.Sequential( + # 32*100 + nn.Conv2D(in_channels, 32, 3, 1, 1, bias_attr=False), + norm_layer(32), nn.ReLU(), + nn.MaxPool2D(kernel_size=2, stride=2), + # 16*50 + nn.Conv2D(32, 64, 3, 1, 1, bias_attr=False), + norm_layer(64), nn.ReLU(), + nn.MaxPool2D(kernel_size=2, stride=2), + # 8*25 + nn.Conv2D(64, 128, 3, 1, 1, bias_attr=False), + norm_layer(128), nn.ReLU(), + nn.MaxPool2D(kernel_size=2, stride=2), + # 4*12 + ) + self.stucture_fc1 = nn.Sequential( + nn.Conv2D(128, 256, 3, 1, 1, bias_attr=False), + norm_layer(256), nn.ReLU(), + nn.MaxPool2D(kernel_size=2, stride=2), + nn.Conv2D(256, 256, 3, 1, 1, bias_attr=False), + norm_layer(256), nn.ReLU(), # 2*6 + nn.MaxPool2D(kernel_size=2, stride=2), + nn.Conv2D(256, 512, 3, 1, 1, bias_attr=False), + norm_layer(512), nn.ReLU(), # 1*3 + nn.AdaptiveAvgPool2D(1), + nn.Flatten(1, -1), # batch_size x 512 + nn.Linear(512, 256, weight_attr=nn.initializer.Normal(0.001)), + nn.BatchNorm1D(256), nn.ReLU() + ) + self.out_weight = 2*default_type+1 + self.spt_length = 2*default_type+1 + if offsets: + self.out_weight += 1 + if self.stn: + self.F = 20 + self.out_weight += self.F * 2 + self.GridGenerator = GridGenerator(self.F*2, self.F) + + # self.out_weight*=nc + # Init structure_fc2 in LocalizationNetwork + initial_bias = self.init_spin(default_type*2) + initial_bias = initial_bias.reshape(-1) + param_attr = ParamAttr( + learning_rate=loc_lr, + initializer=nn.initializer.Assign(np.zeros([256, self.out_weight]))) + bias_attr = ParamAttr( + learning_rate=loc_lr, + initializer=nn.initializer.Assign(initial_bias)) + self.stucture_fc2 = nn.Linear(256, self.out_weight, + weight_attr=param_attr, + bias_attr=bias_attr) + self.sigmoid = nn.Sigmoid() + + if offsets: + self.offset_fc1 = nn.Sequential(nn.Conv2D(128, 16, + 3, 1, 1, + bias_attr=False), + norm_layer(16), + nn.ReLU(),) + self.offset_fc2 = nn.Conv2D(16, in_channels, + 3, 1, 1) + self.pool = nn.MaxPool2D(2, 2) + + def init_spin(self, nz): + """ + Args: + nz (int): number of paired \betas exponents, which means the value of K x 2 + + """ + init_id = [0.00]*nz+[5.00] + if self.offsets: + init_id += [-5.00] + # init_id *=3 + init = np.array(init_id) + + if self.stn: + F = self.F + ctrl_pts_x = np.linspace(-1.0, 1.0, int(F / 2)) + ctrl_pts_y_top = np.linspace(0.0, -1.0, num=int(F / 2)) + ctrl_pts_y_bottom = np.linspace(1.0, 0.0, num=int(F / 2)) + ctrl_pts_top = np.stack([ctrl_pts_x, ctrl_pts_y_top], axis=1) + ctrl_pts_bottom = np.stack([ctrl_pts_x, ctrl_pts_y_bottom], axis=1) + initial_bias = np.concatenate([ctrl_pts_top, ctrl_pts_bottom], axis=0) + initial_bias = initial_bias.reshape(-1) + init = np.concatenate([init, initial_bias], axis=0) + return init + + def forward(self, x, return_weight=False): + """ + Args: + x (Tensor): input image batch + return_weight (bool): set to False by default, + if set to True return the predicted offsets of AIN, denoted as x_{offsets} + + Returns: + Tensor: rectified image [batch_size x I_channel_num x I_height x I_width], the same as the input size + """ + + if self.spt: + feat = self.spt_convnet(x) + fc1 = self.stucture_fc1(feat) + sp_weight_fusion = self.stucture_fc2(fc1) + sp_weight_fusion = sp_weight_fusion.reshape([x.shape[0], self.out_weight, 1]) + if self.offsets: # SPIN w. AIN + lambda_color = sp_weight_fusion[:, self.spt_length, 0] + lambda_color = self.sigmoid(lambda_color).unsqueeze(-1).unsqueeze(-1).unsqueeze(-1) + sp_weight = sp_weight_fusion[:, :self.spt_length, :] + offsets = self.pool(self.offset_fc2(self.offset_fc1(feat))) + + assert offsets.shape[2] == 2 # 2 + assert offsets.shape[3] == 6 # 16 + offsets = self.sigmoid(offsets) # v12 + + if return_weight: + return offsets + offsets = nn.functional.upsample(offsets, size=(x.shape[2], x.shape[3]), mode='bilinear') + + if self.stn: + batch_C_prime = sp_weight_fusion[:, (self.spt_length + 1):, :].reshape([x.shape[0], self.F, 2]) + build_P_prime = self.GridGenerator(batch_C_prime, self.I_r_size) + build_P_prime_reshape = build_P_prime.reshape([build_P_prime.shape[0], + self.I_r_size[0], + self.I_r_size[1], + 2]) + + else: # SPIN w.o. AIN + sp_weight = sp_weight_fusion[:, :self.spt_length, :] + lambda_color, offsets = None, None + + if self.stn: + batch_C_prime = sp_weight_fusion[:, self.spt_length:, :].reshape([x.shape[0], self.F, 2]) + build_P_prime = self.GridGenerator(batch_C_prime, self.I_r_size) + build_P_prime_reshape = build_P_prime.reshape([build_P_prime.shape[0], + self.I_r_size[0], + self.I_r_size[1], + 2]) + + x = self.sp_net(x, sp_weight, offsets, lambda_color) + if self.stn: + x = F.grid_sample(x=x, grid=build_P_prime_reshape, padding_mode='border') + return x diff --git a/ppocr/optimizer/learning_rate.py b/ppocr/optimizer/learning_rate.py index fe251f36e736bb1eac8a71a8115c941cbd7443e6..7d45109b4857871f52764c64d6d32e5322fc7c57 100644 --- a/ppocr/optimizer/learning_rate.py +++ b/ppocr/optimizer/learning_rate.py @@ -308,3 +308,81 @@ class Const(object): end_lr=self.learning_rate, last_epoch=self.last_epoch) return learning_rate + + +class DecayLearningRate(object): + """ + DecayLearningRate learning rate decay + new_lr = (lr - end_lr) * (1 - epoch/decay_steps)**power + end_lr + Args: + learning_rate(float): initial learning rate + step_each_epoch(int): steps each epoch + epochs(int): total training epochs + factor(float): Power of polynomial, should greater than 0.0 to get learning rate decay. Default: 0.9 + end_lr(float): The minimum final learning rate. Default: 0.0. + """ + + def __init__(self, + learning_rate, + step_each_epoch, + epochs, + factor=0.9, + end_lr=0, + **kwargs): + super(DecayLearningRate, self).__init__() + self.learning_rate = learning_rate + self.epochs = epochs + 1 + self.factor = factor + self.end_lr = 0 + self.decay_steps = step_each_epoch * epochs + + def __call__(self): + learning_rate = lr.PolynomialDecay( + learning_rate=self.learning_rate, + decay_steps=self.decay_steps, + power=self.factor, + end_lr=self.end_lr) + return learning_rate + + +class MultiStepDecay(object): + """ + Piecewise learning rate decay + Args: + step_each_epoch(int): steps each epoch + learning_rate (float): The initial learning rate. It is a python float number. + step_size (int): the interval to update. + gamma (float, optional): The Ratio that the learning rate will be reduced. ``new_lr = origin_lr * gamma`` . + It should be less than 1.0. Default: 0.1. + last_epoch (int, optional): The index of last epoch. Can be set to restart training. Default: -1, means initial learning rate. + """ + + def __init__(self, + learning_rate, + milestones, + step_each_epoch, + gamma, + warmup_epoch=0, + last_epoch=-1, + **kwargs): + super(MultiStepDecay, self).__init__() + self.milestones = [step_each_epoch * e for e in milestones] + self.learning_rate = learning_rate + self.gamma = gamma + self.last_epoch = last_epoch + self.warmup_epoch = round(warmup_epoch * step_each_epoch) + + def __call__(self): + learning_rate = lr.MultiStepDecay( + learning_rate=self.learning_rate, + milestones=self.milestones, + gamma=self.gamma, + last_epoch=self.last_epoch) + if self.warmup_epoch > 0: + learning_rate = lr.LinearWarmup( + learning_rate=learning_rate, + warmup_steps=self.warmup_epoch, + start_lr=0.0, + end_lr=self.learning_rate, + last_epoch=self.last_epoch) + return learning_rate diff --git a/ppocr/postprocess/__init__.py b/ppocr/postprocess/__init__.py index 2635117c84298bcf77a77cdcab553278f2df642b..eeebc5803f321df0d6709bb57a009692659bfe77 100644 --- a/ppocr/postprocess/__init__.py +++ b/ppocr/postprocess/__init__.py @@ -26,12 +26,14 @@ from .east_postprocess import EASTPostProcess from .sast_postprocess import SASTPostProcess from .fce_postprocess import FCEPostProcess from .rec_postprocess import CTCLabelDecode, AttnLabelDecode, SRNLabelDecode, \ - DistillationCTCLabelDecode, TableLabelDecode, NRTRLabelDecode, SARLabelDecode, \ - SEEDLabelDecode, PRENLabelDecode, ViTSTRLabelDecode, ABINetLabelDecode + DistillationCTCLabelDecode, NRTRLabelDecode, SARLabelDecode, \ + SEEDLabelDecode, PRENLabelDecode, ViTSTRLabelDecode, ABINetLabelDecode, \ + SPINLabelDecode from .cls_postprocess import ClsPostProcess from .pg_postprocess import PGPostProcess from .vqa_token_ser_layoutlm_postprocess import VQASerTokenLayoutLMPostProcess from .vqa_token_re_layoutlm_postprocess import VQAReTokenLayoutLMPostProcess +from .table_postprocess import TableMasterLabelDecode, TableLabelDecode def build_post_process(config, global_config=None): @@ -42,7 +44,8 @@ def build_post_process(config, global_config=None): 'DistillationDBPostProcess', 'NRTRLabelDecode', 'SARLabelDecode', 'SEEDLabelDecode', 'VQASerTokenLayoutLMPostProcess', 'VQAReTokenLayoutLMPostProcess', 'PRENLabelDecode', - 'DistillationSARLabelDecode', 'ViTSTRLabelDecode', 'ABINetLabelDecode' + 'DistillationSARLabelDecode', 'ViTSTRLabelDecode', 'ABINetLabelDecode', + 'TableMasterLabelDecode', 'SPINLabelDecode' ] if config['name'] == 'PSEPostProcess': diff --git a/ppocr/postprocess/db_postprocess.py b/ppocr/postprocess/db_postprocess.py index 27b428ef2e73c9abf81d3881b23979343c8595b2..5e2553c3a09f8359d1641d2d49b1bfb84df695ac 100755 --- a/ppocr/postprocess/db_postprocess.py +++ b/ppocr/postprocess/db_postprocess.py @@ -38,6 +38,7 @@ class DBPostProcess(object): unclip_ratio=2.0, use_dilation=False, score_mode="fast", + use_polygon=False, **kwargs): self.thresh = thresh self.box_thresh = box_thresh @@ -45,6 +46,7 @@ class DBPostProcess(object): self.unclip_ratio = unclip_ratio self.min_size = 3 self.score_mode = score_mode + self.use_polygon = use_polygon assert score_mode in [ "slow", "fast" ], "Score mode must be in [slow, fast] but got: {}".format(score_mode) @@ -52,6 +54,53 @@ class DBPostProcess(object): self.dilation_kernel = None if not use_dilation else np.array( [[1, 1], [1, 1]]) + def polygons_from_bitmap(self, pred, _bitmap, dest_width, dest_height): + ''' + _bitmap: single map with shape (1, H, W), + whose values are binarized as {0, 1} + ''' + + bitmap = _bitmap + height, width = bitmap.shape + + boxes = [] + scores = [] + + contours, _ = cv2.findContours((bitmap * 255).astype(np.uint8), + cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) + + for contour in contours[:self.max_candidates]: + epsilon = 0.002 * cv2.arcLength(contour, True) + approx = cv2.approxPolyDP(contour, epsilon, True) + points = approx.reshape((-1, 2)) + if points.shape[0] < 4: + continue + + score = self.box_score_fast(pred, points.reshape(-1, 2)) + if self.box_thresh > score: + continue + + if points.shape[0] > 2: + box = self.unclip(points, self.unclip_ratio) + if len(box) > 1: + continue + else: + continue + box = box.reshape(-1, 2) + + _, sside = self.get_mini_boxes(box.reshape((-1, 1, 2))) + if sside < self.min_size + 2: + continue + + box = np.array(box) + box[:, 0] = np.clip( + np.round(box[:, 0] / width * dest_width), 0, dest_width) + box[:, 1] = np.clip( + np.round(box[:, 1] / height * dest_height), 0, dest_height) + boxes.append(box.tolist()) + scores.append(score) + return boxes, scores + def boxes_from_bitmap(self, pred, _bitmap, dest_width, dest_height): ''' _bitmap: single map with shape (1, H, W), @@ -85,7 +134,7 @@ class DBPostProcess(object): if self.box_thresh > score: continue - box = self.unclip(points).reshape(-1, 1, 2) + box = self.unclip(points, self.unclip_ratio).reshape(-1, 1, 2) box, sside = self.get_mini_boxes(box) if sside < self.min_size + 2: continue @@ -99,8 +148,7 @@ class DBPostProcess(object): scores.append(score) return np.array(boxes, dtype=np.int16), scores - def unclip(self, box): - unclip_ratio = self.unclip_ratio + def unclip(self, box, unclip_ratio): poly = Polygon(box) distance = poly.area * unclip_ratio / poly.length offset = pyclipper.PyclipperOffset() @@ -185,8 +233,12 @@ class DBPostProcess(object): self.dilation_kernel) else: mask = segmentation[batch_index] - boxes, scores = self.boxes_from_bitmap(pred[batch_index], mask, - src_w, src_h) + if self.use_polygon is True: + boxes, scores = self.polygons_from_bitmap(pred[batch_index], + mask, src_w, src_h) + else: + boxes, scores = self.boxes_from_bitmap(pred[batch_index], mask, + src_w, src_h) boxes_batch.append({'points': boxes}) return boxes_batch @@ -202,6 +254,7 @@ class DistillationDBPostProcess(object): unclip_ratio=1.5, use_dilation=False, score_mode="fast", + use_polygon=False, **kwargs): self.model_name = model_name self.key = key @@ -211,7 +264,8 @@ class DistillationDBPostProcess(object): max_candidates=max_candidates, unclip_ratio=unclip_ratio, use_dilation=use_dilation, - score_mode=score_mode) + score_mode=score_mode, + use_polygon=use_polygon) def __call__(self, predicts, shape_list): results = {} diff --git a/ppocr/postprocess/pse_postprocess/pse_postprocess.py b/ppocr/postprocess/pse_postprocess/pse_postprocess.py index 34f1b8c9b5397a5513462468a9ee3d8530389607..962f3efe922c4a2656e0f44f478e1baf301a5542 100755 --- a/ppocr/postprocess/pse_postprocess/pse_postprocess.py +++ b/ppocr/postprocess/pse_postprocess/pse_postprocess.py @@ -58,6 +58,8 @@ class PSEPostProcess(object): kernels = (pred > self.thresh).astype('float32') text_mask = kernels[:, 0, :, :] + text_mask = paddle.unsqueeze(text_mask, axis=1) + kernels[:, 0:, :, :] = kernels[:, 0:, :, :] * text_mask score = score.numpy() diff --git a/ppocr/postprocess/rec_postprocess.py b/ppocr/postprocess/rec_postprocess.py index c77420ad19b76e203262568ba77f06ea248a1655..3fe29aabe58f42faa02d1b25b4255ba8a19b3ea3 100644 --- a/ppocr/postprocess/rec_postprocess.py +++ b/ppocr/postprocess/rec_postprocess.py @@ -380,146 +380,6 @@ class SRNLabelDecode(BaseRecLabelDecode): return idx -class TableLabelDecode(object): - """ """ - - def __init__(self, character_dict_path, **kwargs): - list_character, list_elem = self.load_char_elem_dict( - character_dict_path) - list_character = self.add_special_char(list_character) - list_elem = self.add_special_char(list_elem) - self.dict_character = {} - self.dict_idx_character = {} - for i, char in enumerate(list_character): - self.dict_idx_character[i] = char - self.dict_character[char] = i - self.dict_elem = {} - self.dict_idx_elem = {} - for i, elem in enumerate(list_elem): - self.dict_idx_elem[i] = elem - self.dict_elem[elem] = i - - def load_char_elem_dict(self, character_dict_path): - list_character = [] - list_elem = [] - with open(character_dict_path, "rb") as fin: - lines = fin.readlines() - substr = lines[0].decode('utf-8').strip("\n").strip("\r\n").split( - "\t") - character_num = int(substr[0]) - elem_num = int(substr[1]) - for cno in range(1, 1 + character_num): - character = lines[cno].decode('utf-8').strip("\n").strip("\r\n") - list_character.append(character) - for eno in range(1 + character_num, 1 + character_num + elem_num): - elem = lines[eno].decode('utf-8').strip("\n").strip("\r\n") - list_elem.append(elem) - return list_character, list_elem - - def add_special_char(self, list_character): - self.beg_str = "sos" - self.end_str = "eos" - list_character = [self.beg_str] + list_character + [self.end_str] - return list_character - - def __call__(self, preds): - structure_probs = preds['structure_probs'] - loc_preds = preds['loc_preds'] - if isinstance(structure_probs, paddle.Tensor): - structure_probs = structure_probs.numpy() - if isinstance(loc_preds, paddle.Tensor): - loc_preds = loc_preds.numpy() - structure_idx = structure_probs.argmax(axis=2) - structure_probs = structure_probs.max(axis=2) - structure_str, structure_pos, result_score_list, result_elem_idx_list = self.decode( - structure_idx, structure_probs, 'elem') - res_html_code_list = [] - res_loc_list = [] - batch_num = len(structure_str) - for bno in range(batch_num): - res_loc = [] - for sno in range(len(structure_str[bno])): - text = structure_str[bno][sno] - if text in ['', ' 0 and tmp_elem_idx == end_idx: - break - if tmp_elem_idx in ignored_tokens: - continue - - char_list.append(current_dict[tmp_elem_idx]) - elem_pos_list.append(idx) - score_list.append(structure_probs[batch_idx, idx]) - elem_idx_list.append(tmp_elem_idx) - result_list.append(char_list) - result_pos_list.append(elem_pos_list) - result_score_list.append(score_list) - result_elem_idx_list.append(elem_idx_list) - return result_list, result_pos_list, result_score_list, result_elem_idx_list - - def get_ignored_tokens(self, char_or_elem): - beg_idx = self.get_beg_end_flag_idx("beg", char_or_elem) - end_idx = self.get_beg_end_flag_idx("end", char_or_elem) - return [beg_idx, end_idx] - - def get_beg_end_flag_idx(self, beg_or_end, char_or_elem): - if char_or_elem == "char": - if beg_or_end == "beg": - idx = self.dict_character[self.beg_str] - elif beg_or_end == "end": - idx = self.dict_character[self.end_str] - else: - assert False, "Unsupport type %s in get_beg_end_flag_idx of char" \ - % beg_or_end - elif char_or_elem == "elem": - if beg_or_end == "beg": - idx = self.dict_elem[self.beg_str] - elif beg_or_end == "end": - idx = self.dict_elem[self.end_str] - else: - assert False, "Unsupport type %s in get_beg_end_flag_idx of elem" \ - % beg_or_end - else: - assert False, "Unsupport type %s in char_or_elem" \ - % char_or_elem - return idx - - class SARLabelDecode(BaseRecLabelDecode): """ Convert between text-label and text-index """ @@ -807,3 +667,18 @@ class ABINetLabelDecode(NRTRLabelDecode): def add_special_char(self, dict_character): dict_character = [''] + dict_character return dict_character + +class SPINLabelDecode(AttnLabelDecode): + """ Convert between text-label and text-index """ + + def __init__(self, character_dict_path=None, use_space_char=False, + **kwargs): + super(SPINLabelDecode, self).__init__(character_dict_path, + use_space_char) + + def add_special_char(self, dict_character): + self.beg_str = "sos" + self.end_str = "eos" + dict_character = dict_character + dict_character = [self.beg_str] + [self.end_str] + dict_character + return dict_character \ No newline at end of file diff --git a/ppocr/postprocess/table_postprocess.py b/ppocr/postprocess/table_postprocess.py new file mode 100644 index 0000000000000000000000000000000000000000..4396ec4f701478e7bdcdd8c7752738c5c8ef148d --- /dev/null +++ b/ppocr/postprocess/table_postprocess.py @@ -0,0 +1,160 @@ +# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import numpy as np +import paddle + +from .rec_postprocess import AttnLabelDecode + + +class TableLabelDecode(AttnLabelDecode): + """ """ + + def __init__(self, character_dict_path, **kwargs): + super(TableLabelDecode, self).__init__(character_dict_path) + self.td_token = ['', '', ''] + + def __call__(self, preds, batch=None): + structure_probs = preds['structure_probs'] + bbox_preds = preds['loc_preds'] + if isinstance(structure_probs, paddle.Tensor): + structure_probs = structure_probs.numpy() + if isinstance(bbox_preds, paddle.Tensor): + bbox_preds = bbox_preds.numpy() + shape_list = batch[-1] + result = self.decode(structure_probs, bbox_preds, shape_list) + if len(batch) == 1: # only contains shape + return result + + label_decode_result = self.decode_label(batch) + return result, label_decode_result + + def decode(self, structure_probs, bbox_preds, shape_list): + """convert text-label into text-index. + """ + ignored_tokens = self.get_ignored_tokens() + end_idx = self.dict[self.end_str] + + structure_idx = structure_probs.argmax(axis=2) + structure_probs = structure_probs.max(axis=2) + + structure_batch_list = [] + bbox_batch_list = [] + batch_size = len(structure_idx) + for batch_idx in range(batch_size): + structure_list = [] + bbox_list = [] + score_list = [] + for idx in range(len(structure_idx[batch_idx])): + char_idx = int(structure_idx[batch_idx][idx]) + if idx > 0 and char_idx == end_idx: + break + if char_idx in ignored_tokens: + continue + text = self.character[char_idx] + if text in self.td_token: + bbox = bbox_preds[batch_idx, idx] + bbox = self._bbox_decode(bbox, shape_list[batch_idx]) + bbox_list.append(bbox) + structure_list.append(text) + score_list.append(structure_probs[batch_idx, idx]) + structure_batch_list.append([structure_list, np.mean(score_list)]) + bbox_batch_list.append(np.array(bbox_list)) + result = { + 'bbox_batch_list': bbox_batch_list, + 'structure_batch_list': structure_batch_list, + } + return result + + def decode_label(self, batch): + """convert text-label into text-index. + """ + structure_idx = batch[1] + gt_bbox_list = batch[2] + shape_list = batch[-1] + ignored_tokens = self.get_ignored_tokens() + end_idx = self.dict[self.end_str] + + structure_batch_list = [] + bbox_batch_list = [] + batch_size = len(structure_idx) + for batch_idx in range(batch_size): + structure_list = [] + bbox_list = [] + for idx in range(len(structure_idx[batch_idx])): + char_idx = int(structure_idx[batch_idx][idx]) + if idx > 0 and char_idx == end_idx: + break + if char_idx in ignored_tokens: + continue + structure_list.append(self.character[char_idx]) + + bbox = gt_bbox_list[batch_idx][idx] + if bbox.sum() != 0: + bbox = self._bbox_decode(bbox, shape_list[batch_idx]) + bbox_list.append(bbox) + structure_batch_list.append(structure_list) + bbox_batch_list.append(bbox_list) + result = { + 'bbox_batch_list': bbox_batch_list, + 'structure_batch_list': structure_batch_list, + } + return result + + def _bbox_decode(self, bbox, shape): + h, w, ratio_h, ratio_w, pad_h, pad_w = shape + src_h = h / ratio_h + src_w = w / ratio_w + bbox[0::2] *= src_w + bbox[1::2] *= src_h + return bbox + + +class TableMasterLabelDecode(TableLabelDecode): + """ """ + + def __init__(self, character_dict_path, box_shape='ori', **kwargs): + super(TableMasterLabelDecode, self).__init__(character_dict_path) + self.box_shape = box_shape + assert box_shape in [ + 'ori', 'pad' + ], 'The shape used for box normalization must be ori or pad' + + def add_special_char(self, dict_character): + self.beg_str = '' + self.end_str = '' + self.unknown_str = '' + self.pad_str = '' + dict_character = dict_character + dict_character = dict_character + [ + self.unknown_str, self.beg_str, self.end_str, self.pad_str + ] + return dict_character + + def get_ignored_tokens(self): + pad_idx = self.dict[self.pad_str] + start_idx = self.dict[self.beg_str] + end_idx = self.dict[self.end_str] + unknown_idx = self.dict[self.unknown_str] + return [start_idx, end_idx, pad_idx, unknown_idx] + + def _bbox_decode(self, bbox, shape): + h, w, ratio_h, ratio_w, pad_h, pad_w = shape + if self.box_shape == 'pad': + h, w = pad_h, pad_w + bbox[0::2] *= w + bbox[1::2] *= h + bbox[0::2] /= ratio_w + bbox[1::2] /= ratio_h + return bbox diff --git a/ppocr/postprocess/vqa_token_ser_layoutlm_postprocess.py b/ppocr/postprocess/vqa_token_ser_layoutlm_postprocess.py index 782cdea6c58c69e0d728787e0e21e200c9e13790..8a6669f71f5ae6a7a16931e565b43355de5928d9 100644 --- a/ppocr/postprocess/vqa_token_ser_layoutlm_postprocess.py +++ b/ppocr/postprocess/vqa_token_ser_layoutlm_postprocess.py @@ -41,11 +41,13 @@ class VQASerTokenLayoutLMPostProcess(object): self.id2label_map_for_show[val] = key def __call__(self, preds, batch=None, *args, **kwargs): + if isinstance(preds, tuple): + preds = preds[0] if isinstance(preds, paddle.Tensor): preds = preds.numpy() if batch is not None: - return self._metric(preds, batch[1]) + return self._metric(preds, batch[5]) else: return self._infer(preds, **kwargs) @@ -63,11 +65,11 @@ class VQASerTokenLayoutLMPostProcess(object): j]]) return decode_out_list, label_decode_out_list - def _infer(self, preds, attention_masks, segment_offset_ids, ocr_infos): + def _infer(self, preds, segment_offset_ids, ocr_infos): results = [] - for pred, attention_mask, segment_offset_id, ocr_info in zip( - preds, attention_masks, segment_offset_ids, ocr_infos): + for pred, segment_offset_id, ocr_info in zip(preds, segment_offset_ids, + ocr_infos): pred = np.argmax(pred, axis=1) pred = [self.id2label_map[idx] for idx in pred] diff --git a/ppocr/utils/dict/spin_dict.txt b/ppocr/utils/dict/spin_dict.txt new file mode 100644 index 0000000000000000000000000000000000000000..8ee8347fd9c85228a3cf46c810d4fc28ab05c492 --- /dev/null +++ b/ppocr/utils/dict/spin_dict.txt @@ -0,0 +1,68 @@ +0 +1 +2 +3 +4 +5 +6 +7 +8 +9 +a +b +c +d +e +f +g +h +i +j +k +l +m +n +o +p +q +r +s +t +u +v +w +x +y +z +: +( +' +- +, +% +> +. +[ +? +) +" += +_ +* +] +; +& ++ +$ +@ +/ +| +! +< +# +` +{ +~ +\ +} +^ \ No newline at end of file diff --git a/ppocr/utils/dict/table_master_structure_dict.txt b/ppocr/utils/dict/table_master_structure_dict.txt new file mode 100644 index 0000000000000000000000000000000000000000..95ab2539a70aca4f695c53a38cdc1c3e164fcfb3 --- /dev/null +++ b/ppocr/utils/dict/table_master_structure_dict.txt @@ -0,0 +1,39 @@ + + + + + + + + + + + colspan="2" + colspan="3" + + + rowspan="2" + colspan="4" + colspan="6" + rowspan="3" + colspan="9" + colspan="10" + colspan="7" + rowspan="4" + rowspan="5" + rowspan="9" + colspan="8" + rowspan="8" + rowspan="6" + rowspan="7" + rowspan="10" + + + + + + + + diff --git a/ppocr/utils/dict/table_structure_dict.txt b/ppocr/utils/dict/table_structure_dict.txt index 9c4531e5f3b8c498e70d3c2ea0471e5e746a2c30..8edb10b8817ad596af6c63b6b8fc5eb2349b7464 100644 --- a/ppocr/utils/dict/table_structure_dict.txt +++ b/ppocr/utils/dict/table_structure_dict.txt @@ -1,281 +1,3 @@ -277 28 1267 1186 - -V -a -r -i -b -l -e - -H -z -d - -t -o -9 -5 -% -C -I - -p - -v -u -* -A -g -( -m -n -) -0 -. -7 -1 -6 -≤ -> -8 -3 -– -2 -G -4 -M -F -T -y -f -s -L -w -c -U -h -D -S -Q -R -x -P -- -E -O -/ -k -, -+ -N -K -q -′ -[ -] -< -≥ - -− - -μ -± -J -j -W -_ -Δ -B -“ -: -Y -α -λ -; - - -? -∼ -= -° -# -̊ -̈ -̂ -’ -Z -X -∗ -— -β -' -† -~ -@ -" -γ -↓ -↑ -& -‡ -χ -” -σ -§ -| -¶ -‐ -× -$ -→ -√ -✓ -‘ -\ -∞ -π -• -® -^ -∆ -≧ - 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-1760 1 -1764 1 -1772 2 -1798 1 -1808 1 -1820 1 -1852 1 -1856 1 -1874 1 -1902 1 -1908 1 -1952 1 -2004 1 -2018 1 -2020 1 -2028 1 -2174 1 -2233 1 -2244 1 -2280 1 -2290 1 -2352 1 -2604 1 -4190 1 + rowspan="10" \ No newline at end of file diff --git a/ppocr/utils/utility.py b/ppocr/utils/utility.py index 4a25ff8b2fa182faaf4f4ce8909c9ec2e9b55ccc..b881fcab20bc5ca076a0002bd72349768c7d881a 100755 --- a/ppocr/utils/utility.py +++ b/ppocr/utils/utility.py @@ -91,18 +91,19 @@ def check_and_read_gif(img_path): def load_vqa_bio_label_maps(label_map_path): with open(label_map_path, "r", encoding='utf-8') as fin: lines = fin.readlines() - lines = [line.strip() for line in lines] - if "O" not in lines: - lines.insert(0, "O") - labels = [] - for line in lines: - if line == "O": - labels.append("O") - else: - labels.append("B-" + line) - labels.append("I-" + line) - label2id_map = {label: idx for idx, label in enumerate(labels)} - id2label_map = {idx: label for idx, label in enumerate(labels)} + old_lines = [line.strip() for line in lines] + lines = ["O"] + for line in old_lines: + # "O" has already been in lines + if line.upper() in ["OTHER", "OTHERS", "IGNORE"]: + continue + lines.append(line) + labels = ["O"] + for line in lines[1:]: + labels.append("B-" + line) + labels.append("I-" + line) + label2id_map = {label.upper(): idx for idx, label in enumerate(labels)} + id2label_map = {idx: label.upper() for idx, label in enumerate(labels)} return label2id_map, id2label_map diff --git a/ppocr/utils/visual.py b/ppocr/utils/visual.py index 7a8c1674a74f89299de59f7cd120b4577a7499d8..e0fbf06abb471c294cb268520fb99bca1a6b1d61 100644 --- a/ppocr/utils/visual.py +++ b/ppocr/utils/visual.py @@ -11,6 +11,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +import cv2 import os import numpy as np from PIL import Image, ImageDraw, ImageFont @@ -19,7 +20,7 @@ from PIL import Image, ImageDraw, ImageFont def draw_ser_results(image, ocr_results, font_path="doc/fonts/simfang.ttf", - font_size=18): + font_size=14): np.random.seed(2021) color = (np.random.permutation(range(255)), np.random.permutation(range(255)), @@ -40,9 +41,15 @@ def draw_ser_results(image, if ocr_info["pred_id"] not in color_map: continue color = color_map[ocr_info["pred_id"]] - text = "{}: {}".format(ocr_info["pred"], ocr_info["text"]) + text = "{}: {}".format(ocr_info["pred"], ocr_info["transcription"]) - draw_box_txt(ocr_info["bbox"], text, draw, font, font_size, color) + if "bbox" in ocr_info: + # draw with ocr engine + bbox = ocr_info["bbox"] + else: + # draw with ocr groundtruth + bbox = trans_poly_to_bbox(ocr_info["points"]) + draw_box_txt(bbox, text, draw, font, font_size, color) img_new = Image.blend(image, img_new, 0.5) return np.array(img_new) @@ -62,6 +69,14 @@ def draw_box_txt(bbox, text, draw, font, font_size, color): draw.text((bbox[0][0] + 1, start_y), text, fill=(255, 255, 255), font=font) +def trans_poly_to_bbox(poly): + x1 = np.min([p[0] for p in poly]) + x2 = np.max([p[0] for p in poly]) + y1 = np.min([p[1] for p in poly]) + y2 = np.max([p[1] for p in poly]) + return [x1, y1, x2, y2] + + def draw_re_results(image, result, font_path="doc/fonts/simfang.ttf", @@ -80,10 +95,10 @@ def draw_re_results(image, color_line = (0, 255, 0) for ocr_info_head, ocr_info_tail in result: - draw_box_txt(ocr_info_head["bbox"], ocr_info_head["text"], draw, font, - font_size, color_head) - draw_box_txt(ocr_info_tail["bbox"], ocr_info_tail["text"], draw, font, - font_size, color_tail) + draw_box_txt(ocr_info_head["bbox"], ocr_info_head["transcription"], + draw, font, font_size, color_head) + draw_box_txt(ocr_info_tail["bbox"], ocr_info_tail["transcription"], + draw, font, font_size, color_tail) center_head = ( (ocr_info_head['bbox'][0] + ocr_info_head['bbox'][2]) // 2, @@ -96,3 +111,16 @@ def draw_re_results(image, img_new = Image.blend(image, img_new, 0.5) return np.array(img_new) + + +def draw_rectangle(img_path, boxes, use_xywh=False): + img = cv2.imread(img_path) + img_show = img.copy() + for box in boxes.astype(int): + if use_xywh: + x, y, w, h = box + x1, y1, x2, y2 = x - w // 2, y - h // 2, x + w // 2, y + h // 2 + else: + x1, y1, x2, y2 = box + cv2.rectangle(img_show, (x1, y1), (x2, y2), (255, 0, 0), 2) + return img_show \ No newline at end of file diff --git a/ppstructure/docs/kie.md b/ppstructure/docs/kie.md index 35498b33478d1010fd2548dfcb8586b4710723a1..315dd9f7bafa6b6160489eab330e8d278b2d119d 100644 --- a/ppstructure/docs/kie.md +++ b/ppstructure/docs/kie.md @@ -16,7 +16,7 @@ SDMGR是一个关键信息提取算法，将每个检测到的文本区域分类 训练和测试的数据采用wildreceipt数据集，通过如下指令下载数据集： ``` -wget https://paddleocr.bj.bcebos.com/dygraph_v2.1/kie/wildreceipt.tar && tar xf wildreceipt.tar +wget https://paddleocr.bj.bcebos.com/ppstructure/dataset/wildreceipt.tar && tar xf wildreceipt.tar ``` 执行预测： diff --git a/ppstructure/docs/kie_en.md b/ppstructure/docs/kie_en.md index 1fe38b0b399e9290526dafa5409673dc87026db7..7b3752223dd765e780d56d146c90bd0f892aac7b 100644 --- a/ppstructure/docs/kie_en.md +++ b/ppstructure/docs/kie_en.md @@ -15,7 +15,7 @@ This section provides a tutorial example on how to quickly use, train, and evalu [Wildreceipt dataset](https://paperswithcode.com/dataset/wildreceipt) is used for this tutorial. It contains 1765 photos, with 25 classes, and 50000 text boxes, which can be downloaded by wget: ```shell -wget https://paddleocr.bj.bcebos.com/dygraph_v2.1/kie/wildreceipt.tar && tar xf wildreceipt.tar +wget https://paddleocr.bj.bcebos.com/ppstructure/dataset/wildreceipt.tar && tar xf wildreceipt.tar ``` Download the pretrained model and predict the result: diff --git a/ppstructure/docs/models_list.md b/ppstructure/docs/models_list.md index c7dab999ff6e370c56c5495e22e91f117b3d1275..42d44009dad1ba1b07bb410c199993c6f79f3d5d 100644 --- a/ppstructure/docs/models_list.md +++ b/ppstructure/docs/models_list.md @@ -1,11 +1,11 @@ # PP-Structure 系列模型列表 -- [1. 版面分析模型](#1) -- [2. OCR和表格识别模型](#2) - - [2.1 OCR](#21) - - [2.2 表格识别模型](#22) -- [3. VQA模型](#3) -- [4. KIE模型](#4) +- [1. 版面分析模型](#1-版面分析模型) +- [2. OCR和表格识别模型](#2-ocr和表格识别模型) + - [2.1 OCR](#21-ocr) + - [2.2 表格识别模型](#22-表格识别模型) +- [3. VQA模型](#3-vqa模型) +- [4. KIE模型](#4-kie模型) @@ -35,18 +35,18 @@ |模型名称|模型简介|推理模型大小|下载地址| | --- | --- | --- | --- | -|en_ppocr_mobile_v2.0_table_structure|PubLayNet数据集训练的英文表格场景的表格结构预测|18.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/table/en_ppocr_mobile_v2.0_table_structure_train.tar) | +|en_ppocr_mobile_v2.0_table_structure|PubTabNet数据集训练的英文表格场景的表格结构预测|18.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/table/en_ppocr_mobile_v2.0_table_structure_train.tar) | ## 3. VQA模型 |模型名称|模型简介|推理模型大小|下载地址| | --- | --- | --- | --- | -|ser_LayoutXLM_xfun_zh|基于LayoutXLM在xfun中文数据集上训练的SER模型|1.4G|[推理模型 coming soon]() / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutXLM_xfun_zh.tar) | -|re_LayoutXLM_xfun_zh|基于LayoutXLM在xfun中文数据集上训练的RE模型|1.4G|[推理模型 coming soon]() / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutXLM_xfun_zh.tar) | -|ser_LayoutLMv2_xfun_zh|基于LayoutLMv2在xfun中文数据集上训练的SER模型|778M|[推理模型 coming soon]() / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLMv2_xfun_zh.tar) | +|ser_LayoutXLM_xfun_zh|基于LayoutXLM在xfun中文数据集上训练的SER模型|1.4G|[推理模型](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutXLM_xfun_zh_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutXLM_xfun_zh.tar) | +|re_LayoutXLM_xfun_zh|基于LayoutXLM在xfun中文数据集上训练的RE模型|1.4G|[推理模型 coming soon]() / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutXLM_xfun_zh.tar) | +|ser_LayoutLMv2_xfun_zh|基于LayoutLMv2在xfun中文数据集上训练的SER模型|778M|[推理模型](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLMv2_xfun_zh_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLMv2_xfun_zh.tar) | |re_LayoutLMv2_xfun_zh|基于LayoutLMv2在xfun中文数据集上训练的RE模型|765M|[推理模型 coming soon]() / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutLMv2_xfun_zh.tar) | -|ser_LayoutLM_xfun_zh|基于LayoutLM在xfun中文数据集上训练的SER模型|430M|[推理模型 coming soon]() / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLM_xfun_zh.tar) | +|ser_LayoutLM_xfun_zh|基于LayoutLM在xfun中文数据集上训练的SER模型|430M|[推理模型](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLM_xfun_zh_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLM_xfun_zh.tar) | ## 4. KIE模型 diff --git a/ppstructure/docs/models_list_en.md b/ppstructure/docs/models_list_en.md index b92c10c241df72c85649b64f915b4266cd3fe410..e133a0bb2a9b017207b5e92ea444aba4633a7457 100644 --- a/ppstructure/docs/models_list_en.md +++ b/ppstructure/docs/models_list_en.md @@ -1,11 +1,11 @@ # PP-Structure Model list -- [1. Layout Analysis](#1) -- [2. OCR and Table Recognition](#2) - - [2.1 OCR](#21) - - [2.2 Table Recognition](#22) -- [3. VQA](#3) -- [4. KIE](#4) +- [1. Layout Analysis](#1-layout-analysis) +- [2. OCR and Table Recognition](#2-ocr-and-table-recognition) + - [2.1 OCR](#21-ocr) + - [2.2 Table Recognition](#22-table-recognition) +- [3. VQA](#3-vqa) +- [4. KIE](#4-kie) @@ -42,11 +42,11 @@ If you need to use other OCR models, you can download the model in [PP-OCR model |model| description |inference model size|download| | --- |----------------------------------------------------------------| --- | --- | -|ser_LayoutXLM_xfun_zh| SER model trained on xfun Chinese dataset based on LayoutXLM |1.4G|[inference model coming soon]() / [trained model](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutXLM_xfun_zh.tar) | -|re_LayoutXLM_xfun_zh| Re model trained on xfun Chinese dataset based on LayoutXLM |1.4G|[inference model coming soon]() / [trained model](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutXLM_xfun_zh.tar) | -|ser_LayoutLMv2_xfun_zh| SER model trained on xfun Chinese dataset based on LayoutXLMv2 |778M|[inference model coming soon]() / [trained model](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLMv2_xfun_zh.tar) | +|ser_LayoutXLM_xfun_zh| SER model trained on xfun Chinese dataset based on LayoutXLM |1.4G|[inference model](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutXLM_xfun_zh_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutXLM_xfun_zh.tar) | +|re_LayoutXLM_xfun_zh| Re model trained on xfun Chinese dataset based on LayoutXLM |1.4G|[inference model coming soon]() / [trained model](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutXLM_xfun_zh.tar) | +|ser_LayoutLMv2_xfun_zh| SER model trained on xfun Chinese dataset based on LayoutXLMv2 |778M|[inference model](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLMv2_xfun_zh_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLMv2_xfun_zh.tar) | |re_LayoutLMv2_xfun_zh| Re model trained on xfun Chinese dataset based on LayoutXLMv2 |765M|[inference model coming soon]() / [trained model](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutLMv2_xfun_zh.tar) | -|ser_LayoutLM_xfun_zh| SER model trained on xfun Chinese dataset based on LayoutLM |430M|[inference model coming soon]() / [trained model](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLM_xfun_zh.tar) | +|ser_LayoutLM_xfun_zh| SER model trained on xfun Chinese dataset based on LayoutLM |430M|[inference model](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLM_xfun_zh_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLM_xfun_zh.tar) | ## 4. KIE diff --git a/ppstructure/predict_system.py b/ppstructure/predict_system.py index b0ede5f3a1b88df6efed53d7ca33a696bc7a7fff..d6f2e24240ff783e14dbd61efdd27877f9ec39ff 100644 --- a/ppstructure/predict_system.py +++ b/ppstructure/predict_system.py @@ -34,7 +34,7 @@ from ppocr.utils.logging import get_logger from tools.infer.predict_system import TextSystem from ppstructure.table.predict_table import TableSystem, to_excel from ppstructure.utility import parse_args, draw_structure_result -from ppstructure.recovery.docx import convert_info_docx +from ppstructure.recovery.recovery_to_doc import convert_info_docx logger = get_logger() diff --git a/ppstructure/recovery/README_ch.md b/ppstructure/recovery/README_ch.md index 1f72f8de8a5e2eb51c8c4f58df30465f5361a301..5a05abffd0399387bc0d22d878e64d03d8894a79 100644 --- a/ppstructure/recovery/README_ch.md +++ b/ppstructure/recovery/README_ch.md @@ -44,6 +44,12 @@ python3 -m pip install "paddlepaddle>=2.2" -i https://mirror.baidu.com/pypi/simp 更多需求，请参照[安装文档](https://www.paddlepaddle.org.cn/install/quick)中的说明进行操作。 +* **(2)安装依赖** + +```bash +python3 -m pip install -r ppstructure/recovery/requirements.txt +``` + ### 2.2 安装PaddleOCR diff --git a/ppstructure/recovery/docx.py b/ppstructure/recovery/recovery_to_doc.py similarity index 100% rename from ppstructure/recovery/docx.py rename to ppstructure/recovery/recovery_to_doc.py diff --git a/ppstructure/table/predict_structure.py b/ppstructure/table/predict_structure.py index 0179c614ae4864677576f6073f291282fb772988..7a7d3169d567493b4707b63c75cec07485cf5acb 100755 --- a/ppstructure/table/predict_structure.py +++ b/ppstructure/table/predict_structure.py @@ -23,43 +23,63 @@ os.environ["FLAGS_allocator_strategy"] = 'auto_growth' import cv2 import numpy as np import time +import json import tools.infer.utility as utility from ppocr.data import create_operators, transform from ppocr.postprocess import build_post_process from ppocr.utils.logging import get_logger from ppocr.utils.utility import get_image_file_list, check_and_read_gif +from ppocr.utils.visual import draw_rectangle from ppstructure.utility import parse_args logger = get_logger() +def build_pre_process_list(args): + resize_op = {'ResizeTableImage': {'max_len': args.table_max_len, }} + pad_op = { + 'PaddingTableImage': { + 'size': [args.table_max_len, args.table_max_len] + } + } + normalize_op = { + 'NormalizeImage': { + 'std': [0.229, 0.224, 0.225] if + args.table_algorithm not in ['TableMaster'] else [0.5, 0.5, 0.5], + 'mean': [0.485, 0.456, 0.406] if + args.table_algorithm not in ['TableMaster'] else [0.5, 0.5, 0.5], + 'scale': '1./255.', + 'order': 'hwc' + } + } + to_chw_op = {'ToCHWImage': None} + keep_keys_op = {'KeepKeys': {'keep_keys': ['image', 'shape']}} + if args.table_algorithm not in ['TableMaster']: + pre_process_list = [ + resize_op, normalize_op, pad_op, to_chw_op, keep_keys_op + ] + else: + pre_process_list = [ + resize_op, pad_op, normalize_op, to_chw_op, keep_keys_op + ] + return pre_process_list + + class TableStructurer(object): def __init__(self, args): - pre_process_list = [{ - 'ResizeTableImage': { - 'max_len': args.table_max_len - } - }, { - 'NormalizeImage': { - 'std': [0.229, 0.224, 0.225], - 'mean': [0.485, 0.456, 0.406], - 'scale': '1./255.', - 'order': 'hwc' + pre_process_list = build_pre_process_list(args) + if args.table_algorithm not in ['TableMaster']: + postprocess_params = { + 'name': 'TableLabelDecode', + "character_dict_path": args.table_char_dict_path, } - }, { - 'PaddingTableImage': None - }, { - 'ToCHWImage': None - }, { - 'KeepKeys': { - 'keep_keys': ['image'] + else: + postprocess_params = { + 'name': 'TableMasterLabelDecode', + "character_dict_path": args.table_char_dict_path, + 'box_shape': 'pad' } - }] - postprocess_params = { - 'name': 'TableLabelDecode', - "character_dict_path": args.table_char_dict_path, - } self.preprocess_op = create_operators(pre_process_list) self.postprocess_op = build_post_process(postprocess_params) @@ -88,27 +108,17 @@ class TableStructurer(object): preds['structure_probs'] = outputs[1] preds['loc_preds'] = outputs[0] - post_result = self.postprocess_op(preds) - - structure_str_list = post_result['structure_str_list'] - res_loc = post_result['res_loc'] - imgh, imgw = ori_im.shape[0:2] - res_loc_final = [] - for rno in range(len(res_loc[0])): - x0, y0, x1, y1 = res_loc[0][rno] - left = max(int(imgw * x0), 0) - top = max(int(imgh * y0), 0) - right = min(int(imgw * x1), imgw - 1) - bottom = min(int(imgh * y1), imgh - 1) - res_loc_final.append([left, top, right, bottom]) - - structure_str_list = structure_str_list[0][:-1] + shape_list = np.expand_dims(data[-1], axis=0) + post_result = self.postprocess_op(preds, [shape_list]) + + structure_str_list = post_result['structure_batch_list'][0] + bbox_list = post_result['bbox_batch_list'][0] + structure_str_list = structure_str_list[0] structure_str_list = [ '', '', '' ] + structure_str_list + ['
', '', ''] - elapse = time.time() - starttime - return (structure_str_list, res_loc_final), elapse + return (structure_str_list, bbox_list), elapse def main(args): @@ -116,21 +126,35 @@ def main(args): table_structurer = TableStructurer(args) count = 0 total_time = 0 - for image_file in image_file_list: - img, flag = check_and_read_gif(image_file) - if not flag: - img = cv2.imread(image_file) - if img is None: - logger.info("error in loading image:{}".format(image_file)) - continue - structure_res, elapse = table_structurer(img) - - logger.info("result: {}".format(structure_res)) - - if count > 0: - total_time += elapse - count += 1 - logger.info("Predict time of {}: {}".format(image_file, elapse)) + use_xywh = args.table_algorithm in ['TableMaster'] + os.makedirs(args.output, exist_ok=True) + with open( + os.path.join(args.output, 'infer.txt'), mode='w', + encoding='utf-8') as f_w: + for image_file in image_file_list: + img, flag = check_and_read_gif(image_file) + if not flag: + img = cv2.imread(image_file) + if img is None: + logger.info("error in loading image:{}".format(image_file)) + continue + structure_res, elapse = table_structurer(img) + structure_str_list, bbox_list = structure_res + bbox_list_str = json.dumps(bbox_list.tolist()) + logger.info("result: {}, {}".format(structure_str_list, + bbox_list_str)) + f_w.write("result: {}, {}\n".format(structure_str_list, + bbox_list_str)) + + img = draw_rectangle(image_file, bbox_list, use_xywh) + img_save_path = os.path.join(args.output, + os.path.basename(image_file)) + cv2.imwrite(img_save_path, img) + logger.info("save vis result to {}".format(img_save_path)) + if count > 0: + total_time += elapse + count += 1 + logger.info("Predict time of {}: {}".format(image_file, elapse)) if __name__ == "__main__": diff --git a/ppstructure/table/predict_table.py b/ppstructure/table/predict_table.py index aa05459589208dde66a6710322593d091af41325..becc6daef02e7e3e98fcccd3b87a93e725577886 100644 --- a/ppstructure/table/predict_table.py +++ b/ppstructure/table/predict_table.py @@ -129,11 +129,25 @@ class TableSystem(object): def rebuild_table(self, structure_res, dt_boxes, rec_res): pred_structures, pred_bboxes = structure_res + dt_boxes, rec_res = self.filter_ocr_result(pred_bboxes,dt_boxes, rec_res) matched_index = self.match_result(dt_boxes, pred_bboxes) pred_html, pred = self.get_pred_html(pred_structures, matched_index, rec_res) return pred_html, pred + def filter_ocr_result(self, pred_bboxes,dt_boxes, rec_res): + y1 = pred_bboxes[:,1::2].min() + new_dt_boxes = [] + new_rec_res = [] + + for box,rec in zip(dt_boxes, rec_res): + if np.max(box[1::2]) < y1: + continue + new_dt_boxes.append(box) + new_rec_res.append(rec) + return new_dt_boxes, new_rec_res + + def match_result(self, dt_boxes, pred_bboxes): matched = {} for i, gt_box in enumerate(dt_boxes): diff --git a/ppstructure/utility.py b/ppstructure/utility.py index 1ad902e7e6be95a6901e3774420fad337f594861..af0616239b167ff9ca5f6e1222015d51338d6bab 100644 --- a/ppstructure/utility.py +++ b/ppstructure/utility.py @@ -25,6 +25,7 @@ def init_args(): parser.add_argument("--output", type=str, default='./output') # params for table structure parser.add_argument("--table_max_len", type=int, default=488) + parser.add_argument("--table_algorithm", type=str, default='TableAttn') parser.add_argument("--table_model_dir", type=str) parser.add_argument( "--table_char_dict_path", @@ -40,6 +41,13 @@ def init_args(): type=ast.literal_eval, default=None, help='label map according to ppstructure/layout/README_ch.md') + # params for vqa + parser.add_argument("--vqa_algorithm", type=str, default='LayoutXLM') + parser.add_argument("--ser_model_dir", type=str) + parser.add_argument( + "--ser_dict_path", + type=str, + default="../train_data/XFUND/class_list_xfun.txt") # params for inference parser.add_argument( "--mode", @@ -65,7 +73,7 @@ def init_args(): "--recovery", type=bool, default=False, - help='Whether to enable layout of recovery') + help='Whether to enable layout of recovery') return parser diff --git a/ppstructure/vqa/README.md b/ppstructure/vqa/README.md index e3a10671ddb6494eb15073e7ac007aa1e8e6a32a..28b794383bceccf655bdf00df5ee0c98841e2e95 100644 --- a/ppstructure/vqa/README.md +++ b/ppstructure/vqa/README.md @@ -1,19 +1,15 @@ English | [简体中文](README_ch.md) -- [Document Visual Question Answering (Doc-VQA)](#Document-Visual-Question-Answering) - - [1. Introduction](#1-Introduction) - - [2. Performance](#2-performance) - - [3. Effect demo](#3-Effect-demo) - - [3.1 SER](#31-ser) - - [3.2 RE](#32-re) - - [4. Install](#4-Install) - - [4.1 Installation dependencies](#41-Install-dependencies) - - [4.2 Install PaddleOCR](#42-Install-PaddleOCR) - - [5. Usage](#5-Usage) - - [5.1 Data and Model Preparation](#51-Data-and-Model-Preparation) - - [5.2 SER](#52-ser) - - [5.3 RE](#53-re) - - [6. Reference](#6-Reference-Links) +- [1 Introduction](#1-introduction) +- [2. Performance](#2-performance) +- [3. Effect demo](#3-effect-demo) + - [3.1 SER](#31-ser) + - [3.2 RE](#32-re) +- [4. Install](#4-install) + - [4.1 Install dependencies](#41-install-dependencies) + - [5.3 RE](#53-re) +- [6. Reference Links](#6-reference-links) +- [License](#license) # Document Visual Question Answering @@ -125,13 +121,13 @@ If you want to experience the prediction process directly, you can download the * Download the processed dataset -The download address of the processed XFUND Chinese dataset: [https://paddleocr.bj.bcebos.com/dataset/XFUND.tar](https://paddleocr.bj.bcebos.com/dataset/XFUND.tar). +The download address of the processed XFUND Chinese dataset: [link](https://paddleocr.bj.bcebos.com/ppstructure/dataset/XFUND.tar). Download and unzip the dataset, and place the dataset in the current directory after unzipping. ```shell -wget https://paddleocr.bj.bcebos.com/dataset/XFUND.tar +wget https://paddleocr.bj.bcebos.com/ppstructure/dataset/XFUND.tar ```` * Convert the dataset @@ -187,17 +183,17 @@ CUDA_VISIBLE_DEVICES=0 python3 tools/eval.py -c configs/vqa/ser/layoutxlm.yml -o ```` Finally, `precision`, `recall`, `hmean` and other indicators will be printed -* Use `OCR engine + SER` tandem prediction +* `OCR + SER` tandem prediction based on training engine -Use the following command to complete the series prediction of `OCR engine + SER`, taking the pretrained SER model as an example: +Use the following command to complete the series prediction of `OCR engine + SER`, taking the SER model based on LayoutXLM as an example:: ```shell -CUDA_VISIBLE_DEVICES=0 python3 tools/infer_vqa_token_ser.py -c configs/vqa/ser/layoutxlm.yml -o Architecture.Backbone.checkpoints=pretrain/ser_LayoutXLM_xfun_zh/Global.infer_img=doc/vqa/input/zh_val_42.jpg +python3.7 tools/export_model.py -c configs/vqa/ser/layoutxlm.yml -o Architecture.Backbone.checkpoints=pretrain/ser_LayoutXLM_xfun_zh/ Global.save_inference_dir=output/ser/infer ```` Finally, the prediction result visualization image and the prediction result text file will be saved in the directory configured by the `config.Global.save_res_path` field. The prediction result text file is named `infer_results.txt`. -* End-to-end evaluation of `OCR engine + SER` prediction system +* End-to-end evaluation of `OCR + SER` prediction system First use the `tools/infer_vqa_token_ser.py` script to complete the prediction of the dataset, then use the following command to evaluate. @@ -205,6 +201,24 @@ First use the `tools/infer_vqa_token_ser.py` script to complete the prediction o export CUDA_VISIBLE_DEVICES=0 python3 tools/eval_with_label_end2end.py --gt_json_path XFUND/zh_val/xfun_normalize_val.json --pred_json_path output_res/infer_results.txt ```` +* export model + +Use the following command to complete the model export of the SER model, taking the SER model based on LayoutXLM as an example: + +```shell +python3.7 tools/export_model.py -c configs/vqa/ser/layoutxlm.yml -o Architecture.Backbone.checkpoints=pretrain/ser_LayoutXLM_xfun_zh/ Global.save_inference_dir=output/ser/infer +``` +The converted model will be stored in the directory specified by the `Global.save_inference_dir` field. + +* `OCR + SER` tandem prediction based on prediction engine + +Use the following command to complete the tandem prediction of `OCR + SER` based on the prediction engine, taking the SER model based on LayoutXLM as an example: + +```shell +cd ppstructure +CUDA_VISIBLE_DEVICES=0 python3.7 vqa/predict_vqa_token_ser.py --vqa_algorithm=LayoutXLM --ser_model_dir=../output/ser/infer --ser_dict_path=../train_data/XFUND/class_list_xfun.txt --vis_font_path=../doc/fonts/simfang.ttf --image_dir=docs/vqa/input/zh_val_42.jpg --output=output +``` +After the prediction is successful, the visualization images and results will be saved in the directory specified by the `output` field ### 5.3 RE @@ -247,11 +261,19 @@ Finally, `precision`, `recall`, `hmean` and other indicators will be printed Use the following command to complete the series prediction of `OCR engine + SER + RE`, taking the pretrained SER and RE models as an example: ```shell export CUDA_VISIBLE_DEVICES=0 -python3 tools/infer_vqa_token_ser_re.py -c configs/vqa/re/layoutxlm.yml -o Architecture.Backbone.checkpoints=pretrain/re_LayoutXLM_xfun_zh/Global.infer_img=doc/vqa/input/zh_val_21.jpg -c_ser configs/vqa/ser/layoutxlm. yml -o_ser Architecture.Backbone.checkpoints=pretrain/ser_LayoutXLM_xfun_zh/ +python3 tools/infer_vqa_token_ser_re.py -c configs/vqa/re/layoutxlm.yml -o Architecture.Backbone.checkpoints=pretrain/re_LayoutXLM_xfun_zh/Global.infer_img=ppstructure/docs/vqa/input/zh_val_21.jpg -c_ser configs/vqa/ser/layoutxlm. yml -o_ser Architecture.Backbone.checkpoints=pretrain/ser_LayoutXLM_xfun_zh/ ```` Finally, the prediction result visualization image and the prediction result text file will be saved in the directory configured by the `config.Global.save_res_path` field. The prediction result text file is named `infer_results.txt`. +* export model + +cooming soon + +* `OCR + SER + RE` tandem prediction based on prediction engine + +cooming soon + ## 6. Reference Links - LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding, https://arxiv.org/pdf/2104.08836.pdf diff --git a/ppstructure/vqa/README_ch.md b/ppstructure/vqa/README_ch.md index b677dc07bce6c1a752d753b6a1c538b4d3f99271..f168110ed9b2e750b3b2ee6f5ab0116daebc3e77 100644 --- a/ppstructure/vqa/README_ch.md +++ b/ppstructure/vqa/README_ch.md @@ -1,19 +1,19 @@ [English](README.md) | 简体中文 -- [文档视觉问答（DOC-VQA）](#文档视觉问答doc-vqa) - - [1. 简介](#1-简介) - - [2. 性能](#2-性能) - - [3. 效果演示](#3-效果演示) - - [3.1 SER](#31-ser) - - [3.2 RE](#32-re) - - [4. 安装](#4-安装) - - [4.1 安装依赖](#41-安装依赖) - - [4.2 安装PaddleOCR（包含 PP-OCR 和 VQA）](#42-安装paddleocr包含-pp-ocr-和-vqa) - - [5. 使用](#5-使用) - - [5.1 数据和预训练模型准备](#51-数据和预训练模型准备) - - [5.2 SER](#52-ser) - - [5.3 RE](#53-re) - - [6. 参考链接](#6-参考链接) +- [1. 简介](#1-简介) +- [2. 性能](#2-性能) +- [3. 效果演示](#3-效果演示) + - [3.1 SER](#31-ser) + - [3.2 RE](#32-re) +- [4. 安装](#4-安装) + - [4.1 安装依赖](#41-安装依赖) + - [4.2 安装PaddleOCR（包含 PP-OCR 和 VQA）](#42-安装paddleocr包含-pp-ocr-和-vqa) +- [5. 使用](#5-使用) + - [5.1 数据和预训练模型准备](#51-数据和预训练模型准备) + - [5.2 SER](#52-ser) + - [5.3 RE](#53-re) +- [6. 参考链接](#6-参考链接) +- [License](#license) # 文档视觉问答（DOC-VQA） @@ -122,13 +122,13 @@ python3 -m pip install -r ppstructure/vqa/requirements.txt * 下载处理好的数据集 -处理好的XFUND中文数据集下载地址：[https://paddleocr.bj.bcebos.com/dataset/XFUND.tar](https://paddleocr.bj.bcebos.com/dataset/XFUND.tar)。 +处理好的XFUND中文数据集下载地址：[链接](https://paddleocr.bj.bcebos.com/ppstructure/dataset/XFUND.tar)。 下载并解压该数据集，解压后将数据集放置在当前目录下。 ```shell -wget https://paddleocr.bj.bcebos.com/dataset/XFUND.tar +wget https://paddleocr.bj.bcebos.com/ppstructure/dataset/XFUND.tar ``` * 转换数据集 @@ -183,16 +183,16 @@ CUDA_VISIBLE_DEVICES=0 python3 tools/eval.py -c configs/vqa/ser/layoutxlm.yml -o ``` 最终会打印出`precision`, `recall`, `hmean`等指标 -* 使用`OCR引擎 + SER`串联预测 +* 基于训练引擎的`OCR + SER`串联预测 -使用如下命令即可完成`OCR引擎 + SER`的串联预测, 以SER预训练模型为例: +使用如下命令即可完成基于训练引擎的`OCR + SER`的串联预测, 以基于LayoutXLM的SER模型为例: ```shell CUDA_VISIBLE_DEVICES=0 python3 tools/infer_vqa_token_ser.py -c configs/vqa/ser/layoutxlm.yml -o Architecture.Backbone.checkpoints=pretrain/ser_LayoutXLM_xfun_zh/ Global.infer_img=doc/vqa/input/zh_val_42.jpg ``` 最终会在`config.Global.save_res_path`字段所配置的目录下保存预测结果可视化图像以及预测结果文本文件，预测结果文本文件名为`infer_results.txt`。 -* 对`OCR引擎 + SER`预测系统进行端到端评估 +* 对`OCR + SER`预测系统进行端到端评估 首先使用 `tools/infer_vqa_token_ser.py` 脚本完成数据集的预测，然后使用下面的命令进行评估。 @@ -200,6 +200,24 @@ CUDA_VISIBLE_DEVICES=0 python3 tools/infer_vqa_token_ser.py -c configs/vqa/ser/l export CUDA_VISIBLE_DEVICES=0 python3 tools/eval_with_label_end2end.py --gt_json_path XFUND/zh_val/xfun_normalize_val.json --pred_json_path output_res/infer_results.txt ``` +* 模型导出 + +使用如下命令即可完成SER模型的模型导出, 以基于LayoutXLM的SER模型为例: + +```shell +python3.7 tools/export_model.py -c configs/vqa/ser/layoutxlm.yml -o Architecture.Backbone.checkpoints=pretrain/ser_LayoutXLM_xfun_zh/ Global.save_inference_dir=output/ser/infer +``` +转换后的模型会存放在`Global.save_inference_dir`字段指定的目录下。 + +* 基于预测引擎的`OCR + SER`串联预测 + +使用如下命令即可完成基于预测引擎的`OCR + SER`的串联预测, 以基于LayoutXLM的SER模型为例: + +```shell +cd ppstructure +CUDA_VISIBLE_DEVICES=0 python3.7 vqa/predict_vqa_token_ser.py --vqa_algorithm=LayoutXLM --ser_model_dir=../output/ser/infer --ser_dict_path=../train_data/XFUND/class_list_xfun.txt --vis_font_path=../doc/fonts/simfang.ttf --image_dir=docs/vqa/input/zh_val_42.jpg --output=output +``` +预测成功后，可视化图片和结果会保存在`output`字段指定的目录下 ### 5.3 RE @@ -236,16 +254,24 @@ CUDA_VISIBLE_DEVICES=0 python3 tools/eval.py -c configs/vqa/re/layoutxlm.yml -o ``` 最终会打印出`precision`, `recall`, `hmean`等指标 -* 使用`OCR引擎 + SER + RE`串联预测 +* 基于训练引擎的`OCR + SER + RE`串联预测 -使用如下命令即可完成`OCR引擎 + SER + RE`的串联预测, 以预训练SER和RE模型为例： +使用如下命令即可完成基于训练引擎的`OCR + SER + RE`串联预测, 以基于LayoutXLMSER和RE模型为例： ```shell export CUDA_VISIBLE_DEVICES=0 -python3 tools/infer_vqa_token_ser_re.py -c configs/vqa/re/layoutxlm.yml -o Architecture.Backbone.checkpoints=pretrain/re_LayoutXLM_xfun_zh/ Global.infer_img=doc/vqa/input/zh_val_21.jpg -c_ser configs/vqa/ser/layoutxlm.yml -o_ser Architecture.Backbone.checkpoints=pretrain/ser_LayoutXLM_xfun_zh/ +python3 tools/infer_vqa_token_ser_re.py -c configs/vqa/re/layoutxlm.yml -o Architecture.Backbone.checkpoints=pretrain/re_LayoutXLM_xfun_zh/ Global.infer_img=ppstructure/docs/vqa/input/zh_val_21.jpg -c_ser configs/vqa/ser/layoutxlm.yml -o_ser Architecture.Backbone.checkpoints=pretrain/ser_LayoutXLM_xfun_zh/ ``` 最终会在`config.Global.save_res_path`字段所配置的目录下保存预测结果可视化图像以及预测结果文本文件，预测结果文本文件名为`infer_results.txt`。 +* 模型导出 + +cooming soon + +* 基于预测引擎的`OCR + SER + RE`串联预测 + +cooming soon + ## 6. 参考链接 - LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding, https://arxiv.org/pdf/2104.08836.pdf diff --git a/ppstructure/vqa/labels/labels_ser.txt b/ppstructure/vqa/labels/labels_ser.txt deleted file mode 100644 index 508e48112412f62538baf0c78bcf99ec8945196e..0000000000000000000000000000000000000000 --- a/ppstructure/vqa/labels/labels_ser.txt +++ /dev/null @@ -1,3 +0,0 @@ -QUESTION -ANSWER -HEADER diff --git a/ppstructure/vqa/predict_vqa_token_ser.py b/ppstructure/vqa/predict_vqa_token_ser.py new file mode 100644 index 0000000000000000000000000000000000000000..3097ebcf1640eb1e4dd65f76635f21231984b0ef --- /dev/null +++ b/ppstructure/vqa/predict_vqa_token_ser.py @@ -0,0 +1,169 @@ +# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import os +import sys + +__dir__ = os.path.dirname(os.path.abspath(__file__)) +sys.path.append(__dir__) +sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '../..'))) + +os.environ["FLAGS_allocator_strategy"] = 'auto_growth' + +import cv2 +import json +import numpy as np +import time + +import tools.infer.utility as utility +from ppocr.data import create_operators, transform +from ppocr.postprocess import build_post_process +from ppocr.utils.logging import get_logger +from ppocr.utils.visual import draw_ser_results +from ppocr.utils.utility import get_image_file_list, check_and_read_gif +from ppstructure.utility import parse_args + +from paddleocr import PaddleOCR + +logger = get_logger() + + +class SerPredictor(object): + def __init__(self, args): + self.ocr_engine = PaddleOCR(use_angle_cls=False, show_log=False) + + pre_process_list = [{ + 'VQATokenLabelEncode': { + 'algorithm': args.vqa_algorithm, + 'class_path': args.ser_dict_path, + 'contains_re': False, + 'ocr_engine': self.ocr_engine + } + }, { + 'VQATokenPad': { + 'max_seq_len': 512, + 'return_attention_mask': True + } + }, { + 'VQASerTokenChunk': { + 'max_seq_len': 512, + 'return_attention_mask': True + } + }, { + 'Resize': { + 'size': [224, 224] + } + }, { + 'NormalizeImage': { + 'std': [58.395, 57.12, 57.375], + 'mean': [123.675, 116.28, 103.53], + 'scale': '1', + 'order': 'hwc' + } + }, { + 'ToCHWImage': None + }, { + 'KeepKeys': { + 'keep_keys': [ + 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', + 'image', 'labels', 'segment_offset_id', 'ocr_info', + 'entities' + ] + } + }] + postprocess_params = { + 'name': 'VQASerTokenLayoutLMPostProcess', + "class_path": args.ser_dict_path, + } + + self.preprocess_op = create_operators(pre_process_list, + {'infer_mode': True}) + self.postprocess_op = build_post_process(postprocess_params) + self.predictor, self.input_tensor, self.output_tensors, self.config = \ + utility.create_predictor(args, 'ser', logger) + + def __call__(self, img): + ori_im = img.copy() + data = {'image': img} + data = transform(data, self.preprocess_op) + img = data[0] + if img is None: + return None, 0 + img = np.expand_dims(img, axis=0) + img = img.copy() + starttime = time.time() + + for idx in range(len(self.input_tensor)): + expand_input = np.expand_dims(data[idx], axis=0) + self.input_tensor[idx].copy_from_cpu(expand_input) + + self.predictor.run() + + outputs = [] + for output_tensor in self.output_tensors: + output = output_tensor.copy_to_cpu() + outputs.append(output) + preds = outputs[0] + + post_result = self.postprocess_op( + preds, segment_offset_ids=[data[6]], ocr_infos=[data[7]]) + elapse = time.time() - starttime + return post_result, elapse + + +def main(args): + image_file_list = get_image_file_list(args.image_dir) + ser_predictor = SerPredictor(args) + count = 0 + total_time = 0 + + os.makedirs(args.output, exist_ok=True) + with open( + os.path.join(args.output, 'infer.txt'), mode='w', + encoding='utf-8') as f_w: + for image_file in image_file_list: + img, flag = check_and_read_gif(image_file) + if not flag: + img = cv2.imread(image_file) + img = img[:, :, ::-1] + if img is None: + logger.info("error in loading image:{}".format(image_file)) + continue + ser_res, elapse = ser_predictor(img) + ser_res = ser_res[0] + + res_str = '{}\t{}\n'.format( + image_file, + json.dumps( + { + "ocr_info": ser_res, + }, ensure_ascii=False)) + f_w.write(res_str) + + img_res = draw_ser_results( + image_file, + ser_res, + font_path=args.vis_font_path, ) + + img_save_path = os.path.join(args.output, + os.path.basename(image_file)) + cv2.imwrite(img_save_path, img_res) + logger.info("save vis result to {}".format(img_save_path)) + if count > 0: + total_time += elapse + count += 1 + logger.info("Predict time of {}: {}".format(image_file, elapse)) + + +if __name__ == "__main__": + main(parse_args()) diff --git a/ppstructure/vqa/requirements.txt b/ppstructure/vqa/requirements.txt index 0042ec0baedcc3e7bbecb922d10b93c95219219d..fcd882274c4402ba2a1d34f20ee6e2befa157121 100644 --- a/ppstructure/vqa/requirements.txt +++ b/ppstructure/vqa/requirements.txt @@ -1,4 +1,7 @@ sentencepiece yacs seqeval -paddlenlp>=2.2.1 \ No newline at end of file +paddlenlp>=2.2.1 +pypandoc +attrdict +python_docx \ No newline at end of file diff --git a/ppstructure/vqa/tools/trans_funsd_label.py b/ppstructure/vqa/tools/trans_funsd_label.py new file mode 100644 index 0000000000000000000000000000000000000000..ef7d1db010a925b37d285befe77aa202db2141d9 --- /dev/null +++ b/ppstructure/vqa/tools/trans_funsd_label.py @@ -0,0 +1,151 @@ +# copyright (c) 2022 PaddlePaddle Authors. All Rights Reserve. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import json +import os +import sys +import cv2 +import numpy as np +from copy import deepcopy + + +def trans_poly_to_bbox(poly): + x1 = np.min([p[0] for p in poly]) + x2 = np.max([p[0] for p in poly]) + y1 = np.min([p[1] for p in poly]) + y2 = np.max([p[1] for p in poly]) + return [x1, y1, x2, y2] + + +def get_outer_poly(bbox_list): + x1 = min([bbox[0] for bbox in bbox_list]) + y1 = min([bbox[1] for bbox in bbox_list]) + x2 = max([bbox[2] for bbox in bbox_list]) + y2 = max([bbox[3] for bbox in bbox_list]) + return [[x1, y1], [x2, y1], [x2, y2], [x1, y2]] + + +def load_funsd_label(image_dir, anno_dir): + imgs = os.listdir(image_dir) + annos = os.listdir(anno_dir) + + imgs = [img.replace(".png", "") for img in imgs] + annos = [anno.replace(".json", "") for anno in annos] + + fn_info_map = dict() + for anno_fn in annos: + res = [] + with open(os.path.join(anno_dir, anno_fn + ".json"), "r") as fin: + infos = json.load(fin) + infos = infos["form"] + old_id2new_id_map = dict() + global_new_id = 0 + for info in infos: + if info["text"] is None: + continue + words = info["words"] + if len(words) <= 0: + continue + word_idx = 1 + curr_bboxes = [words[0]["box"]] + curr_texts = [words[0]["text"]] + while word_idx < len(words): + # switch to a new link + if words[word_idx]["box"][0] + 10 <= words[word_idx - 1][ + "box"][2]: + if len("".join(curr_texts[0])) > 0: + res.append({ + "transcription": " ".join(curr_texts), + "label": info["label"], + "points": get_outer_poly(curr_bboxes), + "linking": info["linking"], + "id": global_new_id, + }) + if info["id"] not in old_id2new_id_map: + old_id2new_id_map[info["id"]] = [] + old_id2new_id_map[info["id"]].append(global_new_id) + global_new_id += 1 + curr_bboxes = [words[word_idx]["box"]] + curr_texts = [words[word_idx]["text"]] + else: + curr_bboxes.append(words[word_idx]["box"]) + curr_texts.append(words[word_idx]["text"]) + word_idx += 1 + if len("".join(curr_texts[0])) > 0: + res.append({ + "transcription": " ".join(curr_texts), + "label": info["label"], + "points": get_outer_poly(curr_bboxes), + "linking": info["linking"], + "id": global_new_id, + }) + if info["id"] not in old_id2new_id_map: + old_id2new_id_map[info["id"]] = [] + old_id2new_id_map[info["id"]].append(global_new_id) + global_new_id += 1 + res = sorted( + res, key=lambda r: (r["points"][0][1], r["points"][0][0])) + for i in range(len(res) - 1): + for j in range(i, 0, -1): + if abs(res[j + 1]["points"][0][1] - res[j]["points"][0][1]) < 20 and \ + (res[j + 1]["points"][0][0] < res[j]["points"][0][0]): + tmp = deepcopy(res[j]) + res[j] = deepcopy(res[j + 1]) + res[j + 1] = deepcopy(tmp) + else: + break + # re-generate unique ids + for idx, r in enumerate(res): + new_links = [] + for link in r["linking"]: + # illegal links will be removed + if link[0] not in old_id2new_id_map or link[ + 1] not in old_id2new_id_map: + continue + for src in old_id2new_id_map[link[0]]: + for dst in old_id2new_id_map[link[1]]: + new_links.append([src, dst]) + res[idx]["linking"] = deepcopy(new_links) + + fn_info_map[anno_fn] = res + + return fn_info_map + + +def main(): + test_image_dir = "train_data/FUNSD/testing_data/images/" + test_anno_dir = "train_data/FUNSD/testing_data/annotations/" + test_output_dir = "train_data/FUNSD/test.json" + + fn_info_map = load_funsd_label(test_image_dir, test_anno_dir) + with open(test_output_dir, "w") as fout: + for fn in fn_info_map: + fout.write(fn + ".png" + "\t" + json.dumps( + fn_info_map[fn], ensure_ascii=False) + "\n") + + train_image_dir = "train_data/FUNSD/training_data/images/" + train_anno_dir = "train_data/FUNSD/training_data/annotations/" + train_output_dir = "train_data/FUNSD/train.json" + + fn_info_map = load_funsd_label(train_image_dir, train_anno_dir) + with open(train_output_dir, "w") as fout: + for fn in fn_info_map: + fout.write(fn + ".png" + "\t" + json.dumps( + fn_info_map[fn], ensure_ascii=False) + "\n") + print("====ok====") + return + + +if __name__ == "__main__": + main() diff --git a/ppstructure/vqa/tools/trans_xfun_data.py b/ppstructure/vqa/tools/trans_xfun_data.py index 93ec98163c6cec96ec93399c1d41524200ddc499..11d221bea40367f091b3e09dde42e87f2217a617 100644 --- a/ppstructure/vqa/tools/trans_xfun_data.py +++ b/ppstructure/vqa/tools/trans_xfun_data.py @@ -21,26 +21,22 @@ def transfer_xfun_data(json_path=None, output_file=None): json_info = json.loads(lines[0]) documents = json_info["documents"] - label_info = {} with open(output_file, "w", encoding='utf-8') as fout: for idx, document in enumerate(documents): + label_info = [] img_info = document["img"] document = document["document"] image_path = img_info["fname"] - label_info["height"] = img_info["height"] - label_info["width"] = img_info["width"] - - label_info["ocr_info"] = [] - for doc in document: - label_info["ocr_info"].append({ - "text": doc["text"], + x1, y1, x2, y2 = doc["box"] + points = [[x1, y1], [x2, y1], [x2, y2], [x1, y2]] + label_info.append({ + "transcription": doc["text"], "label": doc["label"], - "bbox": doc["box"], + "points": points, "id": doc["id"], - "linking": doc["linking"], - "words": doc["words"] + "linking": doc["linking"] }) fout.write(image_path + "\t" + json.dumps( diff --git a/test_tipc/benchmark_train.sh b/test_tipc/benchmark_train.sh index e3e4d627fa27f3a34ae0ae47a8613d6ec0a0f60e..1dcb0129e767e6c35adfad36aa5dce2fbd84a2fd 100644 --- a/test_tipc/benchmark_train.sh +++ b/test_tipc/benchmark_train.sh @@ -21,6 +21,18 @@ function func_parser_params(){ echo ${tmp} } +function set_dynamic_epoch(){ + string=$1 + num=$2 + _str=${string:1:6} + IFS="C" + arr=(${_str}) + M=${arr[0]} + P=${arr[1]} + ep=`expr $num \* $M \* $P` + echo $ep +} + function func_sed_params(){ filename=$1 line=$2 @@ -139,10 +151,11 @@ else device_num=${params_list[4]} IFS=";" - if [ ${precision} = "null" ];then - precision="fp32" + if [ ${precision} = "fp16" ];then + precision="amp" fi + epoch=$(set_dynamic_epoch $device_num $epoch) fp_items_list=($precision) batch_size_list=($batch_size) device_num_list=($device_num) @@ -150,10 +163,16 @@ fi IFS="|" for batch_size in ${batch_size_list[*]}; do - for precision in ${fp_items_list[*]}; do + for train_precision in ${fp_items_list[*]}; do for device_num in ${device_num_list[*]}; do # sed batchsize and precision - func_sed_params "$FILENAME" "${line_precision}" "$precision" + if [ ${train_precision} = "amp" ];then + precision="fp16" + else + precision="fp32" + fi + + func_sed_params "$FILENAME" "${line_precision}" "$train_precision" func_sed_params "$FILENAME" "${line_batchsize}" "$MODE=$batch_size" func_sed_params "$FILENAME" "${line_epoch}" "$MODE=$epoch" gpu_id=$(set_gpu_id $device_num) diff --git a/test_tipc/configs/ch_PP-OCRv2_det/train_infer_python.txt b/test_tipc/configs/ch_PP-OCRv2_det/train_infer_python.txt index cab0cb0aa390c7cb1efa6e5d3bc636e9c974acba..7a20df7f6c1e2eeee19f55cefa3320d34a62a701 100644 --- a/test_tipc/configs/ch_PP-OCRv2_det/train_infer_python.txt +++ b/test_tipc/configs/ch_PP-OCRv2_det/train_infer_python.txt @@ -13,7 +13,7 @@ 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_PP-OCRv2/ch_PP-OCRv2_det_cml.yml -o +norm_train:tools/train.py -c configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_cml.yml -o Global.print_batch_step=1 Train.loader.shuffle=false pact_train:null fpgm_train:null distill_train:null @@ -51,3 +51,9 @@ null:null null:null ===========================infer_benchmark_params========================== random_infer_input:[{float32,[3,640,640]}];[{float32,[3,960,960]}] +===========================train_benchmark_params========================== +batch_size:8 +fp_items:fp32|fp16 +epoch:2 +--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile +flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096 diff --git 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 index 6c63af6ae8d62e098dec35d5918291291f654e32..3eb82d42bc3f2b3ca7420d999865977bbad09e31 100644 --- 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 @@ -6,7 +6,7 @@ Global: print_batch_step: 10 save_model_dir: ./output/rec_pp-OCRv2_distillation save_epoch_step: 3 - eval_batch_step: [0, 2000] + eval_batch_step: [0, 200000] cal_metric_during_train: true pretrained_model: checkpoints: diff --git a/test_tipc/configs/ch_PP-OCRv2_rec/train_infer_python.txt b/test_tipc/configs/ch_PP-OCRv2_rec/train_infer_python.txt index df42b342ba5fa3947a69c2bde5548975ca92d857..a96b87dede1e1b4c7b3ed59c4bd9c0470402e7e2 100644 --- a/test_tipc/configs/ch_PP-OCRv2_rec/train_infer_python.txt +++ b/test_tipc/configs/ch_PP-OCRv2_rec/train_infer_python.txt @@ -13,7 +13,7 @@ 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 +norm_train:tools/train.py -c test_tipc/configs/ch_PP-OCRv2_rec/ch_PP-OCRv2_rec_distillation.yml -o Global.print_batch_step=4 Train.loader.shuffle=false pact_train:null fpgm_train:null distill_train:null @@ -51,3 +51,9 @@ null:null null:null ===========================infer_benchmark_params========================== random_infer_input:[{float32,[3,32,320]}] +===========================train_benchmark_params========================== +batch_size:64 +fp_items:fp32|fp16 +epoch:1 +--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile +flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096 diff --git a/test_tipc/configs/ch_PP-OCRv3_det/train_infer_python.txt b/test_tipc/configs/ch_PP-OCRv3_det/train_infer_python.txt index a69e0ab81ec6963228e9ab2e39c5bb1d730b6323..bf10aebe3e9aa67e30ce7a20cb07f376825e39ae 100644 --- a/test_tipc/configs/ch_PP-OCRv3_det/train_infer_python.txt +++ b/test_tipc/configs/ch_PP-OCRv3_det/train_infer_python.txt @@ -13,7 +13,7 @@ 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_PP-OCRv3/ch_PP-OCRv3_det_cml.yml -o +norm_train:tools/train.py -c configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml -o Global.print_batch_step=1 Train.loader.shuffle=false Global.eval_batch_step=[4000,400] pact_train:null fpgm_train:null distill_train:null @@ -51,3 +51,9 @@ null:null null:null ===========================infer_benchmark_params========================== random_infer_input:[{float32,[3,640,640]}];[{float32,[3,960,960]}] +===========================train_benchmark_params========================== +batch_size:8 +fp_items:fp32|fp16 +epoch:2 +--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile +flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096 diff --git a/test_tipc/configs/ch_PP-OCRv3_rec/ch_PP-OCRv3_rec_distillation.yml b/test_tipc/configs/ch_PP-OCRv3_rec/ch_PP-OCRv3_rec_distillation.yml index f704a1dfb5dc2335c353a495dfbc0ce42cf35bf4..4c8ba0a6fa4a355e9bad1665a8de82399f919740 100644 --- a/test_tipc/configs/ch_PP-OCRv3_rec/ch_PP-OCRv3_rec_distillation.yml +++ b/test_tipc/configs/ch_PP-OCRv3_rec/ch_PP-OCRv3_rec_distillation.yml @@ -153,7 +153,7 @@ Train: data_dir: ./train_data/ic15_data/ ext_op_transform_idx: 1 label_file_list: - - ./train_data/ic15_data/rec_gt_train_lite.txt + - ./train_data/ic15_data/rec_gt_train.txt transforms: - DecodeImage: img_mode: BGR @@ -183,7 +183,7 @@ Eval: name: SimpleDataSet data_dir: ./train_data/ic15_data label_file_list: - - ./train_data/ic15_data/rec_gt_test_lite.txt + - ./train_data/ic15_data/rec_gt_test.txt transforms: - DecodeImage: img_mode: BGR diff --git a/test_tipc/configs/ch_PP-OCRv3_rec/train_infer_python.txt b/test_tipc/configs/ch_PP-OCRv3_rec/train_infer_python.txt index 1feb9d49fce69d92ef141c3a942f858fc68cfaab..59fc1bd4160ec77edb0b781c8ffa9845c6a3d5c7 100644 --- a/test_tipc/configs/ch_PP-OCRv3_rec/train_infer_python.txt +++ b/test_tipc/configs/ch_PP-OCRv3_rec/train_infer_python.txt @@ -13,7 +13,7 @@ train_infer_img_dir:./inference/rec_inference null:null ## trainer:norm_train -norm_train:tools/train.py -c test_tipc/configs/ch_PP-OCRv3_rec/ch_PP-OCRv3_rec_distillation.yml -o +norm_train:tools/train.py -c test_tipc/configs/ch_PP-OCRv3_rec/ch_PP-OCRv3_rec_distillation.yml -o Global.print_batch_step=1 Train.loader.shuffle=false pact_train:null fpgm_train:null distill_train:null @@ -51,3 +51,10 @@ null:null null:null ===========================infer_benchmark_params========================== random_infer_input:[{float32,[3,48,320]}] +===========================train_benchmark_params========================== +batch_size:64 +fp_items:fp32|fp16 +epoch:1 +--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile +flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096 + diff --git a/test_tipc/configs/ch_ppocr_mobile_v2.0/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt b/test_tipc/configs/ch_ppocr_mobile_v2_0/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt similarity index 91% rename from test_tipc/configs/ch_ppocr_mobile_v2.0/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt rename to test_tipc/configs/ch_ppocr_mobile_v2_0/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt index b42ab9db362b0ba56d795096fdc58a645b425480..73f1d498550b2672078a9353d665fe825a992cec 100644 --- a/test_tipc/configs/ch_ppocr_mobile_v2.0/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt +++ b/test_tipc/configs/ch_ppocr_mobile_v2_0/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt @@ -1,5 +1,5 @@ ===========================cpp_infer_params=========================== -model_name:ch_ppocr_mobile_v2.0 +model_name:ch_ppocr_mobile_v2_0 use_opencv:True infer_model:./inference/ch_ppocr_mobile_v2.0_det_infer/ infer_quant:False diff --git 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 similarity index 94% rename from test_tipc/configs/ch_ppocr_mobile_v2.0/model_linux_gpu_normal_normal_infer_python_linux_gpu_cpu.txt rename to test_tipc/configs/ch_ppocr_mobile_v2_0/model_linux_gpu_normal_normal_infer_python_linux_gpu_cpu.txt index becad991eab2535b2df7862d0d25707ef37f08f8..00373b61eef1e3aecf6f55d945b528ceb1d83a8b 100644 --- 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 @@ -1,5 +1,5 @@ ===========================ch_ppocr_mobile_v2.0=========================== -model_name: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 diff --git 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 similarity index 100% rename from test_tipc/configs/ch_ppocr_mobile_v2.0/model_linux_gpu_normal_normal_lite_cpp_arm_cpu.txt rename to test_tipc/configs/ch_ppocr_mobile_v2_0/model_linux_gpu_normal_normal_lite_cpp_arm_cpu.txt diff --git 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 similarity index 100% rename from test_tipc/configs/ch_ppocr_mobile_v2.0/model_linux_gpu_normal_normal_lite_cpp_arm_gpu_opencl.txt rename to test_tipc/configs/ch_ppocr_mobile_v2_0/model_linux_gpu_normal_normal_lite_cpp_arm_gpu_opencl.txt diff --git a/test_tipc/configs/ch_ppocr_mobile_v2.0/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt b/test_tipc/configs/ch_ppocr_mobile_v2_0/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt similarity index 87% rename from test_tipc/configs/ch_ppocr_mobile_v2.0/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt rename to test_tipc/configs/ch_ppocr_mobile_v2_0/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt index 17c2fbbae2e182c4a7631cb18908180d8c019b4f..3e01ae57380f99fd2eb637b08cb00ab81fd2e966 100644 --- a/test_tipc/configs/ch_ppocr_mobile_v2.0/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt +++ b/test_tipc/configs/ch_ppocr_mobile_v2_0/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt @@ -1,5 +1,5 @@ ===========================paddle2onnx_params=========================== -model_name:ch_ppocr_mobile_v2.0 +model_name:ch_ppocr_mobile_v2_0 python:python3.7 2onnx: paddle2onnx --det_model_dir:./inference/ch_ppocr_mobile_v2.0_det_infer/ diff --git a/test_tipc/configs/ch_ppocr_mobile_v2.0/model_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt b/test_tipc/configs/ch_ppocr_mobile_v2_0/model_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt similarity index 96% rename from test_tipc/configs/ch_ppocr_mobile_v2.0/model_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt rename to test_tipc/configs/ch_ppocr_mobile_v2_0/model_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt index d18e9f11fdd2ff605cdd8f6c1bcf51ca780eb766..305882aa30f0009bb109e74d1668ded8aa00ccfb 100644 --- a/test_tipc/configs/ch_ppocr_mobile_v2.0/model_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt +++ b/test_tipc/configs/ch_ppocr_mobile_v2_0/model_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt @@ -1,5 +1,5 @@ ===========================serving_params=========================== -model_name:ch_ppocr_mobile_v2.0 +model_name:ch_ppocr_mobile_v2_0 python:python3.7 trans_model:-m paddle_serving_client.convert --det_dirname:./inference/ch_ppocr_mobile_v2.0_det_infer/ diff --git a/test_tipc/configs/ch_ppocr_mobile_v2.0/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt b/test_tipc/configs/ch_ppocr_mobile_v2_0/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt similarity index 97% rename from test_tipc/configs/ch_ppocr_mobile_v2.0/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt rename to test_tipc/configs/ch_ppocr_mobile_v2_0/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt index 842c9340176d696c1e43e59491bdcab817f9256e..0c366b03ded2e3a7e038d21f2b482a76131e6a21 100644 --- a/test_tipc/configs/ch_ppocr_mobile_v2.0/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt +++ b/test_tipc/configs/ch_ppocr_mobile_v2_0/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt @@ -1,5 +1,5 @@ ===========================serving_params=========================== -model_name:ch_ppocr_mobile_v2.0 +model_name:ch_ppocr_mobile_v2_0 python:python3.7 trans_model:-m paddle_serving_client.convert --det_dirname:./inference/ch_ppocr_mobile_v2.0_det_infer/ diff --git a/test_tipc/configs/ch_ppocr_mobile_v2.0_det/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt b/test_tipc/configs/ch_ppocr_mobile_v2_0_det/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt similarity index 88% rename from test_tipc/configs/ch_ppocr_mobile_v2.0_det/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt rename to test_tipc/configs/ch_ppocr_mobile_v2_0_det/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt index 1d1c2ae283b2103c2e7282186ab1f53bec05cda3..ded332e674e76ef10a2b312783f2b1bbbbf963a6 100644 --- a/test_tipc/configs/ch_ppocr_mobile_v2.0_det/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt +++ b/test_tipc/configs/ch_ppocr_mobile_v2_0_det/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt @@ -1,5 +1,5 @@ ===========================cpp_infer_params=========================== -model_name:ch_ppocr_mobile_v2.0_det +model_name:ch_ppocr_mobile_v2_0_det use_opencv:True infer_model:./inference/ch_ppocr_mobile_v2.0_det_infer/ infer_quant:False diff --git a/test_tipc/configs/ch_ppocr_mobile_v2.0_det/model_linux_gpu_normal_normal_infer_python_jetson.txt b/test_tipc/configs/ch_ppocr_mobile_v2_0_det/model_linux_gpu_normal_normal_infer_python_jetson.txt similarity index 92% rename from test_tipc/configs/ch_ppocr_mobile_v2.0_det/model_linux_gpu_normal_normal_infer_python_jetson.txt rename to test_tipc/configs/ch_ppocr_mobile_v2_0_det/model_linux_gpu_normal_normal_infer_python_jetson.txt index 24bb8746ab7793dbcb4af99102a007aca8b8e16b..5f9dfa5f55e4556036f45fdef4fa8e6edf8b3eb9 100644 --- a/test_tipc/configs/ch_ppocr_mobile_v2.0_det/model_linux_gpu_normal_normal_infer_python_jetson.txt +++ b/test_tipc/configs/ch_ppocr_mobile_v2_0_det/model_linux_gpu_normal_normal_infer_python_jetson.txt @@ -1,5 +1,5 @@ ===========================infer_params=========================== -model_name:ch_ppocr_mobile_v2.0_det +model_name:ch_ppocr_mobile_v2_0_det python:python infer_model:./inference/ch_ppocr_mobile_v2.0_det_infer infer_export:null diff --git a/test_tipc/configs/ch_ppocr_mobile_v2.0_det/model_linux_gpu_normal_normal_lite_cpp_arm_cpu.txt b/test_tipc/configs/ch_ppocr_mobile_v2_0_det/model_linux_gpu_normal_normal_lite_cpp_arm_cpu.txt similarity index 100% rename from test_tipc/configs/ch_ppocr_mobile_v2.0_det/model_linux_gpu_normal_normal_lite_cpp_arm_cpu.txt rename to test_tipc/configs/ch_ppocr_mobile_v2_0_det/model_linux_gpu_normal_normal_lite_cpp_arm_cpu.txt diff --git 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 similarity index 100% rename from test_tipc/configs/ch_ppocr_mobile_v2.0_det/model_linux_gpu_normal_normal_lite_cpp_arm_gpu_opencl.txt rename to test_tipc/configs/ch_ppocr_mobile_v2_0_det/model_linux_gpu_normal_normal_lite_cpp_arm_gpu_opencl.txt diff --git a/test_tipc/configs/ch_ppocr_mobile_v2.0_det/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt b/test_tipc/configs/ch_ppocr_mobile_v2_0_det/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt similarity index 84% rename from test_tipc/configs/ch_ppocr_mobile_v2.0_det/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt rename to test_tipc/configs/ch_ppocr_mobile_v2_0_det/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt index 00473d1062615834a42e350a727f50233efd831f..8f36ad4b86fc200d558e905e89e7d3594b2f13f1 100644 --- a/test_tipc/configs/ch_ppocr_mobile_v2.0_det/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt +++ b/test_tipc/configs/ch_ppocr_mobile_v2_0_det/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt @@ -1,5 +1,5 @@ ===========================paddle2onnx_params=========================== -model_name:ch_ppocr_mobile_v2.0_det +model_name:ch_ppocr_mobile_v2_0_det python:python3.7 2onnx: paddle2onnx --det_model_dir:./inference/ch_ppocr_mobile_v2.0_det_infer/ diff --git a/test_tipc/configs/ch_ppocr_mobile_v2.0_det/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt b/test_tipc/configs/ch_ppocr_mobile_v2_0_det/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt similarity index 96% rename from test_tipc/configs/ch_ppocr_mobile_v2.0_det/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt rename to test_tipc/configs/ch_ppocr_mobile_v2_0_det/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt index c9dd5ad920d58f60ce36a7b489073279f23ba1b7..6dfd7e7bd02e587dd2dc895d1e206b10c17fe82f 100644 --- a/test_tipc/configs/ch_ppocr_mobile_v2.0_det/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt +++ b/test_tipc/configs/ch_ppocr_mobile_v2_0_det/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt @@ -1,5 +1,5 @@ ===========================serving_params=========================== -model_name:ch_ppocr_mobile_v2.0_det +model_name:ch_ppocr_mobile_v2_0_det python:python3.7 trans_model:-m paddle_serving_client.convert --det_dirname:./inference/ch_ppocr_mobile_v2.0_det_infer/ diff --git a/test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_infer_python.txt b/test_tipc/configs/ch_ppocr_mobile_v2_0_det/train_infer_python.txt similarity index 78% rename from test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_infer_python.txt rename to test_tipc/configs/ch_ppocr_mobile_v2_0_det/train_infer_python.txt index 789ed4d23d9c1fa3997daceee0627218aecd4c73..f3aa9d0f8218a24b11e3d0d079ae79a07d3e5874 100644 --- a/test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_infer_python.txt +++ b/test_tipc/configs/ch_ppocr_mobile_v2_0_det/train_infer_python.txt @@ -1,5 +1,5 @@ ===========================train_params=========================== -model_name:ch_ppocr_mobile_v2.0_det +model_name:ch_ppocr_mobile_v2_0_det python:python3.7 gpu_list:0|0,1 Global.use_gpu:True|True @@ -13,7 +13,7 @@ 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 +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 Global.print_batch_step=1 Train.loader.shuffle=false pact_train:null fpgm_train:null distill_train:null @@ -50,4 +50,10 @@ null:null --benchmark:True null:null ===========================infer_benchmark_params========================== -random_infer_input:[{float32,[3,640,640]}];[{float32,[3,960,960]}] \ No newline at end of file +random_infer_input:[{float32,[3,640,640]}];[{float32,[3,960,960]}] +===========================train_benchmark_params========================== +batch_size:8 +fp_items:fp32|fp16 +epoch:2 +--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile +flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096 diff --git a/test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_linux_dcu_normal_normal_infer_python_dcu.txt b/test_tipc/configs/ch_ppocr_mobile_v2_0_det/train_linux_dcu_normal_normal_infer_python_dcu.txt similarity index 100% rename from test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_linux_dcu_normal_normal_infer_python_dcu.txt rename to test_tipc/configs/ch_ppocr_mobile_v2_0_det/train_linux_dcu_normal_normal_infer_python_dcu.txt diff --git a/test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_linux_gpu_fleet_normal_infer_python_linux_gpu_cpu.txt b/test_tipc/configs/ch_ppocr_mobile_v2_0_det/train_linux_gpu_fleet_normal_infer_python_linux_gpu_cpu.txt similarity index 97% rename from test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_linux_gpu_fleet_normal_infer_python_linux_gpu_cpu.txt rename to test_tipc/configs/ch_ppocr_mobile_v2_0_det/train_linux_gpu_fleet_normal_infer_python_linux_gpu_cpu.txt index 5271f78bb778f9e419da7f9bbbb6b4a6fafb305b..bf81d0baa8fa8c8ae590e5bbf33564fdf664b9c5 100644 --- a/test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_linux_gpu_fleet_normal_infer_python_linux_gpu_cpu.txt +++ b/test_tipc/configs/ch_ppocr_mobile_v2_0_det/train_linux_gpu_fleet_normal_infer_python_linux_gpu_cpu.txt @@ -1,5 +1,5 @@ ===========================train_params=========================== -model_name:ch_ppocr_mobile_v2.0_det +model_name:ch_ppocr_mobile_v2_0_det python:python3.7 gpu_list:192.168.0.1,192.168.0.2;0,1 Global.use_gpu:True diff --git a/test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt b/test_tipc/configs/ch_ppocr_mobile_v2_0_det/train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt similarity index 97% rename from test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt rename to test_tipc/configs/ch_ppocr_mobile_v2_0_det/train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt index 6b3352f741a56124eead2f71c03c783e5c81a70d..df71e907022a8f4a262eb218879578b99464327a 100644 --- a/test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt +++ b/test_tipc/configs/ch_ppocr_mobile_v2_0_det/train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt @@ -1,5 +1,5 @@ ===========================train_params=========================== -model_name:ch_ppocr_mobile_v2.0_det +model_name:ch_ppocr_mobile_v2_0_det python:python3.7 gpu_list:0|0,1 Global.use_gpu:True|True diff --git 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 similarity index 100% rename from test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_mac_cpu_normal_normal_infer_python_mac_cpu.txt rename to test_tipc/configs/ch_ppocr_mobile_v2_0_det/train_mac_cpu_normal_normal_infer_python_mac_cpu.txt diff --git a/test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_pact_infer_python.txt b/test_tipc/configs/ch_ppocr_mobile_v2_0_det/train_pact_infer_python.txt similarity index 97% rename from test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_pact_infer_python.txt rename to test_tipc/configs/ch_ppocr_mobile_v2_0_det/train_pact_infer_python.txt index 04c8d0e194b687f58da1c449a6a0d8d9c1acd25e..ba880d1f9e1a1ade250c25581c8b238ec58c5e30 100644 --- a/test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_pact_infer_python.txt +++ b/test_tipc/configs/ch_ppocr_mobile_v2_0_det/train_pact_infer_python.txt @@ -1,5 +1,5 @@ ===========================train_params=========================== -model_name:ch_ppocr_mobile_v2.0_det_PACT +model_name:ch_ppocr_mobile_v2_0_det_PACT python:python3.7 gpu_list:0|0,1 Global.use_gpu:True|True diff --git a/test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_ptq_infer_python.txt b/test_tipc/configs/ch_ppocr_mobile_v2_0_det/train_ptq_infer_python.txt similarity index 93% rename from test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_ptq_infer_python.txt rename to test_tipc/configs/ch_ppocr_mobile_v2_0_det/train_ptq_infer_python.txt index 2bdec848833b6cf3799370b0337fa00f185a94d5..45c4fd1ae832ab86c94bc04f9acf89ef8e95d09e 100644 --- a/test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_ptq_infer_python.txt +++ b/test_tipc/configs/ch_ppocr_mobile_v2_0_det/train_ptq_infer_python.txt @@ -1,5 +1,5 @@ ===========================kl_quant_params=========================== -model_name:ch_ppocr_mobile_v2.0_det_KL +model_name:ch_ppocr_mobile_v2_0_det_KL python:python3.7 Global.pretrained_model:null Global.save_inference_dir:null diff --git 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 similarity index 100% rename from test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_windows_gpu_normal_normal_infer_python_windows_cpu_gpu.txt rename to test_tipc/configs/ch_ppocr_mobile_v2_0_det/train_windows_gpu_normal_normal_infer_python_windows_cpu_gpu.txt diff --git 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 similarity index 97% rename from test_tipc/configs/ch_ppocr_mobile_v2.0_det_FPGM/train_infer_python.txt rename to test_tipc/configs/ch_ppocr_mobile_v2_0_det_FPGM/train_infer_python.txt index dae3f8053a0264611b5baca0f45839f3550fe6a4..0f6df1ac52c8b42d21ae701634337113e2efab95 100644 --- 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 @@ -1,5 +1,5 @@ ===========================train_params=========================== -model_name:ch_ppocr_mobile_v2.0_det_FPGM +model_name:ch_ppocr_mobile_v2_0_det_FPGM python:python3.7 gpu_list:0|0,1 Global.use_gpu:True|True diff --git a/test_tipc/configs/ch_ppocr_mobile_v2.0_det_FPGM/train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt b/test_tipc/configs/ch_ppocr_mobile_v2_0_det_FPGM/train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt similarity index 97% rename from test_tipc/configs/ch_ppocr_mobile_v2.0_det_FPGM/train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt rename to test_tipc/configs/ch_ppocr_mobile_v2_0_det_FPGM/train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt index 150a8a0315b83e8c62765a4aa66429cfd0590928..2014c6dbcd0f1c4e97348452b520c0e640245fe5 100644 --- a/test_tipc/configs/ch_ppocr_mobile_v2.0_det_FPGM/train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt +++ b/test_tipc/configs/ch_ppocr_mobile_v2_0_det_FPGM/train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt @@ -1,5 +1,5 @@ ===========================train_params=========================== -model_name:ch_ppocr_mobile_v2.0_det_FPGM +model_name:ch_ppocr_mobile_v2_0_det_FPGM python:python3.7 gpu_list:0|0,1 Global.use_gpu:True|True diff --git a/test_tipc/configs/ch_ppocr_mobile_v2.0_det_KL/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt b/test_tipc/configs/ch_ppocr_mobile_v2_0_det_KL/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt similarity index 88% rename from test_tipc/configs/ch_ppocr_mobile_v2.0_det_KL/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt rename to test_tipc/configs/ch_ppocr_mobile_v2_0_det_KL/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt index eb2fd0a001ab506f241bed7eac75d96cf4b5d5cb..f0e58dd566ac62a598930d01d7604e37273b99fa 100644 --- a/test_tipc/configs/ch_ppocr_mobile_v2.0_det_KL/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt +++ b/test_tipc/configs/ch_ppocr_mobile_v2_0_det_KL/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt @@ -1,5 +1,5 @@ ===========================cpp_infer_params=========================== -model_name:ch_ppocr_mobile_v2.0_det_KL +model_name:ch_ppocr_mobile_v2_0_det_KL use_opencv:True infer_model:./inference/ch_ppocr_mobile_v2.0_det_klquant_infer infer_quant:False diff --git 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 similarity index 100% rename from test_tipc/configs/ch_ppocr_mobile_v2.0_det_KL/model_linux_gpu_normal_normal_infer_python_mac_cpu.txt rename to test_tipc/configs/ch_ppocr_mobile_v2_0_det_KL/model_linux_gpu_normal_normal_infer_python_mac_cpu.txt diff --git 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 similarity index 100% rename from test_tipc/configs/ch_ppocr_mobile_v2.0_det_KL/model_linux_gpu_normal_normal_infer_python_windows_gpu_cpu.txt rename to test_tipc/configs/ch_ppocr_mobile_v2_0_det_KL/model_linux_gpu_normal_normal_infer_python_windows_gpu_cpu.txt diff --git a/test_tipc/configs/ch_ppocr_mobile_v2.0_rec_KL/model_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt b/test_tipc/configs/ch_ppocr_mobile_v2_0_det_KL/model_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt similarity index 95% rename from test_tipc/configs/ch_ppocr_mobile_v2.0_rec_KL/model_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt rename to test_tipc/configs/ch_ppocr_mobile_v2_0_det_KL/model_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt index ab518de55ae6b157b26bf332ec3b0afcab71f97a..c5dc52583bb2ff293f6c8f2f49c55e5e1dffaefc 100644 --- a/test_tipc/configs/ch_ppocr_mobile_v2.0_rec_KL/model_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt +++ b/test_tipc/configs/ch_ppocr_mobile_v2_0_det_KL/model_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt @@ -1,5 +1,5 @@ ===========================serving_params=========================== -model_name:ch_ppocr_mobile_v2.0_rec_KL +model_name:ch_ppocr_mobile_v2_0_det_KL python:python3.7 trans_model:-m paddle_serving_client.convert --det_dirname:./inference/ch_ppocr_mobile_v2.0_det_klquant_infer/ diff --git a/test_tipc/configs/ch_ppocr_mobile_v2.0_det_KL/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt b/test_tipc/configs/ch_ppocr_mobile_v2_0_det_KL/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt similarity index 96% rename from test_tipc/configs/ch_ppocr_mobile_v2.0_det_KL/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt rename to test_tipc/configs/ch_ppocr_mobile_v2_0_det_KL/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt index 049ec784581bddcce066bc049b66f6f0ceff9eed..82d4db32ab1c49c3d433e9efdc2d91df4c434cea 100644 --- a/test_tipc/configs/ch_ppocr_mobile_v2.0_det_KL/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt +++ b/test_tipc/configs/ch_ppocr_mobile_v2_0_det_KL/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt @@ -1,5 +1,5 @@ ===========================serving_params=========================== -model_name:ch_ppocr_mobile_v2.0_det_KL +model_name:ch_ppocr_mobile_v2_0_det_KL python:python3.7 trans_model:-m paddle_serving_client.convert --det_dirname:./inference/ch_ppocr_mobile_v2.0_det_klquant_infer/ diff --git a/test_tipc/configs/ch_ppocr_mobile_v2.0_det_PACT/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt b/test_tipc/configs/ch_ppocr_mobile_v2_0_det_PACT/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt similarity index 87% rename from test_tipc/configs/ch_ppocr_mobile_v2.0_det_PACT/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt rename to test_tipc/configs/ch_ppocr_mobile_v2_0_det_PACT/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt index 17723f41ab762f5316cba59c08ec719aa54f03b1..5132330597ac5b9a120f1c1a4bec7bc8fb849f38 100644 --- a/test_tipc/configs/ch_ppocr_mobile_v2.0_det_PACT/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt +++ b/test_tipc/configs/ch_ppocr_mobile_v2_0_det_PACT/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt @@ -1,5 +1,5 @@ ===========================cpp_infer_params=========================== -model_name:ch_ppocr_mobile_v2.0_det_PACT +model_name:ch_ppocr_mobile_v2_0_det_PACT use_opencv:True infer_model:./inference/ch_ppocr_mobile_v2.0_det_pact_infer infer_quant:False diff --git a/test_tipc/configs/ch_ppocr_mobile_v2.0_rec_PACT/model_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt b/test_tipc/configs/ch_ppocr_mobile_v2_0_det_PACT/model_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt similarity index 95% rename from test_tipc/configs/ch_ppocr_mobile_v2.0_rec_PACT/model_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt rename to test_tipc/configs/ch_ppocr_mobile_v2_0_det_PACT/model_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt index 229f70cf353318bf9ccc81f4e5be79dbc096de25..3be53952f26bd17a6f4262b64f0a5958a27a1fab 100644 --- a/test_tipc/configs/ch_ppocr_mobile_v2.0_rec_PACT/model_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt +++ b/test_tipc/configs/ch_ppocr_mobile_v2_0_det_PACT/model_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt @@ -1,5 +1,5 @@ ===========================serving_params=========================== -model_name:ch_ppocr_mobile_v2.0_rec_PACT +model_name:ch_ppocr_mobile_v2_0_det_PACT python:python3.7 trans_model:-m paddle_serving_client.convert --det_dirname:./inference/ch_ppocr_mobile_v2.0_det_pact_infer/ diff --git a/test_tipc/configs/ch_ppocr_mobile_v2.0_det_PACT/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt b/test_tipc/configs/ch_ppocr_mobile_v2_0_det_PACT/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt similarity index 95% rename from test_tipc/configs/ch_ppocr_mobile_v2.0_det_PACT/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt rename to test_tipc/configs/ch_ppocr_mobile_v2_0_det_PACT/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt index 909d738919bed78d6db04e238818cd4fbbb75e5f..63e7f8f735afc99a60f211d9e0f2ff67c4a2a89e 100644 --- a/test_tipc/configs/ch_ppocr_mobile_v2.0_det_PACT/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt +++ b/test_tipc/configs/ch_ppocr_mobile_v2_0_det_PACT/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt @@ -1,5 +1,5 @@ ===========================serving_params=========================== -model_name:ch_ppocr_mobile_v2.0_det_PACT +model_name:ch_ppocr_mobile_v2_0_det_PACT python:python3.7 trans_model:-m paddle_serving_client.convert --det_dirname:./inference/ch_ppocr_mobile_v2.0_det_pact_infer/ diff --git a/test_tipc/configs/ch_ppocr_mobile_v2.0_rec/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt b/test_tipc/configs/ch_ppocr_mobile_v2_0_rec/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt similarity index 89% rename from test_tipc/configs/ch_ppocr_mobile_v2.0_rec/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt rename to test_tipc/configs/ch_ppocr_mobile_v2_0_rec/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt index 480fb16cddfc4c2f4784cc8fa88512f063f7b2ae..332e632bd5da77398d0437c187e3abe11f36dc46 100644 --- a/test_tipc/configs/ch_ppocr_mobile_v2.0_rec/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt +++ b/test_tipc/configs/ch_ppocr_mobile_v2_0_rec/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt @@ -1,5 +1,5 @@ ===========================cpp_infer_params=========================== -model_name:ch_ppocr_mobile_v2.0_rec +model_name:ch_ppocr_mobile_v2_0_rec use_opencv:True infer_model:./inference/ch_ppocr_mobile_v2.0_rec_infer/ infer_quant:False diff --git a/test_tipc/configs/ch_ppocr_mobile_v2.0_rec/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt b/test_tipc/configs/ch_ppocr_mobile_v2_0_rec/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt similarity index 87% rename from test_tipc/configs/ch_ppocr_mobile_v2.0_rec/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt rename to test_tipc/configs/ch_ppocr_mobile_v2_0_rec/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt index 5bab0c9e4c77edba302f6b536306816b09df9224..78b76edae1aaa353f032d2ca1dd2eb21e22183a3 100644 --- a/test_tipc/configs/ch_ppocr_mobile_v2.0_rec/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt +++ b/test_tipc/configs/ch_ppocr_mobile_v2_0_rec/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt @@ -1,5 +1,5 @@ ===========================paddle2onnx_params=========================== -model_name:ch_ppocr_mobile_v2.0_rec +model_name:ch_ppocr_mobile_v2_0_rec python:python3.7 2onnx: paddle2onnx --det_model_dir: diff --git a/test_tipc/configs/ch_ppocr_mobile_v2.0_rec/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt b/test_tipc/configs/ch_ppocr_mobile_v2_0_rec/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt similarity index 96% rename from test_tipc/configs/ch_ppocr_mobile_v2.0_rec/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt rename to test_tipc/configs/ch_ppocr_mobile_v2_0_rec/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt index c0c5291cc480f9f34aa5dcded3eafce7feac89e3..5c60903f678778c4d10c88745a4cca12fb488c6f 100644 --- a/test_tipc/configs/ch_ppocr_mobile_v2.0_rec/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt +++ b/test_tipc/configs/ch_ppocr_mobile_v2_0_rec/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt @@ -1,5 +1,5 @@ ===========================serving_params=========================== -model_name:ch_ppocr_mobile_v2.0_rec +model_name:ch_ppocr_mobile_v2_0_rec python:python3.7 trans_model:-m paddle_serving_client.convert --det_dirname:null diff --git 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 similarity index 78% rename from test_tipc/configs/ch_ppocr_mobile_v2.0_rec/train_infer_python.txt rename to test_tipc/configs/ch_ppocr_mobile_v2_0_rec/train_infer_python.txt index f02b93926cac8116844142fe3ecb03959abb0530..40f397948936beba0a3a4bdce9aa4a9953ec9d0f 100644 --- 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 @@ -1,5 +1,5 @@ ===========================train_params=========================== -model_name:ch_ppocr_mobile_v2.0_rec +model_name:ch_ppocr_mobile_v2_0_rec python:python3.7 gpu_list:0|0,1 Global.use_gpu:True|True @@ -13,7 +13,7 @@ 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 +norm_train:tools/train.py -c configs/rec/rec_icdar15_train.yml -o Global.print_batch_step=4 Train.loader.shuffle=false pact_train:null fpgm_train:null distill_train:null @@ -51,3 +51,9 @@ inference:tools/infer/predict_rec.py null:null ===========================infer_benchmark_params========================== random_infer_input:[{float32,[3,32,100]}] +===========================train_benchmark_params========================== +batch_size:256 +fp_items:fp32|fp16 +epoch:3 +--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile +flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096 diff --git a/test_tipc/configs/ch_ppocr_mobile_v2.0_rec/train_linux_gpu_fleet_normal_infer_python_linux_gpu_cpu.txt b/test_tipc/configs/ch_ppocr_mobile_v2_0_rec/train_linux_gpu_fleet_normal_infer_python_linux_gpu_cpu.txt similarity index 97% rename from test_tipc/configs/ch_ppocr_mobile_v2.0_rec/train_linux_gpu_fleet_normal_infer_python_linux_gpu_cpu.txt rename to test_tipc/configs/ch_ppocr_mobile_v2_0_rec/train_linux_gpu_fleet_normal_infer_python_linux_gpu_cpu.txt index 631118c0a9ab98c10129f12ec1c1cf2bbac46115..2f919d102b2abdd5b72642abc413a47b4cf17350 100644 --- a/test_tipc/configs/ch_ppocr_mobile_v2.0_rec/train_linux_gpu_fleet_normal_infer_python_linux_gpu_cpu.txt +++ b/test_tipc/configs/ch_ppocr_mobile_v2_0_rec/train_linux_gpu_fleet_normal_infer_python_linux_gpu_cpu.txt @@ -1,5 +1,5 @@ ===========================train_params=========================== -model_name:ch_ppocr_mobile_v2.0_rec +model_name:ch_ppocr_mobile_v2_0_rec python:python3.7 gpu_list:192.168.0.1,192.168.0.2;0,1 Global.use_gpu:True diff --git a/test_tipc/configs/ch_ppocr_mobile_v2.0_rec/train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt b/test_tipc/configs/ch_ppocr_mobile_v2_0_rec/train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt similarity index 97% rename from test_tipc/configs/ch_ppocr_mobile_v2.0_rec/train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt rename to test_tipc/configs/ch_ppocr_mobile_v2_0_rec/train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt index bd9c4a8df2565af73b6db24636b6dd132dac0cc2..f60e2790e5879d99e322f36489aba67cb0d2b66c 100644 --- a/test_tipc/configs/ch_ppocr_mobile_v2.0_rec/train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt +++ b/test_tipc/configs/ch_ppocr_mobile_v2_0_rec/train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt @@ -1,5 +1,5 @@ ===========================train_params=========================== -model_name:ch_ppocr_mobile_v2.0_rec +model_name:ch_ppocr_mobile_v2_0_rec python:python3.7 gpu_list:0|0,1 Global.use_gpu:True|True diff --git a/test_tipc/configs/ch_ppocr_mobile_v2.0_rec/train_pact_infer_python.txt b/test_tipc/configs/ch_ppocr_mobile_v2_0_rec/train_pact_infer_python.txt similarity index 97% rename from test_tipc/configs/ch_ppocr_mobile_v2.0_rec/train_pact_infer_python.txt rename to test_tipc/configs/ch_ppocr_mobile_v2_0_rec/train_pact_infer_python.txt index 77472fbdfb21c81bb713df175a135ccf9e652f25..631518ffc43d2ea7ea879479faf8ae0e6c5ecd88 100644 --- a/test_tipc/configs/ch_ppocr_mobile_v2.0_rec/train_pact_infer_python.txt +++ b/test_tipc/configs/ch_ppocr_mobile_v2_0_rec/train_pact_infer_python.txt @@ -1,5 +1,5 @@ ===========================train_params=========================== -model_name:ch_ppocr_mobile_v2.0_rec_PACT +model_name:ch_ppocr_mobile_v2_0_rec_PACT python:python3.7 gpu_list:0 Global.use_gpu:True|True diff --git a/test_tipc/configs/ch_ppocr_mobile_v2.0_rec/train_ptq_infer_python.txt b/test_tipc/configs/ch_ppocr_mobile_v2_0_rec/train_ptq_infer_python.txt similarity index 94% rename from test_tipc/configs/ch_ppocr_mobile_v2.0_rec/train_ptq_infer_python.txt rename to test_tipc/configs/ch_ppocr_mobile_v2_0_rec/train_ptq_infer_python.txt index f63fe4c2bb6a17353ecb008d83e2bee9d38aec23..cd0828f3c60857997db84b8a17373ea0ed1c3b3b 100644 --- a/test_tipc/configs/ch_ppocr_mobile_v2.0_rec/train_ptq_infer_python.txt +++ b/test_tipc/configs/ch_ppocr_mobile_v2_0_rec/train_ptq_infer_python.txt @@ -1,5 +1,5 @@ ===========================kl_quant_params=========================== -model_name:ch_ppocr_mobile_v2.0_rec_KL +model_name:ch_ppocr_mobile_v2_0_rec_KL python:python3.7 Global.pretrained_model:null Global.save_inference_dir:null diff --git 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 similarity index 100% rename from test_tipc/configs/ch_ppocr_mobile_v2.0_rec_FPGM/rec_chinese_lite_train_v2.0.yml rename to test_tipc/configs/ch_ppocr_mobile_v2_0_rec_FPGM/rec_chinese_lite_train_v2.0.yml diff --git 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 similarity index 95% rename from test_tipc/configs/ch_ppocr_mobile_v2.0_rec_FPGM/train_infer_python.txt rename to test_tipc/configs/ch_ppocr_mobile_v2_0_rec_FPGM/train_infer_python.txt index 89daceeb5f4a991699a490b51358d33240e74913..b2fb028823b4060b1d0ec5aaf2475fd7a9754fa8 100644 --- 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 @@ -1,5 +1,5 @@ ===========================train_params=========================== -model_name:ch_ppocr_mobile_v2.0_rec_FPGM +model_name:ch_ppocr_mobile_v2_0_rec_FPGM python:python3.7 gpu_list:0 Global.use_gpu:True|True diff --git a/test_tipc/configs/ch_ppocr_mobile_v2.0_rec_FPGM/train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt b/test_tipc/configs/ch_ppocr_mobile_v2_0_rec_FPGM/train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt similarity index 95% rename from test_tipc/configs/ch_ppocr_mobile_v2.0_rec_FPGM/train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt rename to test_tipc/configs/ch_ppocr_mobile_v2_0_rec_FPGM/train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt index 7abc3e9340fe49a2b0bf0efd5e3c370817cd4e9d..509240f6c5654de71c6f5cfc44e6618d44af3056 100644 --- a/test_tipc/configs/ch_ppocr_mobile_v2.0_rec_FPGM/train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt +++ b/test_tipc/configs/ch_ppocr_mobile_v2_0_rec_FPGM/train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt @@ -1,5 +1,5 @@ ===========================train_params=========================== -model_name:ch_ppocr_mobile_v2.0_rec_FPGM +model_name:ch_ppocr_mobile_v2_0_rec_FPGM python:python3.7 gpu_list:0 Global.use_gpu:True|True diff --git a/test_tipc/configs/ch_ppocr_mobile_v2.0_rec_KL/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt b/test_tipc/configs/ch_ppocr_mobile_v2_0_rec_KL/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt similarity index 89% rename from test_tipc/configs/ch_ppocr_mobile_v2.0_rec_KL/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt rename to test_tipc/configs/ch_ppocr_mobile_v2_0_rec_KL/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt index adf06257a772cfd16d4109497c6e6ef7c3f8af8b..ef4c93fcdecb2e1e3adc832089efa6de50c5484c 100644 --- a/test_tipc/configs/ch_ppocr_mobile_v2.0_rec_KL/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt +++ b/test_tipc/configs/ch_ppocr_mobile_v2_0_rec_KL/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt @@ -1,5 +1,5 @@ ===========================cpp_infer_params=========================== -model_name:ch_ppocr_mobile_v2.0_rec_KL +model_name:ch_ppocr_mobile_v2_0_rec_KL use_opencv:True infer_model:./inference/ch_ppocr_mobile_v2.0_rec_klquant_infer infer_quant:False diff --git a/test_tipc/configs/ch_ppocr_mobile_v2.0_det_KL/model_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt b/test_tipc/configs/ch_ppocr_mobile_v2_0_rec_KL/model_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt similarity index 95% rename from test_tipc/configs/ch_ppocr_mobile_v2.0_det_KL/model_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt rename to test_tipc/configs/ch_ppocr_mobile_v2_0_rec_KL/model_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt index d9de1cc19a729485845601fe929bf57d74002641..d904e22a7cc1f68ab8bccafa86ec11b92c67c244 100644 --- a/test_tipc/configs/ch_ppocr_mobile_v2.0_det_KL/model_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt +++ b/test_tipc/configs/ch_ppocr_mobile_v2_0_rec_KL/model_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt @@ -1,5 +1,5 @@ ===========================serving_params=========================== -model_name:ch_ppocr_mobile_v2.0_det_KL +model_name:ch_ppocr_mobile_v2_0_rec_KL python:python3.7 trans_model:-m paddle_serving_client.convert --det_dirname:./inference/ch_ppocr_mobile_v2.0_det_klquant_infer/ diff --git a/test_tipc/configs/ch_ppocr_mobile_v2.0_rec_KL/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt b/test_tipc/configs/ch_ppocr_mobile_v2_0_rec_KL/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt similarity index 96% rename from test_tipc/configs/ch_ppocr_mobile_v2.0_rec_KL/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt rename to test_tipc/configs/ch_ppocr_mobile_v2_0_rec_KL/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt index 948e3dceb3ef7e3f2199be5e417cfc5fc763d975..de4f7ed2c1d5cd457917cb3668f181e2366bb78f 100644 --- a/test_tipc/configs/ch_ppocr_mobile_v2.0_rec_KL/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt +++ b/test_tipc/configs/ch_ppocr_mobile_v2_0_rec_KL/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt @@ -1,5 +1,5 @@ ===========================serving_params=========================== -model_name:ch_ppocr_mobile_v2.0_rec_KL +model_name:ch_ppocr_mobile_v2_0_rec_KL python:python3.7 trans_model:-m paddle_serving_client.convert --det_dirname:null diff --git 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 similarity index 100% rename from test_tipc/configs/ch_ppocr_mobile_v2.0_rec_KL/rec_chinese_lite_train_v2.0.yml rename to test_tipc/configs/ch_ppocr_mobile_v2_0_rec_KL/rec_chinese_lite_train_v2.0.yml diff --git a/test_tipc/configs/ch_ppocr_mobile_v2.0_rec_PACT/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt b/test_tipc/configs/ch_ppocr_mobile_v2_0_rec_PACT/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt similarity index 89% rename from test_tipc/configs/ch_ppocr_mobile_v2.0_rec_PACT/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt rename to test_tipc/configs/ch_ppocr_mobile_v2_0_rec_PACT/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt index ba2df90f75d2c70e043c45ea19d681aabc2b6fb2..74ca7b50b8b2ac8efa00d3a1cfa5e60b97cf99ba 100644 --- a/test_tipc/configs/ch_ppocr_mobile_v2.0_rec_PACT/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt +++ b/test_tipc/configs/ch_ppocr_mobile_v2_0_rec_PACT/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt @@ -1,5 +1,5 @@ ===========================cpp_infer_params=========================== -model_name:ch_ppocr_mobile_v2.0_rec_PACT +model_name:ch_ppocr_mobile_v2_0_rec_PACT use_opencv:True infer_model:./inference/ch_ppocr_mobile_v2.0_rec_pact_infer infer_quant:False diff --git a/test_tipc/configs/ch_ppocr_mobile_v2.0_det_PACT/model_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt b/test_tipc/configs/ch_ppocr_mobile_v2_0_rec_PACT/model_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt similarity index 95% rename from test_tipc/configs/ch_ppocr_mobile_v2.0_det_PACT/model_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt rename to test_tipc/configs/ch_ppocr_mobile_v2_0_rec_PACT/model_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt index 1a49a10f9b9d4e32916dd35bae3380e2ca5bebb9..5a3047448b5264e9ab771249c7b113e32943c5f1 100644 --- a/test_tipc/configs/ch_ppocr_mobile_v2.0_det_PACT/model_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt +++ b/test_tipc/configs/ch_ppocr_mobile_v2_0_rec_PACT/model_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt @@ -1,5 +1,5 @@ ===========================serving_params=========================== -model_name:ch_ppocr_mobile_v2.0_det_PACT +model_name:ch_ppocr_mobile_v2_0_rec_PACT python:python3.7 trans_model:-m paddle_serving_client.convert --det_dirname:./inference/ch_ppocr_mobile_v2.0_det_pact_infer/ diff --git a/test_tipc/configs/ch_ppocr_mobile_v2.0_rec_PACT/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt b/test_tipc/configs/ch_ppocr_mobile_v2_0_rec_PACT/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt similarity index 95% rename from test_tipc/configs/ch_ppocr_mobile_v2.0_rec_PACT/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt rename to test_tipc/configs/ch_ppocr_mobile_v2_0_rec_PACT/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt index f123f365432ab68f2484cc11dd9ef94c8a60ea8e..5871199bc020fb231d3f485d312339f634d5ccd3 100644 --- a/test_tipc/configs/ch_ppocr_mobile_v2.0_rec_PACT/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt +++ b/test_tipc/configs/ch_ppocr_mobile_v2_0_rec_PACT/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt @@ -1,5 +1,5 @@ ===========================serving_params=========================== -model_name:ch_ppocr_mobile_v2.0_rec_PACT +model_name:ch_ppocr_mobile_v2_0_rec_PACT python:python3.7 trans_model:-m paddle_serving_client.convert --det_dirname:null diff --git 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 similarity index 100% rename from test_tipc/configs/ch_ppocr_mobile_v2.0_rec_PACT/rec_chinese_lite_train_v2.0.yml rename to test_tipc/configs/ch_ppocr_mobile_v2_0_rec_PACT/rec_chinese_lite_train_v2.0.yml diff --git a/test_tipc/configs/ch_ppocr_server_v2.0/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt b/test_tipc/configs/ch_ppocr_server_v2_0/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt similarity index 91% rename from test_tipc/configs/ch_ppocr_server_v2.0/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt rename to test_tipc/configs/ch_ppocr_server_v2_0/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt index 7c980b2baeef7161a93dea360089b333f2003a31..ba8646fd9d73c36a205c2e68ed35a2b777a47cfb 100644 --- a/test_tipc/configs/ch_ppocr_server_v2.0/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt +++ b/test_tipc/configs/ch_ppocr_server_v2_0/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt @@ -1,5 +1,5 @@ ===========================cpp_infer_params=========================== -model_name:ch_ppocr_server_v2.0 +model_name:ch_ppocr_server_v2_0 use_opencv:True infer_model:./inference/ch_ppocr_server_v2.0_det_infer/ infer_quant:False diff --git 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 similarity index 94% rename from test_tipc/configs/ch_ppocr_server_v2.0/model_linux_gpu_normal_normal_infer_python_linux_gpu_cpu.txt rename to test_tipc/configs/ch_ppocr_server_v2_0/model_linux_gpu_normal_normal_infer_python_linux_gpu_cpu.txt index b20596f7a1db6da04307a7e527ef596477d237d3..53f8ab0e746df87205197179ca3367d32eb56a6d 100644 --- 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 @@ -1,5 +1,5 @@ ===========================ch_ppocr_server_v2.0=========================== -model_name: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 diff --git a/test_tipc/configs/ch_ppocr_server_v2.0/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt b/test_tipc/configs/ch_ppocr_server_v2_0/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt similarity index 85% rename from test_tipc/configs/ch_ppocr_server_v2.0/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt rename to test_tipc/configs/ch_ppocr_server_v2_0/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt index e478896a54957481a3ce4c485ac02cd7979233dc..9e2cf191f3c24523279ee53006a00b0be9a83297 100644 --- a/test_tipc/configs/ch_ppocr_server_v2.0/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt +++ b/test_tipc/configs/ch_ppocr_server_v2_0/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt @@ -1,5 +1,5 @@ ===========================paddle2onnx_params=========================== -model_name:ch_ppocr_server_v2.0 +model_name:ch_ppocr_server_v2_0 python:python3.7 2onnx: paddle2onnx --det_model_dir:./inference/ch_ppocr_server_v2.0_det_infer/ diff --git a/test_tipc/configs/ch_ppocr_server_v2.0/model_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt b/test_tipc/configs/ch_ppocr_server_v2_0/model_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt similarity index 96% rename from test_tipc/configs/ch_ppocr_server_v2.0/model_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt rename to test_tipc/configs/ch_ppocr_server_v2_0/model_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt index bbfec44dbab08dcfb932a922797448e541ea385b..55b27e04a20de07d8baf71046253601c658799ea 100644 --- a/test_tipc/configs/ch_ppocr_server_v2.0/model_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt +++ b/test_tipc/configs/ch_ppocr_server_v2_0/model_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt @@ -1,5 +1,5 @@ ===========================serving_params=========================== -model_name:ch_ppocr_server_v2.0 +model_name:ch_ppocr_server_v2_0 python:python3.7 trans_model:-m paddle_serving_client.convert --det_dirname:./inference/ch_ppocr_server_v2.0_det_infer/ diff --git a/test_tipc/configs/ch_ppocr_server_v2.0/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt b/test_tipc/configs/ch_ppocr_server_v2_0/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt similarity index 97% rename from test_tipc/configs/ch_ppocr_server_v2.0/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt rename to test_tipc/configs/ch_ppocr_server_v2_0/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt index 8853e709d40a0fba6bedd7ce582425e39b9076ed..21b8c9a082116c58e50c58e37a5a6842a1839b9b 100644 --- a/test_tipc/configs/ch_ppocr_server_v2.0/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt +++ b/test_tipc/configs/ch_ppocr_server_v2_0/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt @@ -1,5 +1,5 @@ ===========================serving_params=========================== -model_name:ch_ppocr_server_v2.0 +model_name:ch_ppocr_server_v2_0 python:python3.7 trans_model:-m paddle_serving_client.convert --det_dirname:./inference/ch_ppocr_server_v2.0_det_infer/ diff --git a/test_tipc/configs/ch_ppocr_server_v2.0_det/det_r50_vd_db.yml b/test_tipc/configs/ch_ppocr_server_v2_0_det/det_r50_vd_db.yml similarity index 95% rename from test_tipc/configs/ch_ppocr_server_v2.0_det/det_r50_vd_db.yml rename to test_tipc/configs/ch_ppocr_server_v2_0_det/det_r50_vd_db.yml index f512de808141a0a0e815f9477de80b893ae3c946..6728703a10c2eb4e19b3bbf2225f089324e7d5cd 100644 --- a/test_tipc/configs/ch_ppocr_server_v2.0_det/det_r50_vd_db.yml +++ b/test_tipc/configs/ch_ppocr_server_v2_0_det/det_r50_vd_db.yml @@ -2,13 +2,13 @@ Global: use_gpu: false epoch_num: 5 log_smooth_window: 20 - print_batch_step: 1 + print_batch_step: 2 save_model_dir: ./output/db_mv3/ save_epoch_step: 1200 # evaluation is run every 2000 iterations - eval_batch_step: [0, 400] + eval_batch_step: [0, 30000] cal_metric_during_train: False - pretrained_model: ./pretrain_models/MobileNetV3_large_x0_5_pretrained + pretrained_model: checkpoints: save_inference_dir: use_visualdl: False diff --git a/test_tipc/configs/ch_ppocr_server_v2.0_det/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt b/test_tipc/configs/ch_ppocr_server_v2_0_det/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt similarity index 88% rename from test_tipc/configs/ch_ppocr_server_v2.0_det/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt rename to test_tipc/configs/ch_ppocr_server_v2_0_det/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt index 69ae939e2b6cab5e07bc4e401a83c66324754223..4a30affd07748cd87f4c1982d8555f7b5a0a0b54 100644 --- a/test_tipc/configs/ch_ppocr_server_v2.0_det/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt +++ b/test_tipc/configs/ch_ppocr_server_v2_0_det/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt @@ -1,5 +1,5 @@ ===========================cpp_infer_params=========================== -model_name:ch_ppocr_server_v2.0_det +model_name:ch_ppocr_server_v2_0_det use_opencv:True infer_model:./inference/ch_ppocr_server_v2.0_det_infer/ infer_quant:False diff --git a/test_tipc/configs/ch_ppocr_server_v2.0_det/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt b/test_tipc/configs/ch_ppocr_server_v2_0_det/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt similarity index 82% rename from test_tipc/configs/ch_ppocr_server_v2.0_det/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt rename to test_tipc/configs/ch_ppocr_server_v2_0_det/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt index c8bebf54f2ed2627cce9a22013d1566eb7a7b6ef..b7dd6e22b90b0cfaeeac75a7f090e2a199c94831 100644 --- a/test_tipc/configs/ch_ppocr_server_v2.0_det/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt +++ b/test_tipc/configs/ch_ppocr_server_v2_0_det/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt @@ -1,5 +1,5 @@ ===========================paddle2onnx_params=========================== -model_name:ch_ppocr_server_v2.0_det +model_name:ch_ppocr_server_v2_0_det python:python3.7 2onnx: paddle2onnx --det_model_dir:./inference/ch_ppocr_server_v2.0_det_infer/ diff --git a/test_tipc/configs/ch_ppocr_server_v2.0_det/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt b/test_tipc/configs/ch_ppocr_server_v2_0_det/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt similarity index 96% rename from test_tipc/configs/ch_ppocr_server_v2.0_det/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt rename to test_tipc/configs/ch_ppocr_server_v2_0_det/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt index 018dd1a227064479ebd60570113b122b035e7704..4d4f0679bf6cb77b6bdb4fa7494677e933554911 100644 --- a/test_tipc/configs/ch_ppocr_server_v2.0_det/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt +++ b/test_tipc/configs/ch_ppocr_server_v2_0_det/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt @@ -1,5 +1,5 @@ ===========================serving_params=========================== -model_name:ch_ppocr_server_v2.0_det +model_name:ch_ppocr_server_v2_0_det python:python3.7 trans_model:-m paddle_serving_client.convert --det_dirname:./inference/ch_ppocr_server_v2.0_det_infer/ diff --git a/test_tipc/configs/ch_ppocr_server_v2.0_det/train_infer_python.txt b/test_tipc/configs/ch_ppocr_server_v2_0_det/train_infer_python.txt similarity index 80% rename from test_tipc/configs/ch_ppocr_server_v2.0_det/train_infer_python.txt rename to test_tipc/configs/ch_ppocr_server_v2_0_det/train_infer_python.txt index c16ca150029d03052396ca28a6396520e63b3f84..87e18ef7f38f7ed8b3d461e8a19d84ffaa3d717d 100644 --- a/test_tipc/configs/ch_ppocr_server_v2.0_det/train_infer_python.txt +++ b/test_tipc/configs/ch_ppocr_server_v2_0_det/train_infer_python.txt @@ -1,5 +1,5 @@ ===========================train_params=========================== -model_name:ch_ppocr_server_v2.0_det +model_name:ch_ppocr_server_v2_0_det python:python3.7 gpu_list:0|0,1 Global.use_gpu:True|True @@ -13,7 +13,7 @@ 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/ch_ppocr_server_v2.0_det/det_r50_vd_db.yml -o +norm_train:tools/train.py -c test_tipc/configs/ch_ppocr_server_v2.0_det/det_r50_vd_db.yml -o quant_train:null fpgm_train:null distill_train:null @@ -50,4 +50,10 @@ inference:tools/infer/predict_det.py --benchmark:True null:null ===========================infer_benchmark_params========================== -random_infer_input:[{float32,[3,640,640]}];[{float32,[3,960,960]}] \ No newline at end of file +random_infer_input:[{float32,[3,640,640]}];[{float32,[3,960,960]}] +===========================train_benchmark_params========================== +batch_size:8 +fp_items:fp32|fp16 +epoch:2 +--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile +flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096 diff --git a/test_tipc/configs/ch_ppocr_server_v2.0_det/train_linux_gpu_fleet_normal_infer_python_linux_gpu_cpu.txt b/test_tipc/configs/ch_ppocr_server_v2_0_det/train_linux_gpu_fleet_normal_infer_python_linux_gpu_cpu.txt similarity index 97% rename from test_tipc/configs/ch_ppocr_server_v2.0_det/train_linux_gpu_fleet_normal_infer_python_linux_gpu_cpu.txt rename to test_tipc/configs/ch_ppocr_server_v2_0_det/train_linux_gpu_fleet_normal_infer_python_linux_gpu_cpu.txt index 12388d967755c54a46efdb915ef047896dddaef7..d1c7cf1d9e12b8ed00c39793f07152205968254d 100644 --- a/test_tipc/configs/ch_ppocr_server_v2.0_det/train_linux_gpu_fleet_normal_infer_python_linux_gpu_cpu.txt +++ b/test_tipc/configs/ch_ppocr_server_v2_0_det/train_linux_gpu_fleet_normal_infer_python_linux_gpu_cpu.txt @@ -1,5 +1,5 @@ ===========================train_params=========================== -model_name:ch_ppocr_server_v2.0_det +model_name:ch_ppocr_server_v2_0_det python:python3.7 gpu_list:192.168.0.1,192.168.0.2;0,1 Global.use_gpu:True diff --git a/test_tipc/configs/ch_ppocr_server_v2.0_det/train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt b/test_tipc/configs/ch_ppocr_server_v2_0_det/train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt similarity index 97% rename from test_tipc/configs/ch_ppocr_server_v2.0_det/train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt rename to test_tipc/configs/ch_ppocr_server_v2_0_det/train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt index 93ed14cb600229e744167f26573cba406880db8e..2802406b87db870238acff643baab26dc85fab80 100644 --- a/test_tipc/configs/ch_ppocr_server_v2.0_det/train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt +++ b/test_tipc/configs/ch_ppocr_server_v2_0_det/train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt @@ -1,5 +1,5 @@ ===========================train_params=========================== -model_name:ch_ppocr_server_v2.0_det +model_name:ch_ppocr_server_v2_0_det python:python3.7 gpu_list:0|0,1 Global.use_gpu:True|True diff --git a/test_tipc/configs/ch_ppocr_server_v2.0_rec/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt b/test_tipc/configs/ch_ppocr_server_v2_0_rec/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt similarity index 89% rename from test_tipc/configs/ch_ppocr_server_v2.0_rec/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt rename to test_tipc/configs/ch_ppocr_server_v2_0_rec/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt index cbec272cce544e332fd908d4946321a15543fcae..3f3905516abcd6c7bb517bc0df50af331335dabe 100644 --- a/test_tipc/configs/ch_ppocr_server_v2.0_rec/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt +++ b/test_tipc/configs/ch_ppocr_server_v2_0_rec/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt @@ -1,5 +1,5 @@ ===========================cpp_infer_params=========================== -model_name:ch_ppocr_server_v2.0_rec +model_name:ch_ppocr_server_v2_0_rec use_opencv:True infer_model:./inference/ch_ppocr_server_v2.0_rec_infer/ infer_quant:False diff --git a/test_tipc/configs/ch_ppocr_server_v2.0_rec/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt b/test_tipc/configs/ch_ppocr_server_v2_0_rec/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt similarity index 87% rename from test_tipc/configs/ch_ppocr_server_v2.0_rec/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt rename to test_tipc/configs/ch_ppocr_server_v2_0_rec/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt index 462f6090d987ac2c58656136e896e71bcdc3bee1..89b9661003d1a2ee3d0987f50a9278fc96391988 100644 --- a/test_tipc/configs/ch_ppocr_server_v2.0_rec/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt +++ b/test_tipc/configs/ch_ppocr_server_v2_0_rec/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt @@ -1,5 +1,5 @@ ===========================paddle2onnx_params=========================== -model_name:ch_ppocr_server_v2.0_rec +model_name:ch_ppocr_server_v2_0_rec python:python3.7 2onnx: paddle2onnx --det_model_dir: diff --git a/test_tipc/configs/ch_ppocr_server_v2.0_rec/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt b/test_tipc/configs/ch_ppocr_server_v2_0_rec/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt similarity index 96% rename from test_tipc/configs/ch_ppocr_server_v2.0_rec/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt rename to test_tipc/configs/ch_ppocr_server_v2_0_rec/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt index 7f456320b687549fbcd6d4f0be7a1b4a2969684a..4133e961cdaf4acb6dbf7421f38147e996e66401 100644 --- a/test_tipc/configs/ch_ppocr_server_v2.0_rec/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt +++ b/test_tipc/configs/ch_ppocr_server_v2_0_rec/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt @@ -1,5 +1,5 @@ ===========================serving_params=========================== -model_name:ch_ppocr_server_v2.0_rec +model_name:ch_ppocr_server_v2_0_rec python:python3.7 trans_model:-m paddle_serving_client.convert --det_dirname:null diff --git a/test_tipc/configs/ch_ppocr_server_v2.0_rec/rec_icdar15_train.yml b/test_tipc/configs/ch_ppocr_server_v2_0_rec/rec_icdar15_train.yml similarity index 100% rename from test_tipc/configs/ch_ppocr_server_v2.0_rec/rec_icdar15_train.yml rename to test_tipc/configs/ch_ppocr_server_v2_0_rec/rec_icdar15_train.yml diff --git 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 similarity index 78% rename from test_tipc/configs/ch_ppocr_server_v2.0_rec/train_infer_python.txt rename to test_tipc/configs/ch_ppocr_server_v2_0_rec/train_infer_python.txt index 64c0cf455cdd058d1840a9ad1f86954293d2e219..e7cde1334a6c429582360c8ae9f88dd21884c0eb 100644 --- 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 @@ -1,5 +1,5 @@ ===========================train_params=========================== -model_name:ch_ppocr_server_v2.0_rec +model_name:ch_ppocr_server_v2_0_rec python:python3.7 gpu_list:0|0,1 Global.use_gpu:True|True @@ -13,7 +13,7 @@ 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 +norm_train:tools/train.py -c test_tipc/configs/ch_ppocr_server_v2.0_rec/rec_icdar15_train.yml -o Global.print_batch_step=4 Train.loader.shuffle=false pact_train:null fpgm_train:null distill_train:null @@ -51,3 +51,9 @@ inference:tools/infer/predict_rec.py null:null ===========================infer_benchmark_params========================== random_infer_input:[{float32,[3,32,100]}] +===========================train_benchmark_params========================== +batch_size:256 +fp_items:fp32|fp16 +epoch:2 +--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile +flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096 diff --git a/test_tipc/configs/ch_ppocr_server_v2.0_rec/train_linux_gpu_fleet_normal_infer_python_linux_gpu_cpu.txt b/test_tipc/configs/ch_ppocr_server_v2_0_rec/train_linux_gpu_fleet_normal_infer_python_linux_gpu_cpu.txt similarity index 97% rename from test_tipc/configs/ch_ppocr_server_v2.0_rec/train_linux_gpu_fleet_normal_infer_python_linux_gpu_cpu.txt rename to test_tipc/configs/ch_ppocr_server_v2_0_rec/train_linux_gpu_fleet_normal_infer_python_linux_gpu_cpu.txt index 9884ab247b80de4ca700bf084cea4faa89c86396..99b864d1b0ec9f86e97f97f3660d5f8222a4b2c8 100644 --- a/test_tipc/configs/ch_ppocr_server_v2.0_rec/train_linux_gpu_fleet_normal_infer_python_linux_gpu_cpu.txt +++ b/test_tipc/configs/ch_ppocr_server_v2_0_rec/train_linux_gpu_fleet_normal_infer_python_linux_gpu_cpu.txt @@ -1,5 +1,5 @@ ===========================train_params=========================== -model_name:ch_ppocr_server_v2.0_rec +model_name:ch_ppocr_server_v2_0_rec python:python3.7 gpu_list:192.168.0.1,192.168.0.2;0,1 Global.use_gpu:True diff --git a/test_tipc/configs/ch_ppocr_server_v2.0_rec/train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt b/test_tipc/configs/ch_ppocr_server_v2_0_rec/train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt similarity index 97% rename from test_tipc/configs/ch_ppocr_server_v2.0_rec/train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt rename to test_tipc/configs/ch_ppocr_server_v2_0_rec/train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt index 63ddaa4a8b2dcb19823034ee85af14b248b109b2..63aa07dd18581aaa8e8fb3bba48a27d5a0912579 100644 --- a/test_tipc/configs/ch_ppocr_server_v2.0_rec/train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt +++ b/test_tipc/configs/ch_ppocr_server_v2_0_rec/train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt @@ -1,5 +1,5 @@ ===========================train_params=========================== -model_name:ch_ppocr_server_v2.0_rec +model_name:ch_ppocr_server_v2_0_rec python:python3.7 gpu_list:0|0,1 Global.use_gpu:True|True diff --git 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 index ab3aa59b601db58b48cf18de79f77710611e2596..62303b7e511bc42444e3610fb17eaf74ccf0848a 100644 --- 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 @@ -13,7 +13,7 @@ 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 +norm_train:tools/train.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained Global.print_batch_step=1 Train.loader.shuffle=false pact_train:null fpgm_train:null distill_train:null @@ -52,8 +52,8 @@ null:null ===========================infer_benchmark_params========================== random_infer_input:[{float32,[3,640,640]}];[{float32,[3,960,960]}] ===========================train_benchmark_params========================== -batch_size:8|16 +batch_size:16 fp_items:fp32|fp16 -epoch:2 +epoch:4 --profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile -flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096 +flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096 \ No newline at end of file diff --git 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 similarity index 100% rename from test_tipc/configs/det_mv3_east_v2.0/det_mv3_east.yml rename to test_tipc/configs/det_mv3_east_v2_0/det_mv3_east.yml diff --git 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 similarity index 98% rename from test_tipc/configs/det_mv3_east_v2.0/train_infer_python.txt rename to test_tipc/configs/det_mv3_east_v2_0/train_infer_python.txt index 1ec1597a4d50ba1c41cfb076fa7431f170e183bf..2818b5041cb2bb6b1e8f8715b5c47f0c441afbc4 100644 --- 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 @@ -1,5 +1,5 @@ ===========================train_params=========================== -model_name:det_mv3_east_v2.0 +model_name:det_mv3_east_v2_0 python:python3.7 gpu_list:0|0,1 Global.use_gpu:True|True diff --git 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 similarity index 100% rename from test_tipc/configs/det_mv3_pse_v2.0/det_mv3_pse.yml rename to test_tipc/configs/det_mv3_pse_v2_0/det_mv3_pse.yml diff --git 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 similarity index 98% rename from test_tipc/configs/det_mv3_pse_v2.0/train_infer_python.txt rename to test_tipc/configs/det_mv3_pse_v2_0/train_infer_python.txt index daeec69f84a766e1d6cd2f8906772c27f5f8d048..1fb4724ea4510a8305e898f8c15cf3571923895c 100644 --- 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 @@ -1,5 +1,5 @@ ===========================train_params=========================== -model_name:det_mv3_pse_v2.0 +model_name:det_mv3_pse_v2_0 python:python3.7 gpu_list:0 Global.use_gpu:True|True diff --git a/test_tipc/configs/det_r18_vd_db_v2_0/train_infer_python.txt b/test_tipc/configs/det_r18_vd_db_v2_0/train_infer_python.txt index 33e4dbf2337f3799328516119a213bc0f14af9fe..df88c0e5434511fb48deac699e8f67fc535765d3 100644 --- a/test_tipc/configs/det_r18_vd_db_v2_0/train_infer_python.txt +++ b/test_tipc/configs/det_r18_vd_db_v2_0/train_infer_python.txt @@ -54,5 +54,5 @@ random_infer_input:[{float32,[3,640,640]}];[{float32,[3,960,960]}] ===========================train_benchmark_params========================== batch_size:8|16 fp_items:fp32|fp16 -epoch:2 +epoch:15 --profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile diff --git a/test_tipc/configs/det_r50_db_plusplus/train_infer_python.txt b/test_tipc/configs/det_r50_db_plusplus/train_infer_python.txt new file mode 100644 index 0000000000000000000000000000000000000000..110b7f9319cebdef5d9620671b2e62f3c1fe4a6d --- /dev/null +++ b/test_tipc/configs/det_r50_db_plusplus/train_infer_python.txt @@ -0,0 +1,53 @@ +===========================train_params=========================== +model_name:det_r50_db_plusplus +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_r50_db++_icdar15.yml -o Global.pretrained_model=./pretrain_models/ResNet50_dcn_asf_synthtext_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.checkpoints: +norm_export:tools/export_model.py -c configs/det/det_r50_db++_icdar15.yml -o +quant_export:null +fpgm_export:null +distill_export:null +export1:null +export2:null +inference_dir:null +train_model:./inference/det_r50_db++_train/best_accuracy +infer_export:tools/export_model.py -c configs/det/det_r50_db++_icdar15.yml -o +infer_quant:False +inference:tools/infer/predict_det.py --det_algorithm="DB++" +--use_gpu:True|False +--enable_mkldnn:False +--cpu_threads: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 +===========================infer_benchmark_params========================== +random_infer_input:[{float32,[3,640,640]}];[{float32,[3,960,960]}] diff --git 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 similarity index 76% rename from test_tipc/configs/det_r50_db_v2.0/train_infer_python.txt rename to test_tipc/configs/det_r50_db_v2_0/train_infer_python.txt index 151f2769cc2d97d6a3546f338383dd811aa06ace..1d0d9693a98524581bb17850bfdc81a2bc3e460c 100644 --- 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 @@ -1,5 +1,5 @@ ===========================train_params=========================== -model_name:det_r50_db_v2.0 +model_name:det_r50_db_v2_0 python:python3.7 gpu_list:0|0,1 Global.use_gpu:True|True @@ -13,7 +13,7 @@ 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 +norm_train:tools/train.py -c configs/det/det_r50_vd_db.yml -o Global.print_batch_step=1 Train.loader.shuffle=false quant_export:null fpgm_export:null distill_train:null @@ -50,4 +50,10 @@ inference:tools/infer/predict_det.py --benchmark:True null:null ===========================infer_benchmark_params========================== -random_infer_input:[{float32,[3,640,640]}];[{float32,[3,960,960]}] \ No newline at end of file +random_infer_input:[{float32,[3,640,640]}];[{float32,[3,960,960]}] +===========================train_benchmark_params========================== +batch_size:8 +fp_items:fp32|fp16 +epoch:2 +--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile +flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096 \ No newline at end of file diff --git a/test_tipc/configs/det_r50_dcn_fce_ctw_v2_0/det_r50_vd_dcn_fce_ctw.yml b/test_tipc/configs/det_r50_dcn_fce_ctw_v2_0/det_r50_vd_dcn_fce_ctw.yml new file mode 100644 index 0000000000000000000000000000000000000000..3a513b8f38cd5abf800c86f8fbeda789cb3d056a --- /dev/null +++ b/test_tipc/configs/det_r50_dcn_fce_ctw_v2_0/det_r50_vd_dcn_fce_ctw.yml @@ -0,0 +1,139 @@ +Global: + use_gpu: true + epoch_num: 1500 + log_smooth_window: 20 + print_batch_step: 20 + save_model_dir: ./output/det_r50_dcn_fce_ctw/ + save_epoch_step: 100 + # evaluation is run every 835 iterations + eval_batch_step: [0, 4000] + cal_metric_during_train: False + pretrained_model: ./pretrain_models/ResNet50_vd_ssld_pretrained + checkpoints: + save_inference_dir: + use_visualdl: False + infer_img: doc/imgs_en/img_10.jpg + save_res_path: ./output/det_fce/predicts_fce.txt + + +Architecture: + model_type: det + algorithm: FCE + Transform: + Backbone: + name: ResNet_vd + layers: 50 + dcn_stage: [False, True, True, True] + out_indices: [1,2,3] + Neck: + name: FCEFPN + out_channels: 256 + has_extra_convs: False + extra_stage: 0 + Head: + name: FCEHead + fourier_degree: 5 +Loss: + name: FCELoss + fourier_degree: 5 + num_sample: 50 + +Optimizer: + name: Adam + beta1: 0.9 + beta2: 0.999 + lr: + learning_rate: 0.0001 + regularizer: + name: 'L2' + factor: 0 + +PostProcess: + name: FCEPostProcess + scales: [8, 16, 32] + alpha: 1.0 + beta: 1.0 + fourier_degree: 5 + box_type: 'poly' + +Metric: + name: DetFCEMetric + 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 + transforms: + - DecodeImage: # load image + img_mode: BGR + channel_first: False + ignore_orientation: True + - DetLabelEncode: # Class handling label + - ColorJitter: + brightness: 0.142 + saturation: 0.5 + contrast: 0.5 + - RandomScaling: + - RandomCropFlip: + crop_ratio: 0.5 + - RandomCropPolyInstances: + crop_ratio: 0.8 + min_side_ratio: 0.3 + - RandomRotatePolyInstances: + rotate_ratio: 0.5 + max_angle: 30 + pad_with_fixed_color: False + - SquareResizePad: + target_size: 800 + pad_ratio: 0.6 + - IaaAugment: + augmenter_args: + - { 'type': Fliplr, 'args': { 'p': 0.5 } } + - FCENetTargets: + fourier_degree: 5 + - 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', 'p3_maps', 'p4_maps', 'p5_maps'] # dataloader will return list in this order + loader: + shuffle: True + drop_last: False + batch_size_per_card: 6 + 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 + ignore_orientation: True + - DetLabelEncode: # Class handling label + - DetResizeForTest: + limit_type: 'min' + limit_side_len: 736 + - NormalizeImage: + scale: 1./255. + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: 'hwc' + - Pad: + - 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 diff --git a/test_tipc/configs/det_r50_dcn_fce_ctw_v2_0/train_infer_python.txt b/test_tipc/configs/det_r50_dcn_fce_ctw_v2_0/train_infer_python.txt new file mode 100644 index 0000000000000000000000000000000000000000..4454d018acd1dabf10162beac49216d05dea757d --- /dev/null +++ b/test_tipc/configs/det_r50_dcn_fce_ctw_v2_0/train_infer_python.txt @@ -0,0 +1,59 @@ +===========================train_params=========================== +model_name:det_r50_dcn_fce_ctw_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_dcn_fce_ctw_v2.0/det_r50_vd_dcn_fce_ctw.yml -o Global.print_batch_step=1 Train.loader.shuffle=false +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.checkpoints: +norm_export:tools/export_model.py -c test_tipc/configs/det_r50_dcn_fce_ctw_v2.0/det_r50_vd_dcn_fce_ctw.yml -o +quant_export:null +fpgm_export:null +distill_export:null +export1:null +export2:null +## +train_model:./inference/det_r50_dcn_fce_ctw_v2.0_train/best_accuracy +infer_export:tools/export_model.py -c test_tipc/configs/det_r50_dcn_fce_ctw_v2.0/det_r50_vd_dcn_fce_ctw.yml -o +infer_quant:False +inference:tools/infer/predict_det.py +--use_gpu:True|False +--enable_mkldnn:False +--cpu_threads:6 +--rec_batch_num:1 +--use_tensorrt:False +--precision:fp32 +--det_model_dir: +--image_dir:./inference/ch_det_data_50/all-sum-510/ +--save_log_path:null +--benchmark:True +--det_algorithm:FCE +===========================infer_benchmark_params========================== +random_infer_input:[{float32,[3,640,640]}];[{float32,[3,960,960]}] +===========================train_benchmark_params========================== +batch_size:6 +fp_items:fp32|fp16 +epoch:1 +--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile +flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096 \ No newline at end of file diff --git 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 index c6b6fc3ed79d0d717fe3dbd4cb9c8559ff8f07c4..844f42e9ad2b4eafbbd829de5711132f8119671f 100644 --- 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 @@ -20,7 +20,7 @@ Architecture: algorithm: EAST Transform: Backbone: - name: ResNet + name: ResNet_vd layers: 50 Neck: name: EASTFPN diff --git 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 index 8477a4fa74f7a0617104aa83617fc6f61b8234b3..5ee445a6cd03dfb888d1bc73eb51481a014cbb36 100644 --- 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 @@ -1,19 +1,19 @@ ===========================train_params=========================== model_name:det_r50_vd_east_v2_0 python:python3.7 -gpu_list:0 +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 +Global.pretrained_model:./pretrain_models/det_r50_vd_east_v2.0_train/best_accuracy 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 +norm_train:tools/train.py -c test_tipc/configs/det_r50_vd_east_v2_0/det_r50_vd_east.yml -o Global.pretrained_model=pretrain_models/det_r50_vd_east_v2.0_train/best_accuracy.pdparams Global.print_batch_step=1 Train.loader.shuffle=false pact_train:null fpgm_train:null distill_train:null @@ -55,4 +55,5 @@ random_infer_input:[{float32,[3,640,640]}];[{float32,[3,960,960]}] batch_size:8 fp_items:fp32|fp16 epoch:2 ---profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile \ No newline at end of file +--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile +flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096 \ No newline at end of file diff --git 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 index f7e60fd1968820ef093455473346a6b8f0f8d34e..c069d1f132db5fc512011ead0107cf4082c144be 100644 --- 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 @@ -8,7 +8,7 @@ Global: # evaluation is run every 125 iterations eval_batch_step: [ 0,1000 ] cal_metric_during_train: False - pretrained_model: + pretrained_model: ./pretrain_models/ResNet50_vd_ssld_pretrained checkpoints: #./output/det_r50_vd_pse_batch8_ColorJitter/best_accuracy save_inference_dir: use_visualdl: False @@ -20,7 +20,7 @@ Architecture: algorithm: PSE Transform: Backbone: - name: ResNet + name: ResNet_vd layers: 50 Neck: name: FPN diff --git 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 index 62da89fe1c8e3a7c2b7586eae6b2589f94237a2e..78d25f6b17e30d6b7b12ae3acc1b264febfa97da 100644 --- 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 @@ -13,7 +13,7 @@ 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 +norm_train:tools/train.py -c test_tipc/configs/det_r50_vd_pse_v2_0/det_r50_vd_pse.yml -o Global.print_batch_step=1 Train.loader.shuffle=false pact_train:null fpgm_train:null distill_train:null @@ -56,3 +56,4 @@ batch_size:8 fp_items:fp32|fp16 epoch:2 --profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile +flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096 \ No newline at end of file diff --git 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 similarity index 100% rename from test_tipc/configs/det_r50_vd_sast_icdar15_v2.0/det_r50_vd_sast_icdar2015.yml rename to test_tipc/configs/det_r50_vd_sast_icdar15_v2_0/det_r50_vd_sast_icdar2015.yml diff --git 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 similarity index 97% rename from test_tipc/configs/det_r50_vd_sast_icdar15_v2.0/train_infer_python.txt rename to test_tipc/configs/det_r50_vd_sast_icdar15_v2_0/train_infer_python.txt index b70ef46b4afb3a39f3bbd3d6274f0135a0646a37..718aff271d10683d8a9277a93337d16ce200bab2 100644 --- 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 @@ -1,5 +1,5 @@ ===========================train_params=========================== -model_name:det_r50_vd_sast_icdar15_v2.0 +model_name:det_r50_vd_sast_icdar15_v2_0 python:python3.7 gpu_list:0|0,1 Global.use_gpu:True|True diff --git 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 similarity index 100% rename from test_tipc/configs/det_r50_vd_sast_totaltext_v2.0/det_r50_vd_sast_totaltext.yml rename to test_tipc/configs/det_r50_vd_sast_totaltext_v2_0/det_r50_vd_sast_totaltext.yml diff --git 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 similarity index 97% rename from test_tipc/configs/det_r50_vd_sast_totaltext_v2.0/train_infer_python.txt rename to test_tipc/configs/det_r50_vd_sast_totaltext_v2_0/train_infer_python.txt index 7be5af7ddee0ed0f688980f5d5dca5a99c9705a0..cf176b3cffaa07f81d01b2ec94fa6231b73590ef 100644 --- 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 @@ -1,5 +1,5 @@ ===========================train_params=========================== -model_name:det_r50_vd_sast_totaltext_v2.0 +model_name:det_r50_vd_sast_totaltext_v2_0 python:python3.7 gpu_list:0|0,1 Global.use_gpu:True|True diff --git a/test_tipc/configs/en_table_structure/table_mv3.yml b/test_tipc/configs/en_table_structure/table_mv3.yml index adf326bd02aeff4683c8f37a704125b4e426efa9..5d8e84c95c477a639130a342c6c72345e97701da 100755 --- a/test_tipc/configs/en_table_structure/table_mv3.yml +++ b/test_tipc/configs/en_table_structure/table_mv3.yml @@ -6,19 +6,18 @@ Global: save_model_dir: ./output/table_mv3/ save_epoch_step: 3 # evaluation is run every 400 iterations after the 0th iteration - eval_batch_step: [0, 400] + eval_batch_step: [0, 40000] cal_metric_during_train: True pretrained_model: - checkpoints: + checkpoints: save_inference_dir: use_visualdl: False - infer_img: doc/table/table.jpg + infer_img: ppstructure/docs/table/table.jpg + save_res_path: output/table_mv3 # for data or label process character_dict_path: ppocr/utils/dict/table_structure_dict.txt character_type: en - max_text_length: 100 - max_elem_length: 800 - max_cell_num: 500 + max_text_length: 800 infer_mode: False process_total_num: 0 process_cut_num: 0 @@ -44,11 +43,8 @@ Architecture: Head: name: TableAttentionHead hidden_size: 256 - l2_decay: 0.00001 loc_type: 2 - max_text_length: 100 - max_elem_length: 800 - max_cell_num: 500 + max_text_length: 800 Loss: name: TableAttentionLoss @@ -61,28 +57,34 @@ PostProcess: Metric: name: TableMetric main_indicator: acc + compute_bbox_metric: false # cost many time, set False for training Train: dataset: name: PubTabDataSet data_dir: ./train_data/pubtabnet/train - label_file_path: ./train_data/pubtabnet/train.jsonl + label_file_list: [./train_data/pubtabnet/train.jsonl] transforms: - DecodeImage: # load image img_mode: BGR channel_first: False + - TableLabelEncode: + learn_empty_box: False + merge_no_span_structure: False + replace_empty_cell_token: False + - TableBoxEncode: - ResizeTableImage: max_len: 488 - - TableLabelEncode: - NormalizeImage: scale: 1./255. mean: [0.485, 0.456, 0.406] std: [0.229, 0.224, 0.225] order: 'hwc' - PaddingTableImage: + size: [488, 488] - ToCHWImage: - KeepKeys: - keep_keys: ['image', 'structure', 'bbox_list', 'sp_tokens', 'bbox_list_mask'] + keep_keys: [ 'image', 'structure', 'bboxes', 'bbox_masks', 'shape' ] loader: shuffle: True batch_size_per_card: 32 @@ -93,23 +95,28 @@ Eval: dataset: name: PubTabDataSet data_dir: ./train_data/pubtabnet/test/ - label_file_path: ./train_data/pubtabnet/test.jsonl + label_file_list: [./train_data/pubtabnet/test.jsonl] transforms: - DecodeImage: # load image img_mode: BGR channel_first: False + - TableLabelEncode: + learn_empty_box: False + merge_no_span_structure: False + replace_empty_cell_token: False + - TableBoxEncode: - ResizeTableImage: max_len: 488 - - TableLabelEncode: - NormalizeImage: scale: 1./255. mean: [0.485, 0.456, 0.406] std: [0.229, 0.224, 0.225] order: 'hwc' - PaddingTableImage: + size: [488, 488] - ToCHWImage: - KeepKeys: - keep_keys: ['image', 'structure', 'bbox_list', 'sp_tokens', 'bbox_list_mask'] + keep_keys: [ 'image', 'structure', 'bboxes', 'bbox_masks', 'shape' ] loader: shuffle: False drop_last: False diff --git a/test_tipc/configs/en_table_structure/train_infer_python.txt b/test_tipc/configs/en_table_structure/train_infer_python.txt index d9f3b30e16c75281a929130d877b947a23c16190..633b6185d976ac61408283025bd4ba305187317d 100644 --- a/test_tipc/configs/en_table_structure/train_infer_python.txt +++ b/test_tipc/configs/en_table_structure/train_infer_python.txt @@ -13,7 +13,7 @@ train_infer_img_dir:./ppstructure/docs/table/table.jpg null:null ## trainer:norm_train -norm_train:tools/train.py -c test_tipc/configs/en_table_structure/table_mv3.yml -o +norm_train:tools/train.py -c test_tipc/configs/en_table_structure/table_mv3.yml -o Global.print_batch_step=1 Train.loader.shuffle=false pact_train:null fpgm_train:null distill_train:null @@ -27,7 +27,7 @@ null:null ===========================infer_params=========================== Global.save_inference_dir:./output/ Global.checkpoints: -norm_export:tools/export_model.py -c test_tipc/configs/en_table_structure/table_mv3.yml -o +norm_export:tools/export_model.py -c test_tipc/configs/en_table_structure/table_mv3.yml -o quant_export: fpgm_export: distill_export:null @@ -51,3 +51,9 @@ null:null null:null ===========================infer_benchmark_params========================== random_infer_input:[{float32,[3,488,488]}] +===========================train_benchmark_params========================== +batch_size:32 +fp_items:fp32|fp16 +epoch:1 +--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile +flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096 diff --git a/test_tipc/configs/en_table_structure/train_pact_infer_python.txt b/test_tipc/configs/en_table_structure/train_pact_infer_python.txt index f62e8b68bc6c1af06a65a8dfb438d5d63576e123..9890b906a1d3b1127352af567dca0d7186f94694 100644 --- a/test_tipc/configs/en_table_structure/train_pact_infer_python.txt +++ b/test_tipc/configs/en_table_structure/train_pact_infer_python.txt @@ -6,7 +6,7 @@ Global.use_gpu:True|True Global.auto_cast:fp32 Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=50 Global.save_model_dir:./output/ -Train.loader.batch_size_per_card:lite_train_lite_infer=16|whole_train_whole_infer=128 +Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_whole_infer=2 Global.pretrained_model:./pretrain_models/en_ppocr_mobile_v2.0_table_structure_train/best_accuracy train_model_name:latest train_infer_img_dir:./ppstructure/docs/table/table.jpg diff --git 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 similarity index 98% rename from test_tipc/configs/rec_mv3_none_bilstm_ctc_v2.0/rec_icdar15_train.yml rename to test_tipc/configs/rec_mv3_none_bilstm_ctc_v2_0/rec_icdar15_train.yml index 463e8d1d7d7f43ba7b48810e2a2e8552eb5e4fe3..b0ba615293153cc3bbeb5b47d053596306ee45e2 100644 --- 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 @@ -6,7 +6,7 @@ Global: save_model_dir: ./output/rec/mv3_none_bilstm_ctc/ save_epoch_step: 3 # evaluation is run every 2000 iterations - eval_batch_step: [0, 2000] + eval_batch_step: [0, 20000] cal_metric_during_train: True pretrained_model: checkpoints: diff --git 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 similarity index 77% rename from test_tipc/configs/rec_mv3_none_bilstm_ctc_v2.0/train_infer_python.txt rename to test_tipc/configs/rec_mv3_none_bilstm_ctc_v2_0/train_infer_python.txt index 39bf9227902480ffe4ed37d454c21d6a163c41bd..9e1fb14a0a20cabcf63883ab5fc5efa173914b05 100644 --- 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 @@ -1,5 +1,5 @@ ===========================train_params=========================== -model_name:rec_mv3_none_bilstm_ctc_v2.0 +model_name:rec_mv3_none_bilstm_ctc_v2_0 python:python3.7 gpu_list:0|0,1 Global.use_gpu:True|True @@ -13,7 +13,7 @@ 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 +norm_train:tools/train.py -c test_tipc/configs/rec_mv3_none_bilstm_ctc_v2.0/rec_icdar15_train.yml -o Global.print_batch_step=4 Train.loader.shuffle=false pact_train:null fpgm_train:null distill_train:null @@ -50,4 +50,10 @@ inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/ic15_dic --benchmark:True null:null ===========================infer_benchmark_params========================== -random_infer_input:[{float32,[3,32,100]}] \ No newline at end of file +random_infer_input:[{float32,[3,32,100]}] +===========================train_benchmark_params========================== +batch_size:256 +fp_items:fp32|fp16 +epoch:4 +--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile +flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096 diff --git 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 similarity index 100% rename from test_tipc/configs/rec_mv3_none_none_ctc_v2.0/rec_icdar15_train.yml rename to test_tipc/configs/rec_mv3_none_none_ctc_v2_0/rec_icdar15_train.yml diff --git 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 similarity index 95% rename from test_tipc/configs/rec_mv3_none_none_ctc_v2.0/train_infer_python.txt rename to test_tipc/configs/rec_mv3_none_none_ctc_v2_0/train_infer_python.txt index 593de3ff20aa9890e7d9a02a9e5ca5b130e5a266..09fa408cab0c44e1a0fe615e8ed81503289b64cf 100644 --- 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 @@ -1,5 +1,5 @@ ===========================train_params=========================== -model_name:rec_mv3_none_none_ctc_v2.0 +model_name:rec_mv3_none_none_ctc_v2_0 python:python3.7 gpu_list:0|0,1 Global.use_gpu:True|True diff --git 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 similarity index 100% rename from test_tipc/configs/rec_mv3_tps_bilstm_att_v2.0/rec_mv3_tps_bilstm_att.yml rename to test_tipc/configs/rec_mv3_tps_bilstm_att_v2_0/rec_mv3_tps_bilstm_att.yml diff --git 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 similarity index 97% rename from test_tipc/configs/rec_mv3_tps_bilstm_att_v2.0/train_infer_python.txt rename to test_tipc/configs/rec_mv3_tps_bilstm_att_v2_0/train_infer_python.txt index 1b2d9abb0f00467ce92c4f51f97c283bc3e85c5e..6a1d76810ca60e453c417fa6aaa58d0ff107380a 100644 --- 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 @@ -1,5 +1,5 @@ ===========================train_params=========================== -model_name:rec_mv3_tps_bilstm_att_v2.0 +model_name:rec_mv3_tps_bilstm_att_v2_0 python:python3.7 gpu_list:0|0,1 Global.use_gpu:True|True diff --git 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 similarity index 100% rename from test_tipc/configs/rec_mv3_tps_bilstm_ctc_v2.0/rec_icdar15_train.yml rename to test_tipc/configs/rec_mv3_tps_bilstm_ctc_v2_0/rec_icdar15_train.yml diff --git 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 similarity index 97% rename from test_tipc/configs/rec_mv3_tps_bilstm_ctc_v2.0/train_infer_python.txt rename to test_tipc/configs/rec_mv3_tps_bilstm_ctc_v2_0/train_infer_python.txt index 1367c7abd4c9ca5b0c6f1eb291dd2af8d9fa4de4..1b6be1b04b3f55110c44a5b1ded2d3a492eeaa60 100644 --- 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 @@ -1,5 +1,5 @@ ===========================train_params=========================== -model_name:rec_mv3_tps_bilstm_ctc_v2.0 +model_name:rec_mv3_tps_bilstm_ctc_v2_0 python:python3.7 gpu_list:0|0,1 Global.use_gpu:True|True diff --git a/test_tipc/configs/rec_r32_gaspin_bilstm_att/rec_r32_gaspin_bilstm_att.yml b/test_tipc/configs/rec_r32_gaspin_bilstm_att/rec_r32_gaspin_bilstm_att.yml new file mode 100644 index 0000000000000000000000000000000000000000..21d56b685c7da7b1db43acb6570bf7f40d0426fa --- /dev/null +++ b/test_tipc/configs/rec_r32_gaspin_bilstm_att/rec_r32_gaspin_bilstm_att.yml @@ -0,0 +1,117 @@ +Global: + use_gpu: True + epoch_num: 6 + log_smooth_window: 50 + print_batch_step: 50 + save_model_dir: ./output/rec/rec_r32_gaspin_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: pretrain_models/rec_r32_gaspin_bilstm_att_train/best_accuracy + 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/dict/spin_dict.txt + max_text_length: 25 + infer_mode: False + use_space_char: False + save_res_path: ./output/rec/predicts_r32_gaspin_bilstm_att.txt + + +Optimizer: + name: AdamW + beta1: 0.9 + beta2: 0.999 + lr: + name: Piecewise + decay_epochs: [3, 4, 5] + values: [0.001, 0.0003, 0.00009, 0.000027] + + clip_norm: 5 + +Architecture: + model_type: rec + algorithm: SPIN + in_channels: 1 + Transform: + name: GA_SPIN + offsets: True + default_type: 6 + loc_lr: 0.1 + stn: True + Backbone: + name: ResNet32 + out_channels: 512 + Neck: + name: SequenceEncoder + encoder_type: cascadernn + hidden_size: 256 + out_channels: [256, 512] + with_linear: True + Head: + name: SPINAttentionHead + hidden_size: 256 + + +Loss: + name: SPINAttentionLoss + ignore_index: 0 + +PostProcess: + name: SPINLabelDecode + use_space_char: False + + +Metric: + name: RecMetric + main_indicator: acc + is_filter: True + +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 + - SPINLabelEncode: # Class handling label + - SPINRecResizeImg: + image_shape: [100, 32] + interpolation : 2 + mean: [127.5] + std: [127.5] + - KeepKeys: + keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order + loader: + shuffle: True + batch_size_per_card: 128 + drop_last: True + 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 + - SPINLabelEncode: # Class handling label + - SPINRecResizeImg: + image_shape: [100, 32] + interpolation : 2 + mean: [127.5] + std: [127.5] + - KeepKeys: + keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order + loader: + shuffle: False + drop_last: False + batch_size_per_card: 1 + num_workers: 1 diff --git a/test_tipc/configs/rec_r32_gaspin_bilstm_att/train_infer_python.txt b/test_tipc/configs/rec_r32_gaspin_bilstm_att/train_infer_python.txt new file mode 100644 index 0000000000000000000000000000000000000000..115dfd661abc64db9e14c629f79099be7b6ff0e0 --- /dev/null +++ b/test_tipc/configs/rec_r32_gaspin_bilstm_att/train_infer_python.txt @@ -0,0 +1,53 @@ +===========================train_params=========================== +model_name:rec_r32_gaspin_bilstm_att +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=16|whole_train_whole_infer=64 +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_r32_gaspin_bilstm_att/rec_r32_gaspin_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_r32_gaspin_bilstm_att/rec_r32_gaspin_bilstm_att.yml -o +null:null +## +===========================infer_params=========================== +Global.save_inference_dir:./output/ +Global.checkpoints: +norm_export:tools/export_model.py -c test_tipc/configs/rec_r32_gaspin_bilstm_att/rec_r32_gaspin_bilstm_att.yml -o +quant_export:null +fpgm_export:null +distill_export:null +export1:null +export2:null +## +train_model:./inference/rec_r32_gaspin_bilstm_att/best_accuracy +infer_export:tools/export_model.py -c test_tipc/configs/rec_r32_gaspin_bilstm_att/rec_r32_gaspin_bilstm_att.yml -o +infer_quant:False +inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/dict/spin_dict.txt --use_space_char=False --rec_image_shape="3,32,100" --rec_algorithm="SPIN" +--use_gpu:True|False +--enable_mkldnn:False +--cpu_threads:6 +--rec_batch_num:1|6 +--use_tensorrt:False +--precision:fp32 +--rec_model_dir: +--image_dir:./inference/rec_inference +--save_log_path:./test/output/ +--benchmark:True +null:null +===========================infer_benchmark_params========================== +random_infer_input:[{float32,[3,32,100]}] diff --git 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 similarity index 100% rename from test_tipc/configs/rec_r34_vd_none_bilstm_ctc_v2.0/rec_icdar15_train.yml rename to test_tipc/configs/rec_r34_vd_none_bilstm_ctc_v2_0/rec_icdar15_train.yml diff --git 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 similarity index 95% rename from test_tipc/configs/rec_r34_vd_none_bilstm_ctc_v2.0/train_infer_python.txt rename to test_tipc/configs/rec_r34_vd_none_bilstm_ctc_v2_0/train_infer_python.txt index 46aa3d719051a4f124583f88709026569d95c1c7..98953b31fad40902fd04b7564459c4d8fee5bdaf 100644 --- 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 @@ -1,5 +1,5 @@ ===========================train_params=========================== -model_name:rec_r34_vd_none_bilstm_ctc_v2.0 +model_name:rec_r34_vd_none_bilstm_ctc_v2_0 python:python3.7 gpu_list:0|0,1 Global.use_gpu:True|True diff --git 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 similarity index 100% rename from test_tipc/configs/rec_r34_vd_none_none_ctc_v2.0/rec_icdar15_train.yml rename to test_tipc/configs/rec_r34_vd_none_none_ctc_v2_0/rec_icdar15_train.yml diff --git 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 similarity index 95% rename from test_tipc/configs/rec_r34_vd_none_none_ctc_v2.0/train_infer_python.txt rename to test_tipc/configs/rec_r34_vd_none_none_ctc_v2_0/train_infer_python.txt index 3e066d7b72a6a707322b3aabe41ca6d698496433..37e66e826623979fb9859f23e86b43fe3aced444 100644 --- 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 @@ -1,5 +1,5 @@ ===========================train_params=========================== -model_name:rec_r34_vd_none_none_ctc_v2.0 +model_name:rec_r34_vd_none_none_ctc_v2_0 python:python3.7 gpu_list:0|0,1 Global.use_gpu:True|True diff --git 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 similarity index 100% rename from test_tipc/configs/rec_r34_vd_tps_bilstm_att_v2.0/rec_r34_vd_tps_bilstm_att.yml rename to test_tipc/configs/rec_r34_vd_tps_bilstm_att_v2_0/rec_r34_vd_tps_bilstm_att.yml diff --git 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 similarity index 97% rename from test_tipc/configs/rec_r34_vd_tps_bilstm_att_v2.0/train_infer_python.txt rename to test_tipc/configs/rec_r34_vd_tps_bilstm_att_v2_0/train_infer_python.txt index 1e4f46633efbf36fc78ed2beb7ed883d1483b3b0..772db83770620faa488e74bf9443202e87dbb9d9 100644 --- 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 @@ -1,5 +1,5 @@ ===========================train_params=========================== -model_name:rec_r34_vd_tps_bilstm_att_v2.0 +model_name:rec_r34_vd_tps_bilstm_att_v2_0 python:python3.7 gpu_list:0|0,1 Global.use_gpu:True|True diff --git 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 similarity index 100% rename from test_tipc/configs/rec_r34_vd_tps_bilstm_ctc_v2.0/rec_icdar15_train.yml rename to test_tipc/configs/rec_r34_vd_tps_bilstm_ctc_v2_0/rec_icdar15_train.yml diff --git 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 similarity index 97% rename from test_tipc/configs/rec_r34_vd_tps_bilstm_ctc_v2.0/train_infer_python.txt rename to test_tipc/configs/rec_r34_vd_tps_bilstm_ctc_v2_0/train_infer_python.txt index 9e795b66453039696ed5eedb92fba5e25150413c..bc1ea7e85158a8025367fafda99c48618ac9aa14 100644 --- 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 @@ -1,5 +1,5 @@ ===========================train_params=========================== -model_name:rec_r34_vd_tps_bilstm_ctc_v2.0 +model_name:rec_r34_vd_tps_bilstm_ctc_v2_0 python:python3.7 gpu_list:0|0,1 Global.use_gpu:True|True diff --git a/test_tipc/configs/table_master/table_master.yml b/test_tipc/configs/table_master/table_master.yml new file mode 100644 index 0000000000000000000000000000000000000000..c519b5b8f464d8843888155387b74a8416821f2f --- /dev/null +++ b/test_tipc/configs/table_master/table_master.yml @@ -0,0 +1,136 @@ +Global: + use_gpu: true + epoch_num: 17 + log_smooth_window: 20 + print_batch_step: 100 + save_model_dir: ./output/table_master/ + save_epoch_step: 17 + eval_batch_step: [0, 6259] + cal_metric_during_train: true + pretrained_model: null + checkpoints: + save_inference_dir: output/table_master/infer + use_visualdl: false + infer_img: ppstructure/docs/table/table.jpg + save_res_path: ./output/table_master + character_dict_path: ppocr/utils/dict/table_master_structure_dict.txt + infer_mode: false + max_text_length: 500 + process_total_num: 0 + process_cut_num: 0 + + +Optimizer: + name: Adam + beta1: 0.9 + beta2: 0.999 + lr: + name: MultiStepDecay + learning_rate: 0.001 + milestones: [12, 15] + gamma: 0.1 + warmup_epoch: 0.02 + regularizer: + name: L2 + factor: 0.0 + +Architecture: + model_type: table + algorithm: TableMaster + Backbone: + name: TableResNetExtra + gcb_config: + ratio: 0.0625 + headers: 1 + att_scale: False + fusion_type: channel_add + layers: [False, True, True, True] + layers: [1,2,5,3] + Head: + name: TableMasterHead + hidden_size: 512 + headers: 8 + dropout: 0 + d_ff: 2024 + max_text_length: 500 + +Loss: + name: TableMasterLoss + ignore_index: 42 # set to len of dict + 3 + +PostProcess: + name: TableMasterLabelDecode + box_shape: pad + +Metric: + name: TableMetric + main_indicator: acc + compute_bbox_metric: False + +Train: + dataset: + name: PubTabDataSet + data_dir: ./train_data/pubtabnet/train + label_file_list: [./train_data/pubtabnet/train.jsonl] + transforms: + - DecodeImage: + img_mode: BGR + channel_first: False + - TableMasterLabelEncode: + learn_empty_box: False + merge_no_span_structure: True + replace_empty_cell_token: True + - ResizeTableImage: + max_len: 480 + resize_bboxes: True + - PaddingTableImage: + size: [480, 480] + - TableBoxEncode: + use_xywh: True + - NormalizeImage: + scale: 1./255. + mean: [0.5, 0.5, 0.5] + std: [0.5, 0.5, 0.5] + order: hwc + - ToCHWImage: null + - KeepKeys: + keep_keys: [image, structure, bboxes, bbox_masks, shape] + loader: + shuffle: True + batch_size_per_card: 10 + drop_last: True + num_workers: 8 + +Eval: + dataset: + name: PubTabDataSet + data_dir: ./train_data/pubtabnet/test/ + label_file_list: [./train_data/pubtabnet/test.jsonl] + transforms: + - DecodeImage: + img_mode: BGR + channel_first: False + - TableMasterLabelEncode: + learn_empty_box: False + merge_no_span_structure: True + replace_empty_cell_token: True + - ResizeTableImage: + max_len: 480 + resize_bboxes: True + - PaddingTableImage: + size: [480, 480] + - TableBoxEncode: + use_xywh: True + - NormalizeImage: + scale: 1./255. + mean: [0.5, 0.5, 0.5] + std: [0.5, 0.5, 0.5] + order: hwc + - ToCHWImage: null + - KeepKeys: + keep_keys: [image, structure, bboxes, bbox_masks, shape] + loader: + shuffle: False + drop_last: False + batch_size_per_card: 10 + num_workers: 8 \ No newline at end of file diff --git a/test_tipc/configs/table_master/train_infer_python.txt b/test_tipc/configs/table_master/train_infer_python.txt new file mode 100644 index 0000000000000000000000000000000000000000..56b8e636026939ae8cd700308690010e1300d8f6 --- /dev/null +++ b/test_tipc/configs/table_master/train_infer_python.txt @@ -0,0 +1,53 @@ +===========================train_params=========================== +model_name:table_master +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=17 +Global.save_model_dir:./output/ +Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_whole_infer=4 +Global.pretrained_model:./pretrain_models/table_structure_tablemaster_train/best_accuracy +train_model_name:latest +train_infer_img_dir:./ppstructure/docs/table/table.jpg +null:null +## +trainer:norm_train +norm_train:tools/train.py -c test_tipc/configs/table_master/table_master.yml -o Global.print_batch_step=10 +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.checkpoints: +norm_export:tools/export_model.py -c test_tipc/configs/table_master/table_master.yml -o +quant_export: +fpgm_export: +distill_export:null +export1:null +export2:null +## +infer_model:null +infer_export:null +infer_quant:False +inference:ppstructure/table/predict_structure.py --table_char_dict_path=./ppocr/utils/dict/table_master_structure_dict.txt --image_dir=./ppstructure/docs/table/table.jpg --output ./output/table --table_algorithm=TableMaster --table_max_len=480 +--use_gpu:True|False +--enable_mkldnn:False +--cpu_threads:6 +--rec_batch_num:1 +--use_tensorrt:False +--precision:fp32 +--table_model_dir: +--image_dir:./ppstructure/docs/table/table.jpg +null:null +--benchmark:False +null:null +===========================infer_benchmark_params========================== +random_infer_input:[{float32,[3,480,480]}] diff --git a/test_tipc/docs/benchmark_train.md b/test_tipc/docs/benchmark_train.md index 3f846574ff75c8602ba1222977362c143582f560..0196edc2473b774d254a9bbed02e008c7991a971 100644 --- a/test_tipc/docs/benchmark_train.md +++ b/test_tipc/docs/benchmark_train.md @@ -9,7 +9,7 @@ ```shell # 运行格式：bash test_tipc/prepare.sh train_benchmark.txt mode -bash test_tipc/prepare.sh test_tipc/configs/det_mv3_db_v2_0/train_benchmark.txt benchmark_train +bash test_tipc/prepare.sh test_tipc/configs/det_mv3_db_v2_0/train_infer_python.txt benchmark_train ``` ## 1.2 功能测试 @@ -33,7 +33,7 @@ dynamic_bs8_fp32_DP_N1C1为test_tipc/benchmark_train.sh传入的参数，格式 ## 2. 日志输出 -运行后将保存模型的训练日志和解析日志，使用 `test_tipc/configs/det_mv3_db_v2_0/train_benchmark.txt` 参数文件的训练日志解析结果是： +运行后将保存模型的训练日志和解析日志，使用 `test_tipc/configs/det_mv3_db_v2_0/train_infer_python.txt` 参数文件的训练日志解析结果是： ``` {"model_branch": "dygaph", "model_commit": "7c39a1996b19087737c05d883fd346d2f39dbcc0", "model_name": "det_mv3_db_v2_0_bs8_fp32_SingleP_DP", "batch_size": 8, "fp_item": "fp32", "run_process_type": "SingleP", "run_mode": "DP", "convergence_value": "5.413110", "convergence_key": "loss:", "ips": 19.333, "speed_unit": "samples/s", "device_num": "N1C1", "model_run_time": "0", "frame_commit": "8cc09552473b842c651ead3b9848d41827a3dbab", "frame_version": "0.0.0"} @@ -51,3 +51,25 @@ train_log/ ├── PaddleOCR_det_mv3_db_v2_0_bs8_fp32_SingleP_DP_N1C1_log └── PaddleOCR_det_mv3_db_v2_0_bs8_fp32_SingleP_DP_N1C4_log ``` +## 3. 各模型单卡性能数据一览 + +*注：本节中的速度指标均使用单卡（1块Nvidia V100 16G GPU）测得。通常情况下。 + + +|模型名称|配置文件|大数据集 float32 fps |小数据集 float32 fps |diff |大数据集 float16 fps|小数据集 float16 fps| diff | 大数据集大小 | 小数据集大小 | +|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:| +| ch_ppocr_mobile_v2.0_det |[config](../configs/ch_ppocr_mobile_v2.0_det/train_infer_python.txt) | 53.836 | 53.343 / 53.914 / 52.785 |0.020940758 | 45.574 | 45.57 / 46.292 / 46.213 | 0.015596647 | 10,000| 2,000| +| ch_ppocr_mobile_v2.0_rec |[config](../configs/ch_ppocr_mobile_v2.0_rec/train_infer_python.txt) | 2083.311 | 2043.194 / 2066.372 / 2093.317 |0.023944295 | 2153.261 | 2167.561 / 2165.726 / 2155.614| 0.005511725 | 600,000| 160,000| +| ch_ppocr_server_v2.0_det |[config](../configs/ch_ppocr_server_v2.0_det/train_infer_python.txt) | 20.716 | 20.739 / 20.807 / 20.755 |0.003268131 | 20.592 | 20.498 / 20.993 / 20.75| 0.023579288 | 10,000| 2,000| +| ch_ppocr_server_v2.0_rec |[config](../configs/ch_ppocr_server_v2.0_rec/train_infer_python.txt) | 528.56 | 528.386 / 528.991 / 528.391 |0.001143687 | 1189.788 | 1190.007 / 1176.332 / 1192.084| 0.013213834 | 600,000| 160,000| +| ch_PP-OCRv2_det |[config](../configs/ch_PP-OCRv2_det/train_infer_python.txt) | 13.87 | 13.386 / 13.529 / 13.428 |0.010569887 | 17.847 | 17.746 / 17.908 / 17.96| 0.011915367 | 10,000| 2,000| +| ch_PP-OCRv2_rec |[config](../configs/ch_PP-OCRv2_rec/train_infer_python.txt) | 109.248 | 106.32 / 106.318 / 108.587 |0.020895687 | 117.491 | 117.62 / 117.757 / 117.726| 0.001163413 | 140,000| 40,000| +| det_mv3_db_v2.0 |[config](../configs/det_mv3_db_v2_0/train_infer_python.txt) | 61.802 | 62.078 / 61.802 / 62.008 |0.00444602 | 82.947 | 84.294 / 84.457 / 84.005| 0.005351836 | 10,000| 2,000| +| det_r50_vd_db_v2.0 |[config](../configs/det_r50_vd_db_v2.0/train_infer_python.txt) | 29.955 | 29.092 / 29.31 / 28.844 |0.015899011 | 51.097 |50.367 / 50.879 / 50.227| 0.012814717 | 10,000| 2,000| +| det_r50_vd_east_v2.0 |[config](../configs/det_r50_vd_east_v2.0/train_infer_python.txt) | 42.485 | 42.624 / 42.663 / 42.561 |0.00239083 | 67.61 |67.825/ 68.299/ 68.51| 0.00999854 | 10,000| 2,000| +| det_r50_vd_pse_v2.0 |[config](../configs/det_r50_vd_pse_v2.0/train_infer_python.txt) | 16.455 | 16.517 / 16.555 / 16.353 |0.012201752 | 27.02 |27.288 / 27.152 / 27.408| 0.009340339 | 10,000| 2,000| +| rec_mv3_none_bilstm_ctc_v2.0 |[config](../configs/rec_mv3_none_bilstm_ctc_v2.0/train_infer_python.txt) | 2288.358 | 2291.906 / 2293.725 / 2290.05 |0.001602197 | 2336.17 |2327.042 / 2328.093 / 2344.915| 0.007622025 | 600,000| 160,000| +| PP-Structure-table |[config](../configs/en_table_structure/train_infer_python.txt) | 14.151 | 14.077 / 14.23 / 14.25 |0.012140351 | 16.285 | 16.595 / 16.878 / 16.531 | 0.020559308 | 20,000| 5,000| +| det_r50_dcn_fce_ctw_v2.0 |[config](../configs/det_r50_dcn_fce_ctw_v2.0/train_infer_python.txt) | 14.057 | 14.029 / 14.02 / 14.014 |0.001069214 | 18.298 |18.411 / 18.376 / 18.331| 0.004345228 | 10,000| 2,000| +| ch_PP-OCRv3_det |[config](../configs/ch_PP-OCRv3_det/train_infer_python.txt) | 8.622 | 8.431 / 8.423 / 8.479|0.006604552 | 14.203 |14.346 14.468 14.23| 0.016450097 | 10,000| 2,000| +| ch_PP-OCRv3_rec |[config](../configs/ch_PP-OCRv3_rec/train_infer_python.txt) | 90.239 | 90.077 / 91.513 / 91.325|0.01569176 | | | | 160,000| 40,000| diff --git a/test_tipc/prepare.sh b/test_tipc/prepare.sh index 2c9bd2901b52ff7d4b6af483a9aa201aef339099..88154d6187b9ae45bdc8f2ae3930d4858302b537 100644 --- a/test_tipc/prepare.sh +++ b/test_tipc/prepare.sh @@ -22,21 +22,89 @@ trainer_list=$(func_parser_value "${lines[14]}") if [ ${MODE} = "benchmark_train" ];then pip install -r requirements.txt - if [[ ${model_name} =~ "det_mv3_db_v2_0" || ${model_name} =~ "det_r50_vd_east_v2_0" || ${model_name} =~ "det_r50_vd_pse_v2_0" || ${model_name} =~ "det_r18_db_v2_0" ]];then - rm -rf ./train_data/icdar2015 + if [[ ${model_name} =~ "ch_ppocr_mobile_v2_0_det" || ${model_name} =~ "det_mv3_db_v2_0" ]];then wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/pretrained/MobileNetV3_large_x0_5_pretrained.pdparams --no-check-certificate - wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015.tar --no-check-certificate - cd ./train_data/ && tar xf icdar2015.tar && cd ../ + rm -rf ./train_data/icdar2015 + wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dataset/icdar2015_benckmark.tar --no-check-certificate + cd ./train_data/ && tar xf icdar2015_benckmark.tar + ln -s ./icdar2015_benckmark ./icdar2015 + cd ../ + fi + if [[ ${model_name} =~ "ch_ppocr_server_v2_0_det" || ${model_name} =~ "ch_PP-OCRv3_det" ]];then + rm -rf ./train_data/icdar2015 + wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dataset/icdar2015_benckmark.tar --no-check-certificate + cd ./train_data/ && tar xf icdar2015_benckmark.tar + ln -s ./icdar2015_benckmark ./icdar2015 + cd ../ fi - if [[ ${model_name} =~ "det_r50_vd_east_v2_0" || ${model_name} =~ "det_r50_vd_pse_v2_0" ]];then + if [[ ${model_name} =~ "ch_PP-OCRv2_det" ]];then + wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_train.tar --no-check-certificate + cd ./pretrain_models/ && tar xf ch_ppocr_server_v2.0_det_train.tar && cd ../ + rm -rf ./train_data/icdar2015 + wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dataset/icdar2015_benckmark.tar --no-check-certificate + cd ./train_data/ && tar xf icdar2015_benckmark.tar + ln -s ./icdar2015_benckmark ./icdar2015 + cd ../ + fi + if [[ ${model_name} =~ "det_r50_vd_east_v2_0" ]]; then + wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_east_v2.0_train.tar --no-check-certificate + cd ./pretrain_models/ && tar xf det_r50_vd_east_v2.0_train.tar && cd ../ + rm -rf ./train_data/icdar2015 + wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dataset/icdar2015_benckmark.tar --no-check-certificate + cd ./train_data/ && tar xf icdar2015_benckmark.tar + ln -s ./icdar2015_benckmark ./icdar2015 + cd ../ + fi + if [[ ${model_name} =~ "det_r50_db_v2_0" || ${model_name} =~ "det_r50_vd_pse_v2_0" ]];then wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/pretrained/ResNet50_vd_ssld_pretrained.pdparams --no-check-certificate - wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015.tar --no-check-certificate - cd ./train_data/ && tar xf icdar2015.tar && cd ../ + rm -rf ./train_data/icdar2015 + wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dataset/icdar2015_benckmark.tar --no-check-certificate + cd ./train_data/ && tar xf icdar2015_benckmark.tar + ln -s ./icdar2015_benckmark ./icdar2015 + cd ../ fi if [[ ${model_name} =~ "det_r18_db_v2_0" ]];then wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/pretrained/ResNet18_vd_pretrained.pdparams --no-check-certificate - wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015.tar --no-check-certificate - cd ./train_data/ && tar xf icdar2015.tar && cd ../ + rm -rf ./train_data/icdar2015 + wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dataset/icdar2015_benckmark.tar --no-check-certificate + cd ./train_data/ && tar xf icdar2015_benckmark.tar + ln -s ./icdar2015_benckmark ./icdar2015 + cd ../ + fi + if [[ ${model_name} =~ "ch_ppocr_mobile_v2_0_rec" || ${model_name} =~ "ch_ppocr_server_v2_0_rec" || ${model_name} =~ "ch_PP-OCRv2_rec" || ${model_name} =~ "rec_mv3_none_bilstm_ctc_v2_0" || ${model_name} =~ "ch_PP-OCRv3_rec" ]];then + rm -rf ./train_data/ic15_data + wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dataset/ic15_data_benckmark.tar --no-check-certificate + cd ./train_data/ && tar xf ic15_data_benckmark.tar + ln -s ./ic15_data_benckmark ./ic15_data + cd ../ + fi + if [[ ${model_name} =~ "ch_PP-OCRv2_rec" || ${model_name} =~ "ch_PP-OCRv3_rec" ]];then + rm -rf ./train_data/ic15_data + wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dataset/ic15_data_benckmark.tar --no-check-certificate + cd ./train_data/ && tar xf ic15_data_benckmark.tar + ln -s ./ic15_data_benckmark ./ic15_data + cd ic15_data + mv rec_gt_train4w.txt rec_gt_train.txt + cd ../ + cd ../ + fi + if [[ ${model_name} == "en_table_structure" ]];then + wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.1/table/en_ppocr_mobile_v2.0_table_structure_train.tar --no-check-certificate + cd ./pretrain_models/ && tar xf en_ppocr_mobile_v2.0_table_structure_train.tar && cd ../ + rm -rf ./train_data/pubtabnet + wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dataset/pubtabnet_benckmark.tar --no-check-certificate + cd ./train_data/ && tar xf pubtabnet_benckmark.tar + ln -s ./pubtabnet_benckmark ./pubtabnet + cd ../ + fi + if [[ ${model_name} == "det_r50_dcn_fce_ctw_v2_0" ]]; then + wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/contribution/det_r50_dcn_fce_ctw_v2.0_train.tar --no-check-certificate + cd ./pretrain_models/ && tar xf det_r50_dcn_fce_ctw_v2.0_train.tar && cd ../ + rm -rf ./train_data/icdar2015 + wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dataset/icdar2015_benckmark.tar --no-check-certificate + cd ./train_data/ && tar xf icdar2015_benckmark.tar + ln -s ./icdar2015_benckmark ./icdar2015 + cd ../ fi fi @@ -52,13 +120,20 @@ if [ ${MODE} = "lite_train_lite_infer" ];then wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_distill_train.tar --no-check-certificate cd ./pretrain_models/ && tar xf ch_PP-OCRv3_det_distill_train.tar && cd ../ fi - if [ ${model_name} == "en_table_structure" ];then + if [ ${model_name} == "en_table_structure" ] || [ ${model_name} == "en_table_structure_PACT" ];then wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.1/table/en_ppocr_mobile_v2.0_table_structure_train.tar --no-check-certificate cd ./pretrain_models/ && tar xf en_ppocr_mobile_v2.0_table_structure_train.tar && cd ../ wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_det_infer.tar --no-check-certificate wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_rec_infer.tar --no-check-certificate cd ./inference/ && tar xf en_ppocr_mobile_v2.0_table_det_infer.tar && tar xf en_ppocr_mobile_v2.0_table_rec_infer.tar && cd ../ fi + if [[ ${model_name} =~ "det_r50_db_plusplus" ]];then + wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/ResNet50_dcn_asf_synthtext_pretrained.pdparams --no-check-certificate + fi + if [ ${model_name} == "table_master" ];then + wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_train.tar --no-check-certificate + cd ./pretrain_models/ && tar xf table_structure_tablemaster_train.tar && cd ../ + fi cd ./pretrain_models/ && tar xf det_mv3_db_v2.0_train.tar && cd ../ rm -rf ./train_data/icdar2015 rm -rf ./train_data/ic15_data @@ -96,7 +171,7 @@ if [ ${MODE} = "lite_train_lite_infer" ];then 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 + 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 ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_icdar15_v2.0_train.tar --no-check-certificate wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/total_text_lite.tar --no-check-certificate @@ -107,19 +182,31 @@ if [ ${MODE} = "lite_train_lite_infer" ];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 + 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 + 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 - if [ ${model_name} == "det_mv3_east_v2.0" ]; then + if [ ${model_name} == "det_mv3_east_v2_0" ]; then wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_east_v2.0_train.tar --no-check-certificate cd ./pretrain_models/ && tar xf det_mv3_east_v2.0_train.tar && cd ../ fi + if [ ${model_name} == "det_r50_vd_east_v2_0" ]; then + wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_east_v2.0_train.tar --no-check-certificate + cd ./pretrain_models/ && tar xf det_r50_vd_east_v2.0_train.tar && cd ../ + fi + if [ ${model_name} == "det_r50_dcn_fce_ctw_v2_0" ]; then + wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/contribution/det_r50_dcn_fce_ctw_v2.0_train.tar --no-check-certificate + cd ./pretrain_models/ && tar xf det_r50_dcn_fce_ctw_v2.0_train.tar & cd ../ + fi + if [ ${model_name} == "rec_r32_gaspin_bilstm_att" ]; then + wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/rec_r32_gaspin_bilstm_att_train.tar --no-check-certificate + cd ./pretrain_models/ && tar xf rec_r32_gaspin_bilstm_att_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 @@ -147,7 +234,7 @@ elif [ ${MODE} = "whole_train_whole_infer" ];then cd ./pretrain_models/ && tar xf en_server_pgnetA.tar && cd ../ cd ./train_data && tar xf total_text.tar && ln -s total_text_lite total_text && cd ../ fi - if [ ${model_name} == "det_r50_vd_sast_totaltext_v2.0" ]; then + 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/dygraph_v2.0/test/total_text_lite.tar --no-check-certificate cd ./train_data && tar xf total_text.tar && ln -s total_text_lite total_text && cd ../ @@ -191,32 +278,32 @@ elif [ ${MODE} = "whole_infer" ];then wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar --no-check-certificate wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/rec_inference.tar --no-check-certificate cd ./inference && tar xf rec_inference.tar && tar xf ch_det_data_50.tar && cd ../ - if [ ${model_name} = "ch_ppocr_mobile_v2.0_det" ]; then + 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/ch/ch_ppocr_mobile_v2.0_det_train.tar --no-check-certificate wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar --no-check-certificate cd ./inference && tar xf ${eval_model_name}.tar && tar xf ch_det_data_50.tar && tar xf ch_ppocr_mobile_v2.0_det_infer.tar && cd ../ - elif [ ${model_name} = "ch_ppocr_mobile_v2.0_det_PACT" ]; then + elif [ ${model_name} = "ch_ppocr_mobile_v2_0_det_PACT" ]; then eval_model_name="ch_ppocr_mobile_v2.0_det_prune_infer" wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_prune_infer.tar --no-check-certificate cd ./inference && tar xf ${eval_model_name}.tar && tar xf ch_det_data_50.tar && cd ../ - elif [ ${model_name} = "ch_ppocr_server_v2.0_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 cd ./inference && tar xf ch_ppocr_server_v2.0_det_train.tar && tar xf ch_det_data_50.tar && cd ../ - elif [ ${model_name} = "ch_ppocr_mobile_v2.0" ]; then + 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/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} = "ch_ppocr_server_v2.0" ]; 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/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} = "ch_ppocr_mobile_v2.0_rec_PACT" ]; then + elif [ ${model_name} = "ch_ppocr_mobile_v2_0_rec_PACT" ]; then eval_model_name="ch_ppocr_mobile_v2.0_rec_slim_infer" wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_slim_infer.tar --no-check-certificate cd ./inference && tar xf ${eval_model_name}.tar && cd ../ - elif [ ${model_name} = "ch_ppocr_mobile_v2.0_rec_FPGM" ]; then + elif [ ${model_name} = "ch_ppocr_mobile_v2_0_rec_FPGM" ]; then eval_model_name="ch_PP-OCRv2_rec_infer" wget -nc -P ./inference https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar --no-check-certificate cd ./inference && tar xf ${eval_model_name}.tar && cd ../ @@ -261,39 +348,39 @@ elif [ ${MODE} = "whole_infer" ];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 && tar xf ch_det_data_50.tar && cd ../ fi - if [ ${model_name} == "det_r50_vd_sast_icdar15_v2.0" ]; then + 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 && tar xf ch_det_data_50.tar && cd ../ fi - if [ ${model_name} == "rec_mv3_none_none_ctc_v2.0" ]; then + if [ ${model_name} == "rec_mv3_none_none_ctc_v2_0" ]; then wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_none_none_ctc_v2.0_train.tar --no-check-certificate cd ./inference/ && tar xf rec_mv3_none_none_ctc_v2.0_train.tar && cd ../ fi - if [ ${model_name} == "rec_r34_vd_none_none_ctc_v2.0" ]; then + if [ ${model_name} == "rec_r34_vd_none_none_ctc_v2_0" ]; then wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_none_none_ctc_v2.0_train.tar --no-check-certificate cd ./inference/ && tar xf rec_r34_vd_none_none_ctc_v2.0_train.tar && cd ../ fi - if [ ${model_name} == "rec_mv3_none_bilstm_ctc_v2.0" ]; then + if [ ${model_name} == "rec_mv3_none_bilstm_ctc_v2_0" ]; then wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_none_bilstm_ctc_v2.0_train.tar --no-check-certificate cd ./inference/ && tar xf rec_mv3_none_bilstm_ctc_v2.0_train.tar && cd ../ fi - if [ ${model_name} == "rec_r34_vd_none_bilstm_ctc_v2.0" ]; then + if [ ${model_name} == "rec_r34_vd_none_bilstm_ctc_v2_0" ]; then wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_none_bilstm_ctc_v2.0_train.tar --no-check-certificate cd ./inference/ && tar xf rec_r34_vd_none_bilstm_ctc_v2.0_train.tar && cd ../ fi - if [ ${model_name} == "rec_mv3_tps_bilstm_ctc_v2.0" ]; then + if [ ${model_name} == "rec_mv3_tps_bilstm_ctc_v2_0" ]; then wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_tps_bilstm_ctc_v2.0_train.tar --no-check-certificate cd ./inference/ && tar xf rec_mv3_tps_bilstm_ctc_v2.0_train.tar && cd ../ fi - if [ ${model_name} == "rec_r34_vd_tps_bilstm_ctc_v2.0" ]; then + if [ ${model_name} == "rec_r34_vd_tps_bilstm_ctc_v2_0" ]; then wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_tps_bilstm_ctc_v2.0_train.tar --no-check-certificate cd ./inference/ && tar xf rec_r34_vd_tps_bilstm_ctc_v2.0_train.tar && cd ../ fi - if [ ${model_name} == "ch_ppocr_server_v2.0_rec" ]; then + if [ ${model_name} == "ch_ppocr_server_v2_0_rec" ]; then wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_train.tar --no-check-certificate cd ./inference/ && tar xf ch_ppocr_server_v2.0_rec_train.tar && cd ../ fi - if [ ${model_name} == "ch_ppocr_mobile_v2.0_rec" ]; then + if [ ${model_name} == "ch_ppocr_mobile_v2_0_rec" ]; then wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_train.tar --no-check-certificate cd ./inference/ && tar xf ch_ppocr_mobile_v2.0_rec_train.tar && cd ../ fi @@ -301,11 +388,11 @@ elif [ ${MODE} = "whole_infer" ];then wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mtb_nrtr_train.tar --no-check-certificate cd ./inference/ && tar xf rec_mtb_nrtr_train.tar && cd ../ fi - if [ ${model_name} == "rec_mv3_tps_bilstm_att_v2.0" ]; then + if [ ${model_name} == "rec_mv3_tps_bilstm_att_v2_0" ]; then wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_tps_bilstm_att_v2.0_train.tar --no-check-certificate cd ./inference/ && tar xf rec_mv3_tps_bilstm_att_v2.0_train.tar && cd ../ fi - if [ ${model_name} == "rec_r34_vd_tps_bilstm_att_v2.0" ]; then + if [ ${model_name} == "rec_r34_vd_tps_bilstm_att_v2_0" ]; then wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_tps_bilstm_att_v2.0_train.tar --no-check-certificate cd ./inference/ && tar xf rec_r34_vd_tps_bilstm_att_v2.0_train.tar && cd ../ fi @@ -318,7 +405,7 @@ elif [ ${MODE} = "whole_infer" ];then cd ./inference/ && tar xf rec_r50_vd_srn_train.tar && cd ../ fi - if [ ${model_name} == "det_r50_vd_sast_totaltext_v2.0" ]; then + if [ ${model_name} == "det_r50_vd_sast_totaltext_v2_0" ]; then wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_totaltext_v2.0_train.tar --no-check-certificate cd ./inference/ && tar xf det_r50_vd_sast_totaltext_v2.0_train.tar && cd ../ fi @@ -326,11 +413,11 @@ elif [ ${MODE} = "whole_infer" ];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 && tar xf ch_det_data_50.tar && cd ../ fi - if [ ${model_name} == "det_r50_db_v2.0" ]; then + 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 && tar xf ch_det_data_50.tar && cd ../ fi - if [ ${model_name} == "det_mv3_pse_v2.0" ]; then + if [ ${model_name} == "det_mv3_pse_v2_0" ]; then wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_mv3_pse_v2.0_train.tar --no-check-certificate cd ./inference/ && tar xf det_mv3_pse_v2.0_train.tar & cd ../ fi @@ -338,7 +425,7 @@ elif [ ${MODE} = "whole_infer" ];then wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_r50_vd_pse_v2.0_train.tar --no-check-certificate cd ./inference/ && tar xf det_r50_vd_pse_v2.0_train.tar & cd ../ fi - if [ ${model_name} == "det_mv3_east_v2.0" ]; then + if [ ${model_name} == "det_mv3_east_v2_0" ]; then wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_east_v2.0_train.tar --no-check-certificate cd ./inference/ && tar xf det_mv3_east_v2.0_train.tar & cd ../ fi @@ -346,6 +433,10 @@ elif [ ${MODE} = "whole_infer" ];then wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_east_v2.0_train.tar --no-check-certificate cd ./inference/ && tar xf det_r50_vd_east_v2.0_train.tar & cd ../ fi + if [ ${model_name} == "det_r50_dcn_fce_ctw_v2_0" ]; then + wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/contribution/det_r50_dcn_fce_ctw_v2.0_train.tar --no-check-certificate + cd ./inference/ && tar xf det_r50_dcn_fce_ctw_v2.0_train.tar & cd ../ + fi if [[ ${model_name} =~ "en_table_structure" ]];then wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar --no-check-certificate wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_det_infer.tar --no-check-certificate @@ -357,7 +448,7 @@ fi if [[ ${model_name} =~ "KL" ]]; 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 && rm -rf ./icdar2015 && ln -s ./icdar2015_lite ./icdar2015 && cd ../ - if [ ${model_name} = "ch_ppocr_mobile_v2.0_det_KL" ]; then + 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 ../ @@ -389,7 +480,7 @@ if [[ ${model_name} =~ "KL" ]]; then wget -nc -P ./inference https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar --no-check-certificate cd ./inference && tar xf ch_PP-OCRv3_det_infer.tar && tar xf ch_det_data_50.tar && cd ../ fi - if [ ${model_name} = "ch_ppocr_mobile_v2.0_rec_KL" ]; then + 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 @@ -407,35 +498,35 @@ if [[ ${model_name} =~ "KL" ]]; then fi if [ ${MODE} = "cpp_infer" ];then - if [ ${model_name} = "ch_ppocr_mobile_v2.0_det" ]; then + if [ ${model_name} = "ch_ppocr_mobile_v2_0_det" ]; then wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar --no-check-certificate wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/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} = "ch_ppocr_mobile_v2.0_det_KL" ]; then + elif [ ${model_name} = "ch_ppocr_mobile_v2_0_det_KL" ]; 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/tipc_fake_model/ch_ppocr_mobile_v2.0_det_klquant_infer.tar --no-check-certificate cd ./inference && tar xf ch_ppocr_mobile_v2.0_det_klquant_infer.tar && tar xf ch_det_data_50.tar && cd ../ - elif [ ${model_name} = "ch_ppocr_mobile_v2.0_det_PACT" ]; then + elif [ ${model_name} = "ch_ppocr_mobile_v2_0_det_PACT" ]; 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/tipc_fake_model/ch_ppocr_mobile_v2.0_det_pact_infer.tar --no-check-certificate cd ./inference && tar xf ch_ppocr_mobile_v2.0_det_pact_infer.tar && tar xf ch_det_data_50.tar && cd ../ - elif [ ${model_name} = "ch_ppocr_mobile_v2.0_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 ../ - elif [ ${model_name} = "ch_ppocr_mobile_v2.0_rec_KL" ]; then + elif [ ${model_name} = "ch_ppocr_mobile_v2_0_rec_KL" ]; 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/tipc_fake_model/ch_ppocr_mobile_v2.0_rec_klquant_infer.tar --no-check-certificate cd ./inference && tar xf ch_ppocr_mobile_v2.0_rec_klquant_infer.tar && tar xf rec_inference.tar && cd ../ - elif [ ${model_name} = "ch_ppocr_mobile_v2.0_rec_PACT" ]; then + elif [ ${model_name} = "ch_ppocr_mobile_v2_0_rec_PACT" ]; 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/tipc_fake_model/ch_ppocr_mobile_v2.0_rec_pact_infer.tar --no-check-certificate cd ./inference && tar xf ch_ppocr_mobile_v2.0_rec_pact_infer.tar && tar xf rec_inference.tar && cd ../ - elif [ ${model_name} = "ch_ppocr_server_v2.0_det" ]; then + elif [ ${model_name} = "ch_ppocr_server_v2_0_det" ]; then wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar --no-check-certificate wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar --no-check-certificate cd ./inference && tar xf ch_ppocr_server_v2.0_det_infer.tar && tar xf ch_det_data_50.tar && cd ../ - elif [ ${model_name} = "ch_ppocr_server_v2.0_rec" ]; then + elif [ ${model_name} = "ch_ppocr_server_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_server_v2.0_rec_infer.tar --no-check-certificate cd ./inference && tar xf ch_ppocr_server_v2.0_rec_infer.tar && tar xf rec_inference.tar && cd ../ @@ -487,12 +578,12 @@ if [ ${MODE} = "cpp_infer" ];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/tipc_fake_model/ch_PP-OCRv3_rec_pact_infer.tar --no-check-certificate cd ./inference && tar xf ch_PP-OCRv3_rec_pact_infer.tar && tar xf rec_inference.tar && cd ../ - elif [ ${model_name} = "ch_ppocr_mobile_v2.0" ]; 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} = "ch_ppocr_server_v2.0" ]; 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 @@ -520,7 +611,7 @@ if [ ${MODE} = "serving_infer" ];then ${python_name} -m pip install paddle_serving_client ${python_name} -m pip install paddle-serving-app # wget model - if [ ${model_name} == "ch_ppocr_mobile_v2.0_det_KL" ] || [ ${model_name} == "ch_ppocr_mobile_v2.0_rec_KL" ] ; then + if [ ${model_name} == "ch_ppocr_mobile_v2_0_det_KL" ] || [ ${model_name} == "ch_ppocr_mobile_v2.0_rec_KL" ] ; then wget -nc -P ./inference https://paddleocr.bj.bcebos.com/tipc_fake_model/ch_ppocr_mobile_v2.0_det_klquant_infer.tar --no-check-certificate wget -nc -P ./inference https://paddleocr.bj.bcebos.com/tipc_fake_model/ch_ppocr_mobile_v2.0_rec_klquant_infer.tar --no-check-certificate cd ./inference && tar xf ch_ppocr_mobile_v2.0_det_klquant_infer.tar && tar xf ch_ppocr_mobile_v2.0_rec_klquant_infer.tar && cd ../ @@ -532,7 +623,7 @@ if [ ${MODE} = "serving_infer" ];then wget -nc -P ./inference https://paddleocr.bj.bcebos.com/tipc_fake_model/ch_PP-OCRv3_det_klquant_infer.tar --no-check-certificate wget -nc -P ./inference https://paddleocr.bj.bcebos.com/tipc_fake_model/ch_PP-OCRv3_rec_klquant_infer.tar --no-check-certificate cd ./inference && tar xf ch_PP-OCRv3_det_klquant_infer.tar && tar xf ch_PP-OCRv3_rec_klquant_infer.tar && cd ../ - elif [ ${model_name} == "ch_ppocr_mobile_v2.0_det_PACT" ] || [ ${model_name} == "ch_ppocr_mobile_v2.0_rec_PACT" ] ; then + elif [ ${model_name} == "ch_ppocr_mobile_v2_0_det_PACT" ] || [ ${model_name} == "ch_ppocr_mobile_v2.0_rec_PACT" ] ; then wget -nc -P ./inference https://paddleocr.bj.bcebos.com/tipc_fake_model/ch_ppocr_mobile_v2.0_det_pact_infer.tar --no-check-certificate wget -nc -P ./inference https://paddleocr.bj.bcebos.com/tipc_fake_model/ch_ppocr_mobile_v2.0_rec_pact_infer.tar --no-check-certificate cd ./inference && tar xf ch_ppocr_mobile_v2.0_det_pact_infer.tar && tar xf ch_ppocr_mobile_v2.0_rec_pact_infer.tar && cd ../ @@ -544,11 +635,11 @@ if [ ${MODE} = "serving_infer" ];then wget -nc -P ./inference https://paddleocr.bj.bcebos.com/tipc_fake_model/ch_PP-OCRv3_det_pact_infer.tar --no-check-certificate wget -nc -P ./inference https://paddleocr.bj.bcebos.com/tipc_fake_model/ch_PP-OCRv3_rec_pact_infer.tar --no-check-certificate cd ./inference && tar xf ch_PP-OCRv3_det_pact_infer.tar && tar xf ch_PP-OCRv3_rec_pact_infer.tar && cd ../ - elif [[ ${model_name} =~ "ch_ppocr_mobile_v2.0" ]]; 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/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 && cd ../ - elif [[ ${model_name} =~ "ch_ppocr_server_v2.0" ]]; 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/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 && cd ../ @@ -573,11 +664,11 @@ if [ ${MODE} = "paddle2onnx_infer" ];then ${python_name} -m pip install paddle2onnx ${python_name} -m pip install onnxruntime # wget model - if [[ ${model_name} =~ "ch_ppocr_mobile_v2.0" ]]; then + if [[ ${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/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 && cd ../ - elif [[ ${model_name} =~ "ch_ppocr_server_v2.0" ]]; 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/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 && cd ../ diff --git a/test_tipc/readme.md b/test_tipc/readme.md index effb2f168b6cc91012bef3de120de9e98a21dbda..f9e9d89e4198c1ad5fabdf58775c6f7b6d190322 100644 --- a/test_tipc/readme.md +++ b/test_tipc/readme.md @@ -53,7 +53,9 @@ | SRN |rec_r50fpn_vd_none_srn | 识别 | 支持 | 多机多卡
混合精度 | - | - | | NRTR |rec_mtb_nrtr | 识别 | 支持 | 多机多卡
混合精度 | - | - | | SAR |rec_r31_sar | 识别 | 支持 | 多机多卡
混合精度 | - | - | +| SPIN |rec_r32_gaspin_bilstm_att | 识别 | 支持 | 多机多卡
混合精度 | - | - | | PGNet |rec_r34_vd_none_none_ctc_v2.0 | 端到端| 支持 | 多机多卡
混合精度 | - | - | +| TableMaster |table_structure_tablemaster_train | 表格识别| 支持 | 多机多卡
混合精度 | - | - | diff --git a/test_tipc/test_ptq_inference_python.sh b/test_tipc/test_ptq_inference_python.sh index c1aa3daa6c0c647d7a9f9980e7698a85ee09ca78..e2939fd5e638ad0f6b4c44422a6fec6459903d1c 100644 --- a/test_tipc/test_ptq_inference_python.sh +++ b/test_tipc/test_ptq_inference_python.sh @@ -139,7 +139,7 @@ if [ ${MODE} = "whole_infer" ]; then save_infer_dir="${infer_model}_klquant" set_export_weight=$(func_set_params "${export_weight}" "${infer_model}") set_save_infer_key=$(func_set_params "${save_infer_key}" "${save_infer_dir}") - export_log_path="${LOG_PATH}/_export_${Count}.log" + export_log_path="${LOG_PATH}/${MODE}_export_${Count}.log" export_cmd="${python} ${infer_run_exports[Count]} ${set_export_weight} ${set_save_infer_key} > ${export_log_path} 2>&1 " echo ${infer_run_exports[Count]} echo $export_cmd diff --git a/test_tipc/test_serving_infer_cpp.sh b/test_tipc/test_serving_infer_cpp.sh index 4088c66f576187215e5945793868d13fc74005af..0be6a45adf3105f088a96336dddfbe9ac612f19b 100644 --- a/test_tipc/test_serving_infer_cpp.sh +++ b/test_tipc/test_serving_infer_cpp.sh @@ -87,11 +87,12 @@ function func_serving(){ set_image_dir=$(func_set_params "${image_dir_key}" "${image_dir_value}") python_list=(${python_list}) cd ${serving_dir_value} + # cpp serving for gpu_id in ${gpu_value[*]}; do if [ ${gpu_id} = "null" ]; then server_log_path="${LOG_PATH}/cpp_server_cpu.log" - web_service_cpp_cmd="${python_list[0]} ${web_service_py} --model ${det_server_value} ${rec_server_value} ${op_key} ${op_value} ${port_key} ${port_value} > ${server_log_path} 2>&1 " + web_service_cpp_cmd="nohup ${python_list[0]} ${web_service_py} --model ${det_server_value} ${rec_server_value} ${op_key} ${op_value} ${port_key} ${port_value} > ${server_log_path} 2>&1 &" eval $web_service_cpp_cmd last_status=${PIPESTATUS[0]} status_check $last_status "${web_service_cpp_cmd}" "${status_log}" "${model_name}" @@ -105,7 +106,7 @@ function func_serving(){ ps ux | grep -i ${port_value} | awk '{print $2}' | xargs kill -s 9 else server_log_path="${LOG_PATH}/cpp_server_gpu.log" - web_service_cpp_cmd="${python_list[0]} ${web_service_py} --model ${det_server_value} ${rec_server_value} ${op_key} ${op_value} ${port_key} ${port_value} ${gpu_key} ${gpu_id} > ${server_log_path} 2>&1 " + web_service_cpp_cmd="nohup ${python_list[0]} ${web_service_py} --model ${det_server_value} ${rec_server_value} ${op_key} ${op_value} ${port_key} ${port_value} ${gpu_key} ${gpu_id} > ${server_log_path} 2>&1 &" eval $web_service_cpp_cmd sleep 5s _save_log_path="${LOG_PATH}/cpp_client_gpu.log" diff --git a/test_tipc/test_serving_infer_python.sh b/test_tipc/test_serving_infer_python.sh index 57dab6aeb5dab31605a56abd072cb1568368a66c..4ccccc06e23ce086e7dac1f3446aae9130605444 100644 --- a/test_tipc/test_serving_infer_python.sh +++ b/test_tipc/test_serving_infer_python.sh @@ -112,7 +112,7 @@ function func_serving(){ cd ${serving_dir_value} python=${python_list[0]} - + # python serving for use_gpu in ${web_use_gpu_list[*]}; do if [ ${use_gpu} = "null" ]; then @@ -123,19 +123,19 @@ function func_serving(){ if [ ${model_name} = "ch_PP-OCRv2" ] || [ ${model_name} = "ch_PP-OCRv3" ] || [ ${model_name} = "ch_ppocr_mobile_v2.0" ] || [ ${model_name} = "ch_ppocr_server_v2.0" ]; then set_det_model_config=$(func_set_params "${det_server_key}" "${det_server_value}") set_rec_model_config=$(func_set_params "${rec_server_key}" "${rec_server_value}") - web_service_cmd="${python} ${web_service_py} ${web_use_gpu_key}="" ${web_use_mkldnn_key}=${use_mkldnn} ${set_cpu_threads} ${set_det_model_config} ${set_rec_model_config} > ${server_log_path} 2>&1 " + web_service_cmd="nohup ${python} ${web_service_py} ${web_use_gpu_key}="" ${web_use_mkldnn_key}=${use_mkldnn} ${set_cpu_threads} ${set_det_model_config} ${set_rec_model_config} > ${server_log_path} 2>&1 &" eval $web_service_cmd last_status=${PIPESTATUS[0]} status_check $last_status "${web_service_cmd}" "${status_log}" "${model_name}" elif [[ ${model_name} =~ "det" ]]; then set_det_model_config=$(func_set_params "${det_server_key}" "${det_server_value}") - web_service_cmd="${python} ${web_service_py} ${web_use_gpu_key}="" ${web_use_mkldnn_key}=${use_mkldnn} ${set_cpu_threads} ${set_det_model_config} > ${server_log_path} 2>&1 " + web_service_cmd="nohup ${python} ${web_service_py} ${web_use_gpu_key}="" ${web_use_mkldnn_key}=${use_mkldnn} ${set_cpu_threads} ${set_det_model_config} > ${server_log_path} 2>&1 &" eval $web_service_cmd last_status=${PIPESTATUS[0]} status_check $last_status "${web_service_cmd}" "${status_log}" "${model_name}" elif [[ ${model_name} =~ "rec" ]]; then set_rec_model_config=$(func_set_params "${rec_server_key}" "${rec_server_value}") - web_service_cmd="${python} ${web_service_py} ${web_use_gpu_key}="" ${web_use_mkldnn_key}=${use_mkldnn} ${set_cpu_threads} ${set_rec_model_config} > ${server_log_path} 2>&1 " + web_service_cmd="nohup ${python} ${web_service_py} ${web_use_gpu_key}="" ${web_use_mkldnn_key}=${use_mkldnn} ${set_cpu_threads} ${set_rec_model_config} > ${server_log_path} 2>&1 &" eval $web_service_cmd last_status=${PIPESTATUS[0]} status_check $last_status "${web_service_cmd}" "${status_log}" "${model_name}" @@ -174,19 +174,19 @@ function func_serving(){ if [ ${model_name} = "ch_PP-OCRv2" ] || [ ${model_name} = "ch_PP-OCRv3" ] || [ ${model_name} = "ch_ppocr_mobile_v2.0" ] || [ ${model_name} = "ch_ppocr_server_v2.0" ]; then set_det_model_config=$(func_set_params "${det_server_key}" "${det_server_value}") set_rec_model_config=$(func_set_params "${rec_server_key}" "${rec_server_value}") - web_service_cmd="${python} ${web_service_py} ${set_tensorrt} ${set_precision} ${set_det_model_config} ${set_rec_model_config} > ${server_log_path} 2>&1 " + web_service_cmd="nohup ${python} ${web_service_py} ${set_tensorrt} ${set_precision} ${set_det_model_config} ${set_rec_model_config} > ${server_log_path} 2>&1 &" eval $web_service_cmd last_status=${PIPESTATUS[0]} status_check $last_status "${web_service_cmd}" "${status_log}" "${model_name}" elif [[ ${model_name} =~ "det" ]]; then set_det_model_config=$(func_set_params "${det_server_key}" "${det_server_value}") - web_service_cmd="${python} ${web_service_py} ${set_tensorrt} ${set_precision} ${set_det_model_config} > ${server_log_path} 2>&1 " + web_service_cmd="nohup ${python} ${web_service_py} ${set_tensorrt} ${set_precision} ${set_det_model_config} > ${server_log_path} 2>&1 &" eval $web_service_cmd last_status=${PIPESTATUS[0]} status_check $last_status "${web_service_cmd}" "${status_log}" "${model_name}" elif [[ ${model_name} =~ "rec" ]]; then set_rec_model_config=$(func_set_params "${rec_server_key}" "${rec_server_value}") - web_service_cmd="${python} ${web_service_py} ${set_tensorrt} ${set_precision} ${set_rec_model_config} > ${server_log_path} 2>&1 " + web_service_cmd="nohup ${python} ${web_service_py} ${set_tensorrt} ${set_precision} ${set_rec_model_config} > ${server_log_path} 2>&1 &" eval $web_service_cmd last_status=${PIPESTATUS[0]} status_check $last_status "${web_service_cmd}" "${status_log}" "${model_name}" diff --git a/test_tipc/test_train_inference_python.sh b/test_tipc/test_train_inference_python.sh index fa68cb2632ee69fe361f99093e7a2352006ed283..545cdbba2051c8123ef7f70f2aeb4b4b5a57b7c5 100644 --- a/test_tipc/test_train_inference_python.sh +++ b/test_tipc/test_train_inference_python.sh @@ -101,6 +101,7 @@ function func_inference(){ _log_path=$4 _img_dir=$5 _flag_quant=$6 + _gpu=$7 # inference for use_gpu in ${use_gpu_list[*]}; do if [ ${use_gpu} = "False" ] || [ ${use_gpu} = "cpu" ]; then @@ -119,7 +120,7 @@ function func_inference(){ fi # skip when quant model inference but precision is not int8 set_precision=$(func_set_params "${precision_key}" "${precision}") - _save_log_path="${_log_path}/python_infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_precision_${precision}_batchsize_${batch_size}.log" + _save_log_path="${_log_path}/python_infer_cpu_gpus_${_gpu}_usemkldnn_${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}") @@ -150,7 +151,7 @@ function func_inference(){ continue fi for batch_size in ${batch_size_list[*]}; do - _save_log_path="${_log_path}/python_infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_${batch_size}.log" + _save_log_path="${_log_path}/python_infer_gpu_gpus_${_gpu}_usetrt_${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}") @@ -184,6 +185,7 @@ if [ ${MODE} = "whole_infer" ]; then # set CUDA_VISIBLE_DEVICES eval $env export Count=0 + gpu=0 IFS="|" infer_run_exports=(${infer_export_list}) infer_quant_flag=(${infer_is_quant}) @@ -193,7 +195,7 @@ if [ ${MODE} = "whole_infer" ]; then save_infer_dir="${infer_model}" set_export_weight=$(func_set_params "${export_weight}" "${infer_model}") set_save_infer_key=$(func_set_params "${save_infer_key}" "${save_infer_dir}") - export_log_path="${LOG_PATH}/_export_${Count}.log" + export_log_path="${LOG_PATH}_export_${Count}.log" export_cmd="${python} ${infer_run_exports[Count]} ${set_export_weight} ${set_save_infer_key} > ${export_log_path} 2>&1 " echo ${infer_run_exports[Count]} echo $export_cmd @@ -205,7 +207,7 @@ if [ ${MODE} = "whole_infer" ]; then fi #run inference is_quant=${infer_quant_flag[Count]} - func_inference "${python}" "${inference_py}" "${save_infer_dir}" "${LOG_PATH}" "${infer_img_dir}" ${is_quant} + func_inference "${python}" "${inference_py}" "${save_infer_dir}" "${LOG_PATH}" "${infer_img_dir}" ${is_quant} "${gpu}" Count=$(($Count + 1)) done else @@ -265,7 +267,7 @@ else if [ ${run_train} = "null" ]; then continue fi - set_autocast=$(func_set_params "${autocast_key}" "${autocast}") + set_epoch=$(func_set_params "${epoch_key}" "${epoch_num}") set_pretrain=$(func_set_params "${pretrain_model_key}" "${pretrain_model_value}") set_batchsize=$(func_set_params "${train_batch_key}" "${train_batch_value}") @@ -287,14 +289,15 @@ else set_save_model=$(func_set_params "${save_model_key}" "${save_log}") if [ ${#gpu} -le 2 ];then # train with cpu or single gpu - cmd="${python} ${run_train} ${set_use_gpu} ${set_save_model} ${set_epoch} ${set_pretrain} ${set_autocast} ${set_batchsize} ${set_train_params1} ${set_amp_config} " + cmd="${python} ${run_train} ${set_use_gpu} ${set_save_model} ${set_epoch} ${set_pretrain} ${set_batchsize} ${set_train_params1} ${set_amp_config} " elif [ ${#ips} -le 15 ];then # train with multi-gpu - cmd="${python} -m paddle.distributed.launch --gpus=${gpu} ${run_train} ${set_use_gpu} ${set_save_model} ${set_epoch} ${set_pretrain} ${set_autocast} ${set_batchsize} ${set_train_params1} ${set_amp_config}" + cmd="${python} -m paddle.distributed.launch --gpus=${gpu} ${run_train} ${set_use_gpu} ${set_save_model} ${set_epoch} ${set_pretrain} ${set_batchsize} ${set_train_params1} ${set_amp_config}" else # train with multi-machine - cmd="${python} -m paddle.distributed.launch --ips=${ips} --gpus=${gpu} ${run_train} ${set_use_gpu} ${set_save_model} ${set_pretrain} ${set_epoch} ${set_autocast} ${set_batchsize} ${set_train_params1} ${set_amp_config}" + cmd="${python} -m paddle.distributed.launch --ips=${ips} --gpus=${gpu} ${run_train} ${set_use_gpu} ${set_save_model} ${set_pretrain} ${set_epoch} ${set_batchsize} ${set_train_params1} ${set_amp_config}" fi # run train eval $cmd + eval "cat ${save_log}/train.log >> ${save_log}.log" status_check $? "${cmd}" "${status_log}" "${model_name}" set_eval_pretrain=$(func_set_params "${pretrain_model_key}" "${save_log}/${train_model_name}") @@ -327,7 +330,7 @@ else else infer_model_dir=${save_infer_path} fi - func_inference "${python}" "${inference_py}" "${infer_model_dir}" "${LOG_PATH}" "${train_infer_img_dir}" "${flag_quant}" + func_inference "${python}" "${inference_py}" "${infer_model_dir}" "${LOG_PATH}" "${train_infer_img_dir}" "${flag_quant}" "${gpu}" eval "unset CUDA_VISIBLE_DEVICES" fi diff --git a/tools/export_model.py b/tools/export_model.py index b10d41d5b288258ad895cefa7d8cc243eff10546..69ac904c661fad77255c70563fdf1f16c5c29875 100755 --- a/tools/export_model.py +++ b/tools/export_model.py @@ -91,12 +91,28 @@ def export_single_model(model, ] # print([None, 3, 32, 128]) model = to_static(model, input_spec=other_shape) - elif arch_config["algorithm"] == "NRTR": + elif arch_config["algorithm"] in ["NRTR", "SPIN"]: other_shape = [ paddle.static.InputSpec( shape=[None, 1, 32, 100], dtype="float32"), ] model = to_static(model, input_spec=other_shape) + elif arch_config["algorithm"] in ["LayoutLM", "LayoutLMv2", "LayoutXLM"]: + input_spec = [ + paddle.static.InputSpec( + shape=[None, 512], dtype="int64"), # input_ids + paddle.static.InputSpec( + shape=[None, 512, 4], dtype="int64"), # bbox + paddle.static.InputSpec( + shape=[None, 512], dtype="int64"), # attention_mask + paddle.static.InputSpec( + shape=[None, 512], dtype="int64"), # token_type_ids + paddle.static.InputSpec( + shape=[None, 3, 224, 224], dtype="int64"), # image + ] + if arch_config["algorithm"] == "LayoutLM": + input_spec.pop(4) + model = to_static(model, input_spec=[input_spec]) else: infer_shape = [3, -1, -1] if arch_config["model_type"] == "rec": @@ -110,6 +126,8 @@ def export_single_model(model, infer_shape[-1] = 100 elif arch_config["model_type"] == "table": infer_shape = [3, 488, 488] + if arch_config["algorithm"] == "TableMaster": + infer_shape = [3, 480, 480] model = to_static( model, input_spec=[ @@ -172,7 +190,7 @@ def main(): config["Architecture"]["Head"]["out_channels"] = char_num model = build_model(config["Architecture"]) - load_model(config, model) + load_model(config, model, model_type=config['Architecture']["model_type"]) model.eval() save_path = config["Global"]["save_inference_dir"] diff --git a/tools/infer/predict_det.py b/tools/infer/predict_det.py index 7b6bebf1fbced2de5bb0e4e75840fb8dd7beb374..394a48948b1f284bd405532769b76eeb298668bd 100755 --- a/tools/infer/predict_det.py +++ b/tools/infer/predict_det.py @@ -67,6 +67,23 @@ class TextDetector(object): postprocess_params["unclip_ratio"] = args.det_db_unclip_ratio postprocess_params["use_dilation"] = args.use_dilation postprocess_params["score_mode"] = args.det_db_score_mode + elif self.det_algorithm == "DB++": + postprocess_params['name'] = 'DBPostProcess' + postprocess_params["thresh"] = args.det_db_thresh + postprocess_params["box_thresh"] = args.det_db_box_thresh + postprocess_params["max_candidates"] = 1000 + postprocess_params["unclip_ratio"] = args.det_db_unclip_ratio + postprocess_params["use_dilation"] = args.use_dilation + postprocess_params["score_mode"] = args.det_db_score_mode + pre_process_list[1] = { + 'NormalizeImage': { + 'std': [1.0, 1.0, 1.0], + 'mean': + [0.48109378172549, 0.45752457890196, 0.40787054090196], + 'scale': '1./255.', + 'order': 'hwc' + } + } elif self.det_algorithm == "EAST": postprocess_params['name'] = 'EASTPostProcess' postprocess_params["score_thresh"] = args.det_east_score_thresh @@ -231,7 +248,7 @@ class TextDetector(object): preds['f_score'] = outputs[1] preds['f_tco'] = outputs[2] preds['f_tvo'] = outputs[3] - elif self.det_algorithm in ['DB', 'PSE']: + elif self.det_algorithm in ['DB', 'PSE', 'DB++']: preds['maps'] = outputs[0] elif self.det_algorithm == 'FCE': for i, output in enumerate(outputs): diff --git a/tools/infer/predict_rec.py b/tools/infer/predict_rec.py index a95f55596647acc0eaca9616b5630917d7ebdf3a..fdbf429be0ef2008d05c141504fcc216987112b3 100755 --- a/tools/infer/predict_rec.py +++ b/tools/infer/predict_rec.py @@ -81,6 +81,12 @@ class TextRecognizer(object): "character_dict_path": args.rec_char_dict_path, "use_space_char": args.use_space_char } + elif self.rec_algorithm == "SPIN": + postprocess_params = { + 'name': 'SPINLabelDecode', + "character_dict_path": args.rec_char_dict_path, + "use_space_char": args.use_space_char + } self.postprocess_op = build_post_process(postprocess_params) self.predictor, self.input_tensor, self.output_tensors, self.config = \ utility.create_predictor(args, 'rec', logger) @@ -258,6 +264,22 @@ class TextRecognizer(object): return padding_im, resize_shape, pad_shape, valid_ratio + def resize_norm_img_spin(self, img): + img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) + # return padding_im + img = cv2.resize(img, tuple([100, 32]), cv2.INTER_CUBIC) + img = np.array(img, np.float32) + img = np.expand_dims(img, -1) + img = img.transpose((2, 0, 1)) + mean = [127.5] + std = [127.5] + mean = np.array(mean, dtype=np.float32) + std = np.array(std, dtype=np.float32) + mean = np.float32(mean.reshape(1, -1)) + stdinv = 1 / np.float32(std.reshape(1, -1)) + img -= mean + img *= stdinv + return img def resize_norm_img_svtr(self, img, image_shape): imgC, imgH, imgW = image_shape @@ -337,6 +359,10 @@ class TextRecognizer(object): self.rec_image_shape) norm_img = norm_img[np.newaxis, :] norm_img_batch.append(norm_img) + elif self.rec_algorithm == 'SPIN': + norm_img = self.resize_norm_img_spin(img_list[indices[ino]]) + norm_img = norm_img[np.newaxis, :] + norm_img_batch.append(norm_img) elif self.rec_algorithm == "ABINet": norm_img = self.resize_norm_img_abinet( img_list[indices[ino]], self.rec_image_shape) diff --git a/tools/infer/utility.py b/tools/infer/utility.py index 366212f228eec33f11c825bfaf1e360258af9b2e..7eb77dec74bf283936e1143edcb5b5dfc28365bd 100644 --- a/tools/infer/utility.py +++ b/tools/infer/utility.py @@ -153,6 +153,8 @@ def create_predictor(args, mode, logger): model_dir = args.rec_model_dir elif mode == 'table': model_dir = args.table_model_dir + elif mode == 'ser': + model_dir = args.ser_model_dir else: model_dir = args.e2e_model_dir @@ -316,8 +318,13 @@ def create_predictor(args, mode, logger): # create predictor predictor = inference.create_predictor(config) input_names = predictor.get_input_names() - for name in input_names: - input_tensor = predictor.get_input_handle(name) + if mode in ['ser', 're']: + input_tensor = [] + for name in input_names: + input_tensor.append(predictor.get_input_handle(name)) + else: + for name in input_names: + input_tensor = predictor.get_input_handle(name) output_tensors = get_output_tensors(args, mode, predictor) return predictor, input_tensor, output_tensors, config diff --git a/tools/infer_det.py b/tools/infer_det.py index 1acecedf3e42fe67a93644a7f06c07c8b6bea2e3..f253e8f2876a5942538f18e93dfdada4391875b2 100755 --- a/tools/infer_det.py +++ b/tools/infer_det.py @@ -44,7 +44,7 @@ def draw_det_res(dt_boxes, config, img, img_name, save_path): import cv2 src_im = img for box in dt_boxes: - box = box.astype(np.int32).reshape((-1, 1, 2)) + box = np.array(box).astype(np.int32).reshape((-1, 1, 2)) cv2.polylines(src_im, [box], True, color=(255, 255, 0), thickness=2) if not os.path.exists(save_path): os.makedirs(save_path) @@ -106,7 +106,7 @@ def main(): dt_boxes_list = [] for box in boxes: tmp_json = {"transcription": ""} - tmp_json['points'] = box.tolist() + tmp_json['points'] = np.array(box).tolist() dt_boxes_list.append(tmp_json) det_box_json[k] = dt_boxes_list save_det_path = os.path.dirname(config['Global'][ @@ -118,7 +118,7 @@ def main(): # write result for box in boxes: tmp_json = {"transcription": ""} - tmp_json['points'] = box.tolist() + tmp_json['points'] = np.array(box).tolist() dt_boxes_json.append(tmp_json) save_det_path = os.path.dirname(config['Global'][ 'save_res_path']) + "/det_results/" diff --git a/tools/infer_kie.py b/tools/infer_kie.py index 0cb0b8702cbd7ea74a7b7fcff69122731578a1bd..346e2e0aeeee695ab49577b6b13dcc058150df1a 100755 --- a/tools/infer_kie.py +++ b/tools/infer_kie.py @@ -39,13 +39,12 @@ import time def read_class_list(filepath): - dict = {} + ret = {} with open(filepath, "r") as f: lines = f.readlines() - for line in lines: - key, value = line.split(" ") - dict[key] = value.rstrip() - return dict + for idx, line in enumerate(lines): + ret[idx] = line.strip("\n") + return ret def draw_kie_result(batch, node, idx_to_cls, count): @@ -71,7 +70,7 @@ def draw_kie_result(batch, node, idx_to_cls, count): x_min = int(min([point[0] for point in new_box])) y_min = int(min([point[1] for point in new_box])) - pred_label = str(node_pred_label[i]) + pred_label = node_pred_label[i] if pred_label in idx_to_cls: pred_label = idx_to_cls[pred_label] pred_score = '{:.2f}'.format(node_pred_score[i]) @@ -109,8 +108,7 @@ def main(): save_res_path = config['Global']['save_res_path'] class_path = config['Global']['class_path'] idx_to_cls = read_class_list(class_path) - if not os.path.exists(os.path.dirname(save_res_path)): - os.makedirs(os.path.dirname(save_res_path)) + os.makedirs(os.path.dirname(save_res_path), exist_ok=True) model.eval() diff --git a/tools/infer_table.py b/tools/infer_table.py index 66c2da4421a313c634d27eb7a1013638a7c005ed..6c02dd8640c9345c267e56d6e5a0c14bde121b7e 100644 --- a/tools/infer_table.py +++ b/tools/infer_table.py @@ -36,10 +36,12 @@ from ppocr.modeling.architectures import build_model from ppocr.postprocess import build_post_process from ppocr.utils.save_load import load_model from ppocr.utils.utility import get_image_file_list +from ppocr.utils.visual import draw_rectangle import tools.program as program import cv2 +@paddle.no_grad() def main(config, device, logger, vdl_writer): global_config = config['Global'] @@ -53,53 +55,61 @@ def main(config, device, logger, vdl_writer): getattr(post_process_class, 'character')) model = build_model(config['Architecture']) + algorithm = config['Architecture']['algorithm'] + use_xywh = algorithm in ['TableMaster'] load_model(config, model) # create data ops transforms = [] - use_padding = False for op in config['Eval']['dataset']['transforms']: op_name = list(op)[0] - if 'Label' in op_name: + if 'Encode' in op_name: continue if op_name == 'KeepKeys': - op[op_name]['keep_keys'] = ['image'] - if op_name == "ResizeTableImage": - use_padding = True - padding_max_len = op['ResizeTableImage']['max_len'] + op[op_name]['keep_keys'] = ['image', 'shape'] transforms.append(op) global_config['infer_mode'] = True ops = create_operators(transforms, global_config) + save_res_path = config['Global']['save_res_path'] + os.makedirs(save_res_path, exist_ok=True) + model.eval() - for file in get_image_file_list(config['Global']['infer_img']): - logger.info("infer_img: {}".format(file)) - with open(file, 'rb') as f: - img = f.read() - data = {'image': img} - batch = transform(data, ops) - images = np.expand_dims(batch[0], axis=0) - images = paddle.to_tensor(images) - preds = model(images) - post_result = post_process_class(preds) - res_html_code = post_result['res_html_code'] - res_loc = post_result['res_loc'] - img = cv2.imread(file) - imgh, imgw = img.shape[0:2] - res_loc_final = [] - for rno in range(len(res_loc[0])): - x0, y0, x1, y1 = res_loc[0][rno] - left = max(int(imgw * x0), 0) - top = max(int(imgh * y0), 0) - right = min(int(imgw * x1), imgw - 1) - bottom = min(int(imgh * y1), imgh - 1) - cv2.rectangle(img, (left, top), (right, bottom), (0, 0, 255), 2) - res_loc_final.append([left, top, right, bottom]) - res_loc_str = json.dumps(res_loc_final) - logger.info("result: {}, {}".format(res_html_code, res_loc_final)) - logger.info("success!") + with open( + os.path.join(save_res_path, 'infer.txt'), mode='w', + encoding='utf-8') as f_w: + for file in get_image_file_list(config['Global']['infer_img']): + logger.info("infer_img: {}".format(file)) + with open(file, 'rb') as f: + img = f.read() + data = {'image': img} + batch = transform(data, ops) + images = np.expand_dims(batch[0], axis=0) + shape_list = np.expand_dims(batch[1], axis=0) + + images = paddle.to_tensor(images) + preds = model(images) + post_result = post_process_class(preds, [shape_list]) + + structure_str_list = post_result['structure_batch_list'][0] + bbox_list = post_result['bbox_batch_list'][0] + structure_str_list = structure_str_list[0] + structure_str_list = [ + '', '', '' + ] + structure_str_list + ['
', '', ''] + bbox_list_str = json.dumps(bbox_list.tolist()) + + logger.info("result: {}, {}".format(structure_str_list, + bbox_list_str)) + f_w.write("result: {}, {}\n".format(structure_str_list, + bbox_list_str)) + + img = draw_rectangle(file, bbox_list, use_xywh) + cv2.imwrite( + os.path.join(save_res_path, os.path.basename(file)), img) + logger.info("success!") if __name__ == '__main__': diff --git a/tools/infer_vqa_token_ser.py b/tools/infer_vqa_token_ser.py index 83ed72b392e627c161903c3945f57be0abfabc2b..0173a554cace31e20ab47dbe36d132a4dbb2127b 100755 --- a/tools/infer_vqa_token_ser.py +++ b/tools/infer_vqa_token_ser.py @@ -44,6 +44,7 @@ def to_tensor(data): from collections import defaultdict data_dict = defaultdict(list) to_tensor_idxs = [] + for idx, v in enumerate(data): if isinstance(v, (np.ndarray, paddle.Tensor, numbers.Number)): if idx not in to_tensor_idxs: @@ -57,6 +58,7 @@ def to_tensor(data): class SerPredictor(object): def __init__(self, config): global_config = config['Global'] + self.algorithm = config['Architecture']["algorithm"] # build post process self.post_process_class = build_post_process(config['PostProcess'], @@ -70,7 +72,10 @@ class SerPredictor(object): from paddleocr import PaddleOCR - self.ocr_engine = PaddleOCR(use_angle_cls=False, show_log=False) + self.ocr_engine = PaddleOCR( + use_angle_cls=False, + show_log=False, + use_gpu=global_config['use_gpu']) # create data ops transforms = [] @@ -80,29 +85,30 @@ class SerPredictor(object): op[op_name]['ocr_engine'] = self.ocr_engine elif op_name == 'KeepKeys': op[op_name]['keep_keys'] = [ - 'input_ids', 'labels', 'bbox', 'image', 'attention_mask', - 'token_type_ids', 'segment_offset_id', 'ocr_info', + 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', + 'image', 'labels', 'segment_offset_id', 'ocr_info', 'entities' ] transforms.append(op) - global_config['infer_mode'] = True + if config["Global"].get("infer_mode", None) is None: + global_config['infer_mode'] = True self.ops = create_operators(config['Eval']['dataset']['transforms'], global_config) self.model.eval() - def __call__(self, img_path): - with open(img_path, 'rb') as f: + def __call__(self, data): + with open(data["img_path"], 'rb') as f: img = f.read() - data = {'image': img} + data["image"] = img batch = transform(data, self.ops) batch = to_tensor(batch) preds = self.model(batch) + if self.algorithm in ['LayoutLMv2', 'LayoutXLM']: + preds = preds[0] + post_result = self.post_process_class( - preds, - attention_masks=batch[4], - segment_offset_ids=batch[6], - ocr_infos=batch[7]) + preds, segment_offset_ids=batch[6], ocr_infos=batch[7]) return post_result, batch @@ -112,20 +118,33 @@ if __name__ == '__main__': ser_engine = SerPredictor(config) - infer_imgs = get_image_file_list(config['Global']['infer_img']) + if config["Global"].get("infer_mode", None) is False: + data_dir = config['Eval']['dataset']['data_dir'] + with open(config['Global']['infer_img'], "rb") as f: + infer_imgs = f.readlines() + else: + infer_imgs = get_image_file_list(config['Global']['infer_img']) + with open( os.path.join(config['Global']['save_res_path'], "infer_results.txt"), "w", encoding='utf-8') as fout: - for idx, img_path in enumerate(infer_imgs): + for idx, info in enumerate(infer_imgs): + if config["Global"].get("infer_mode", None) is False: + data_line = info.decode('utf-8') + substr = data_line.strip("\n").split("\t") + img_path = os.path.join(data_dir, substr[0]) + data = {'img_path': img_path, 'label': substr[1]} + else: + img_path = info + data = {'img_path': img_path} + save_img_path = os.path.join( config['Global']['save_res_path'], os.path.splitext(os.path.basename(img_path))[0] + "_ser.jpg") - logger.info("process: [{}/{}], save result to {}".format( - idx, len(infer_imgs), save_img_path)) - result, _ = ser_engine(img_path) + result, _ = ser_engine(data) result = result[0] fout.write(img_path + "\t" + json.dumps( { @@ -133,3 +152,6 @@ if __name__ == '__main__': }, ensure_ascii=False) + "\n") img_res = draw_ser_results(img_path, result) cv2.imwrite(save_img_path, img_res) + + logger.info("process: [{}/{}], save result to {}".format( + idx, len(infer_imgs), save_img_path)) diff --git a/tools/infer_vqa_token_ser_re.py b/tools/infer_vqa_token_ser_re.py index 6210f7f3c24227c9d366b08ce93ccfe4df849ce1..20ab1fe176c3be75f7a7b01a8d77df6419c58c75 100755 --- a/tools/infer_vqa_token_ser_re.py +++ b/tools/infer_vqa_token_ser_re.py @@ -38,7 +38,7 @@ from ppocr.utils.save_load import load_model from ppocr.utils.visual import draw_re_results from ppocr.utils.logging import get_logger from ppocr.utils.utility import get_image_file_list, load_vqa_bio_label_maps, print_dict -from tools.program import ArgsParser, load_config, merge_config, check_gpu +from tools.program import ArgsParser, load_config, merge_config from tools.infer_vqa_token_ser import SerPredictor @@ -107,7 +107,7 @@ def make_input(ser_inputs, ser_results): # remove ocr_info segment_offset_id and label in ser input ser_inputs.pop(7) ser_inputs.pop(6) - ser_inputs.pop(1) + ser_inputs.pop(5) return ser_inputs, entity_idx_dict_batch @@ -131,9 +131,7 @@ class SerRePredictor(object): self.model.eval() def __call__(self, img_path): - ser_results, ser_inputs = self.ser_engine(img_path) - paddle.save(ser_inputs, 'ser_inputs.npy') - paddle.save(ser_results, 'ser_results.npy') + ser_results, ser_inputs = self.ser_engine({'img_path': img_path}) re_input, entity_idx_dict_batch = make_input(ser_inputs, ser_results) preds = self.model(re_input) post_result = self.post_process_class( @@ -155,7 +153,6 @@ def preprocess(): # check if set use_gpu=True in paddlepaddle cpu version use_gpu = config['Global']['use_gpu'] - check_gpu(use_gpu) device = 'gpu:{}'.format(dist.ParallelEnv().dev_id) if use_gpu else 'cpu' device = paddle.set_device(device) @@ -185,9 +182,7 @@ if __name__ == '__main__': for idx, img_path in enumerate(infer_imgs): save_img_path = os.path.join( config['Global']['save_res_path'], - os.path.splitext(os.path.basename(img_path))[0] + "_ser.jpg") - logger.info("process: [{}/{}], save result to {}".format( - idx, len(infer_imgs), save_img_path)) + os.path.splitext(os.path.basename(img_path))[0] + "_ser_re.jpg") result = ser_re_engine(img_path) result = result[0] @@ -197,3 +192,6 @@ if __name__ == '__main__': }, ensure_ascii=False) + "\n") img_res = draw_re_results(img_path, result) cv2.imwrite(save_img_path, img_res) + + logger.info("process: [{}/{}], save result to {}".format( + idx, len(infer_imgs), save_img_path)) diff --git a/tools/program.py b/tools/program.py index aa3ba82c44d6afba725a8059dc7f8cae41947b3d..0fa0e609bd14d07cc593786b3a3f760cb9b98500 100755 --- a/tools/program.py +++ b/tools/program.py @@ -154,6 +154,24 @@ def check_xpu(use_xpu): except Exception as e: pass +def to_float32(preds): + if isinstance(preds, dict): + for k in preds: + if isinstance(preds[k], dict) or isinstance(preds[k], list): + preds[k] = to_float32(preds[k]) + else: + preds[k] = preds[k].astype(paddle.float32) + elif isinstance(preds, list): + for k in range(len(preds)): + if isinstance(preds[k], dict): + preds[k] = to_float32(preds[k]) + elif isinstance(preds[k], list): + preds[k] = to_float32(preds[k]) + else: + preds[k] = preds[k].astype(paddle.float32) + else: + preds = preds.astype(paddle.float32) + return preds def train(config, train_dataloader, @@ -207,7 +225,7 @@ def train(config, model.train() use_srn = config['Architecture']['algorithm'] == "SRN" - extra_input_models = ["SRN", "NRTR", "SAR", "SEED", "SVTR"] + extra_input_models = ["SRN", "NRTR", "SAR", "SEED", "SVTR", "SPIN"] extra_input = False if config['Architecture']['algorithm'] == 'Distillation': for key in config['Architecture']["Models"]: @@ -252,13 +270,19 @@ def train(config, # use amp if scaler: - with paddle.amp.auto_cast(): + with paddle.amp.auto_cast(level='O2'): if model_type == 'table' or extra_input: preds = model(images, data=batch[1:]) elif model_type in ["kie", 'vqa']: preds = model(batch) else: preds = model(images) + preds = to_float32(preds) + loss = loss_class(preds, batch) + avg_loss = loss['loss'] + scaled_avg_loss = scaler.scale(avg_loss) + scaled_avg_loss.backward() + scaler.minimize(optimizer, scaled_avg_loss) else: if model_type == 'table' or extra_input: preds = model(images, data=batch[1:]) @@ -266,23 +290,19 @@ def train(config, preds = model(batch) else: preds = model(images) - - loss = loss_class(preds, batch) - avg_loss = loss['loss'] - - if scaler: - scaled_avg_loss = scaler.scale(avg_loss) - scaled_avg_loss.backward() - scaler.minimize(optimizer, scaled_avg_loss) - else: + loss = loss_class(preds, batch) + avg_loss = loss['loss'] avg_loss.backward() optimizer.step() optimizer.clear_grad() if cal_metric_during_train and epoch % calc_epoch_interval == 0: # only rec and cls need batch = [item.numpy() for item in batch] - if model_type in ['table', 'kie']: + if model_type in ['kie']: eval_class(preds, batch) + elif model_type in ['table']: + post_result = post_process_class(preds, batch) + eval_class(post_result, batch) else: if config['Loss']['name'] in ['MultiLoss', 'MultiLoss_v2' ]: # for multi head loss @@ -463,7 +483,6 @@ def eval(model, preds = model(batch) else: preds = model(images) - batch_numpy = [] for item in batch: if isinstance(item, paddle.Tensor): @@ -473,9 +492,9 @@ def eval(model, # Obtain usable results from post-processing methods total_time += time.time() - start # Evaluate the results of the current batch - if model_type in ['table', 'kie']: + if model_type in ['kie']: eval_class(preds, batch_numpy) - elif model_type in ['vqa']: + elif model_type in ['table', 'vqa']: post_result = post_process_class(preds, batch_numpy) eval_class(post_result, batch_numpy) else: @@ -576,8 +595,8 @@ def preprocess(is_train=False): assert alg in [ 'EAST', 'DB', 'SAST', 'Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN', 'CLS', 'PGNet', 'Distillation', 'NRTR', 'TableAttn', 'SAR', 'PSE', - 'SEED', 'SDMGR', 'LayoutXLM', 'LayoutLM', 'PREN', 'FCE', 'SVTR', - 'ViTSTR', 'ABINet' + 'SEED', 'SDMGR', 'LayoutXLM', 'LayoutLM', 'LayoutLMv2', 'PREN', 'FCE', + 'SVTR', 'ViTSTR', 'ABINet', 'DB++', 'TableMaster', 'SPIN' ] if use_xpu: diff --git a/tools/train.py b/tools/train.py index b7c25e34231fb650fd2c7c89dc17320f561962f9..dc8cae8a63744bb9bd486d9899680dbde9da1697 100755 --- a/tools/train.py +++ b/tools/train.py @@ -119,9 +119,6 @@ def main(config, device, logger, vdl_writer): config['Loss']['ignore_index'] = char_num - 1 model = build_model(config['Architecture']) - if config['Global']['distributed']: - model = paddle.DataParallel(model) - model = apply_to_static(model, config, logger) # build loss @@ -157,9 +154,13 @@ def main(config, device, logger, vdl_writer): scaler = paddle.amp.GradScaler( init_loss_scaling=scale_loss, use_dynamic_loss_scaling=use_dynamic_loss_scaling) + model, optimizer = paddle.amp.decorate( + models=model, optimizers=optimizer, level='O2', master_weight=True) else: scaler = None + if config['Global']['distributed']: + model = paddle.DataParallel(model) # start train program.train(config, train_dataloader, valid_dataloader, device, model, loss_class, optimizer, lr_scheduler, post_process_class,