Merge remote-tracking branch 'origin/dygraph' into dygraph

PPOCRLabel/README.md

@@ -8,6 +8,8 @@ PPOCRLabel is a semi-automatic graphic annotation tool suitable for OCR field, w
 
 ### Recent Update
 
+- 2022.02：（by [PeterH0323](https://github.com/peterh0323) ）
+  - Added KIE mode, for [detection + identification + keyword extraction] labeling.
 - 2022.01：（by [PeterH0323](https://github.com/peterh0323) ）
   - Improve user experience: prompt for the number of files and labels, optimize interaction, and fix bugs such as only use CPU when inference
 - 2021.11.17:
@@ -72,7 +74,8 @@ PPOCRLabel
 ```bash
 pip3 install PPOCRLabel
 pip3 install opencv-contrib-python-headless==4.2.0.32
-PPOCRLabel # run
+PPOCRLabel  # [Normal mode] for [detection + recognition] labeling
+PPOCRLabel --kie True # [KIE mode] for [detection + recognition + keyword extraction] labeling
 ```
 
 #### 1.2.2 Build and Install the Whl Package Locally
@@ -87,7 +90,8 @@ pip3 install dist/PPOCRLabel-1.0.2-py2.py3-none-any.whl
 
 ```bash
 cd ./PPOCRLabel  # Switch to the PPOCRLabel directory
-python PPOCRLabel.py
+python PPOCRLabel.py  # [Normal mode] for [detection + recognition] labeling
+python PPOCRLabel.py --kie True # [KIE mode] for [detection + recognition + keyword extraction] labeling
 ```
 
 
@@ -198,21 +202,31 @@ For some data that are difficult to recognize, the recognition results will not
 
 - Enter the following command in the terminal to execute the dataset division script:
 
-  ```
+    ```
   cd ./PPOCRLabel # Change the directory to the PPOCRLabel folder
-  python gen_ocr_train_val_test.py --trainValTestRatio 6:2:2 --labelRootPath ../train_data/label --detRootPath ../train_data/det --recRootPath ../train_data/rec
+  python gen_ocr_train_val_test.py --trainValTestRatio 6:2:2 --datasetRootPath ../train_data 
   ```
 
   Parameter Description:
 
   - `trainValTestRatio` is the division ratio of the number of images in the training set, validation set, and test set, set according to your actual situation, the default is `6:2:2`
 
-  - `labelRootPath` is the storage path of the dataset labeled by PPOCRLabel, the default is `../train_data/label`
-
-  - `detRootPath` is the path where the text detection dataset is divided according to the dataset marked by PPOCRLabel. The default is `../train_data/det`
-
-  - `recRootPath` is the path where the character recognition dataset is divided according to the dataset marked by PPOCRLabel. The default is `../train_data/rec`
-
+  - `datasetRootPath` is the storage path of the complete dataset labeled by PPOCRLabel. The default path is `PaddleOCR/train_data` .
+  ```
+  |-train_data
+    |-crop_img
+      |- word_001_crop_0.png
+      |- word_002_crop_0.jpg
+      |- word_003_crop_0.jpg
+      | ...
+    | Label.txt
+    | rec_gt.txt
+    |- word_001.png
+    |- word_002.jpg
+    |- word_003.jpg
+    | ...
+  ```
+  
 ### 3.6 Error message
 
 - If paddleocr is installed with whl, it has a higher priority than calling PaddleOCR class with paddleocr.py, which may cause an exception if whl package is not updated.

PPOCRLabel/README_ch.md

@@ -8,6 +8,8 @@ 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
 
+- 2022.02：（by [PeterH0323](https://github.com/peterh0323) ）
+  - 新增：KIE 功能，用于打【检测+识别+关键字提取】的标签
 - 2022.01：（by [PeterH0323](https://github.com/peterh0323) ）
   - 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
 - 2021.11.17：
@@ -70,7 +72,8 @@ PPOCRLabel --lang ch
 ```bash
 pip3 install PPOCRLabel
 pip3 install opencv-contrib-python-headless==4.2.0.32 # 如果下载过慢请添加"-i https://mirror.baidu.com/pypi/simple"
-PPOCRLabel --lang ch # 启动
+PPOCRLabel --lang ch  # 启动【普通模式】，用于打【检测+识别】场景的标签
+PPOCRLabel --lang ch --kie True  # 启动 【KIE 模式】，用于打【检测+识别+关键字提取】场景的标签
 ```
 
 > 如果上述安装出现问题，可以参考3.6节 错误提示
@@ -89,7 +92,8 @@ pip3 install dist/PPOCRLabel-1.0.2-py2.py3-none-any.whl -i https://mirror.baidu.
 
 ```bash
 cd ./PPOCRLabel  # 切换到PPOCRLabel目录
-python PPOCRLabel.py --lang ch
+python PPOCRLabel.py --lang ch  # 启动【普通模式】，用于打【检测+识别】场景的标签
+python PPOCRLabel.py --lang ch --kie True  # 启动 【KIE 模式】，用于打【检测+识别+关键字提取】场景的标签
 ```
 
 
@@ -185,19 +189,29 @@ PPOCRLabel支持三种导出方式：
 
 ```
 cd ./PPOCRLabel # 将目录切换到PPOCRLabel文件夹下
-python gen_ocr_train_val_test.py --trainValTestRatio 6:2:2 --labelRootPath ../train_data/label --detRootPath ../train_data/det --recRootPath ../train_data/rec
+python gen_ocr_train_val_test.py --trainValTestRatio 6:2:2 --datasetRootPath ../train_data 
 ```
 
 参数说明：
 
 - `trainValTestRatio` 是训练集、验证集、测试集的图像数量划分比例，根据实际情况设定，默认是`6:2:2`
 
-- `labelRootPath` 是PPOCRLabel标注的数据集存放路径，默认是`../train_data/label`
-
-- `detRootPath` 是根据PPOCRLabel标注的数据集划分后的文本检测数据集存放的路径，默认是`../train_data/det `
-
-- `recRootPath` 是根据PPOCRLabel标注的数据集划分后的字符识别数据集存放的路径，默认是`../train_data/rec`
-
+- `datasetRootPath` 是PPOCRLabel标注的完整数据集存放路径。默认路径是 `PaddleOCR/train_data` 分割数据集前应有如下结构：
+  ```
+  |-train_data
+    |-crop_img
+      |- word_001_crop_0.png
+      |- word_002_crop_0.jpg
+      |- word_003_crop_0.jpg
+      | ...
+    | Label.txt
+    | rec_gt.txt
+    |- word_001.png
+    |- word_002.jpg
+    |- word_003.jpg
+    | ...
+  ```
+  
 ### 3.6 错误提示
 
 - 如果同时使用whl包安装了paddleocr，其优先级大于通过paddleocr.py调用PaddleOCR类，whl包未更新时会导致程序异常。

PPOCRLabel/gen_ocr_train_val_test.py

@@ -17,15 +17,14 @@ def isCreateOrDeleteFolder(path, flag):
     return flagAbsPath
 
 
-def splitTrainVal(root, dir, absTrainRootPath, absValRootPath, absTestRootPath, trainTxt, valTxt, testTxt, flag):
+def splitTrainVal(root, absTrainRootPath, absValRootPath, absTestRootPath, trainTxt, valTxt, testTxt, flag):
     # 按照指定的比例划分训练集、验证集、测试集
-    labelPath = os.path.join(root, dir)
-    labelAbsPath = os.path.abspath(labelPath)
+    dataAbsPath = os.path.abspath(root)
 
     if flag == "det":
-        labelFilePath = os.path.join(labelAbsPath, args.detLabelFileName)
+        labelFilePath = os.path.join(dataAbsPath, args.detLabelFileName)
     elif flag == "rec":
-        labelFilePath = os.path.join(labelAbsPath, args.recLabelFileName)
+        labelFilePath = os.path.join(dataAbsPath, args.recLabelFileName)
 
     labelFileRead = open(labelFilePath, "r", encoding="UTF-8")
     labelFileContent = labelFileRead.readlines()
@@ -38,9 +37,9 @@ def splitTrainVal(root, dir, absTrainRootPath, absValRootPath, absTestRootPath,
         imageName = os.path.basename(imageRelativePath)
 
         if flag == "det":
-            imagePath = os.path.join(labelAbsPath, imageName)
+            imagePath = os.path.join(dataAbsPath, imageName)
         elif flag == "rec":
-            imagePath = os.path.join(labelAbsPath, "{}\\{}".format(args.recImageDirName, imageName))
+            imagePath = os.path.join(dataAbsPath, "{}\\{}".format(args.recImageDirName, imageName))
 
         # 按预设的比例划分训练集、验证集、测试集
         trainValTestRatio = args.trainValTestRatio.split(":")
@@ -90,15 +89,20 @@ def genDetRecTrainVal(args):
     recValTxt = open(os.path.join(args.recRootPath, "val.txt"), "a", encoding="UTF-8")
     recTestTxt = open(os.path.join(args.recRootPath, "test.txt"), "a", encoding="UTF-8")
 
-    for root, dirs, files in os.walk(args.labelRootPath):
+    splitTrainVal(args.datasetRootPath, detAbsTrainRootPath, detAbsValRootPath, detAbsTestRootPath, detTrainTxt, detValTxt,
+                  detTestTxt, "det")
+
+    for root, dirs, files in os.walk(args.datasetRootPath):
         for dir in dirs:
-            splitTrainVal(root, dir, detAbsTrainRootPath, detAbsValRootPath, detAbsTestRootPath, detTrainTxt, detValTxt,
-                          detTestTxt, "det")
-            splitTrainVal(root, dir, recAbsTrainRootPath, recAbsValRootPath, recAbsTestRootPath, recTrainTxt, recValTxt,
-                          recTestTxt, "rec")
+            if dir == 'crop_img':
+                splitTrainVal(root, recAbsTrainRootPath, recAbsValRootPath, recAbsTestRootPath, recTrainTxt, recValTxt,
+                              recTestTxt, "rec")
+            else:
+                continue
         break
 
 
+
 if __name__ == "__main__":
     # 功能描述：分别划分检测和识别的训练集、验证集、测试集
     # 说明：可以根据自己的路径和需求调整参数，图像数据往往多人合作分批标注，每一批图像数据放在一个文件夹内用PPOCRLabel进行标注，
@@ -110,9 +114,9 @@ if __name__ == "__main__":
         default="6:2:2",
         help="ratio of trainset:valset:testset")
     parser.add_argument(
-        "--labelRootPath",
+        "--datasetRootPath",
         type=str,
-        default="../train_data/label",
+        default="../train_data/",
         help="path to the dataset marked by ppocrlabel, E.g, dataset folder named 1,2,3..."
     )
     parser.add_argument(

PPOCRLabel/libs/canvas.py

@@ -783,7 +783,7 @@ class Canvas(QWidget):
         points = [p1+p2 for p1, p2 in zip(self.selectedShape.points, [step]*4)]
         return True in map(self.outOfPixmap, points)
 
-    def setLastLabel(self, text, line_color  = None, fill_color = None):
+    def setLastLabel(self, text, line_color=None, fill_color=None, key_cls=None):
         assert text
         self.shapes[-1].label = text
         if line_color:
@@ -791,6 +791,10 @@ class Canvas(QWidget):
 
         if fill_color:
             self.shapes[-1].fill_color = fill_color
+
+        if key_cls:
+            self.shapes[-1].key_cls = key_cls
+
         self.storeShapes()
 
         return self.shapes[-1]

PPOCRLabel/libs/keyDialog.py

+import re
+
+from PyQt5 import QtCore
+from PyQt5 import QtGui
+from PyQt5 import QtWidgets
+from PyQt5.Qt import QT_VERSION_STR
+from libs.utils import newIcon, labelValidator
+
+QT5 = QT_VERSION_STR[0] == '5'
+
+
+# TODO(unknown):
+# - Calculate optimal position so as not to go out of screen area.
+
+
+class KeyQLineEdit(QtWidgets.QLineEdit):
+    def setListWidget(self, list_widget):
+        self.list_widget = list_widget
+
+    def keyPressEvent(self, e):
+        if e.key() in [QtCore.Qt.Key_Up, QtCore.Qt.Key_Down]:
+            self.list_widget.keyPressEvent(e)
+        else:
+            super(KeyQLineEdit, self).keyPressEvent(e)
+
+
+class KeyDialog(QtWidgets.QDialog):
+    def __init__(
+            self,
+            text="Enter object label",
+            parent=None,
+            labels=None,
+            sort_labels=True,
+            show_text_field=True,
+            completion="startswith",
+            fit_to_content=None,
+            flags=None,
+    ):
+        if fit_to_content is None:
+            fit_to_content = {"row": False, "column": True}
+        self._fit_to_content = fit_to_content
+
+        super(KeyDialog, self).__init__(parent)
+        self.edit = KeyQLineEdit()
+        self.edit.setPlaceholderText(text)
+        self.edit.setValidator(labelValidator())
+        self.edit.editingFinished.connect(self.postProcess)
+        if flags:
+            self.edit.textChanged.connect(self.updateFlags)
+
+        layout = QtWidgets.QVBoxLayout()
+        if show_text_field:
+            layout_edit = QtWidgets.QHBoxLayout()
+            layout_edit.addWidget(self.edit, 6)
+            layout.addLayout(layout_edit)
+        # buttons
+        self.buttonBox = bb = QtWidgets.QDialogButtonBox(
+            QtWidgets.QDialogButtonBox.Ok | QtWidgets.QDialogButtonBox.Cancel,
+            QtCore.Qt.Horizontal,
+            self,
+        )
+        bb.button(bb.Ok).setIcon(newIcon("done"))
+        bb.button(bb.Cancel).setIcon(newIcon("undo"))
+        bb.accepted.connect(self.validate)
+        bb.rejected.connect(self.reject)
+        layout.addWidget(bb)
+        # label_list
+        self.labelList = QtWidgets.QListWidget()
+        if self._fit_to_content["row"]:
+            self.labelList.setHorizontalScrollBarPolicy(
+                QtCore.Qt.ScrollBarAlwaysOff
+            )
+        if self._fit_to_content["column"]:
+            self.labelList.setVerticalScrollBarPolicy(
+                QtCore.Qt.ScrollBarAlwaysOff
+            )
+        self._sort_labels = sort_labels
+        if labels:
+            self.labelList.addItems(labels)
+        if self._sort_labels:
+            self.labelList.sortItems()
+        else:
+            self.labelList.setDragDropMode(
+                QtWidgets.QAbstractItemView.InternalMove
+            )
+        self.labelList.currentItemChanged.connect(self.labelSelected)
+        self.labelList.itemDoubleClicked.connect(self.labelDoubleClicked)
+        self.edit.setListWidget(self.labelList)
+        layout.addWidget(self.labelList)
+        # label_flags
+        if flags is None:
+            flags = {}
+        self._flags = flags
+        self.flagsLayout = QtWidgets.QVBoxLayout()
+        self.resetFlags()
+        layout.addItem(self.flagsLayout)
+        self.edit.textChanged.connect(self.updateFlags)
+        self.setLayout(layout)
+        # completion
+        completer = QtWidgets.QCompleter()
+        if not QT5 and completion != "startswith":
+            completion = "startswith"
+        if completion == "startswith":
+            completer.setCompletionMode(QtWidgets.QCompleter.InlineCompletion)
+            # Default settings.
+            # completer.setFilterMode(QtCore.Qt.MatchStartsWith)
+        elif completion == "contains":
+            completer.setCompletionMode(QtWidgets.QCompleter.PopupCompletion)
+            completer.setFilterMode(QtCore.Qt.MatchContains)
+        else:
+            raise ValueError("Unsupported completion: {}".format(completion))
+        completer.setModel(self.labelList.model())
+        self.edit.setCompleter(completer)
+
+    def addLabelHistory(self, label):
+        if self.labelList.findItems(label, QtCore.Qt.MatchExactly):
+            return
+        self.labelList.addItem(label)
+        if self._sort_labels:
+            self.labelList.sortItems()
+
+    def labelSelected(self, item):
+        self.edit.setText(item.text())
+
+    def validate(self):
+        text = self.edit.text()
+        if hasattr(text, "strip"):
+            text = text.strip()
+        else:
+            text = text.trimmed()
+        if text:
+            self.accept()
+
+    def labelDoubleClicked(self, item):
+        self.validate()
+
+    def postProcess(self):
+        text = self.edit.text()
+        if hasattr(text, "strip"):
+            text = text.strip()
+        else:
+            text = text.trimmed()
+        self.edit.setText(text)
+
+    def updateFlags(self, label_new):
+        # keep state of shared flags
+        flags_old = self.getFlags()
+
+        flags_new = {}
+        for pattern, keys in self._flags.items():
+            if re.match(pattern, label_new):
+                for key in keys:
+                    flags_new[key] = flags_old.get(key, False)
+        self.setFlags(flags_new)
+
+    def deleteFlags(self):
+        for i in reversed(range(self.flagsLayout.count())):
+            item = self.flagsLayout.itemAt(i).widget()
+            self.flagsLayout.removeWidget(item)
+            item.setParent(None)
+
+    def resetFlags(self, label=""):
+        flags = {}
+        for pattern, keys in self._flags.items():
+            if re.match(pattern, label):
+                for key in keys:
+                    flags[key] = False
+        self.setFlags(flags)
+
+    def setFlags(self, flags):
+        self.deleteFlags()
+        for key in flags:
+            item = QtWidgets.QCheckBox(key, self)
+            item.setChecked(flags[key])
+            self.flagsLayout.addWidget(item)
+            item.show()
+
+    def getFlags(self):
+        flags = {}
+        for i in range(self.flagsLayout.count()):
+            item = self.flagsLayout.itemAt(i).widget()
+            flags[item.text()] = item.isChecked()
+        return flags
+
+    def popUp(self, text=None, move=True, flags=None):
+        if self._fit_to_content["row"]:
+            self.labelList.setMinimumHeight(
+                self.labelList.sizeHintForRow(0) * self.labelList.count() + 2
+            )
+        if self._fit_to_content["column"]:
+            self.labelList.setMinimumWidth(
+                self.labelList.sizeHintForColumn(0) + 2
+            )
+        # if text is None, the previous label in self.edit is kept
+        if text is None:
+            text = self.edit.text()
+        if flags:
+            self.setFlags(flags)
+        else:
+            self.resetFlags(text)
+        self.edit.setText(text)
+        self.edit.setSelection(0, len(text))
+
+        items = self.labelList.findItems(text, QtCore.Qt.MatchFixedString)
+        if items:
+            if len(items) != 1:
+                self.labelList.setCurrentItem(items[0])
+            row = self.labelList.row(items[0])
+            self.edit.completer().setCurrentRow(row)
+        self.edit.setFocus(QtCore.Qt.PopupFocusReason)
+        if move:
+            self.move(QtGui.QCursor.pos())
+        if self.exec_():
+            return self.edit.text(), self.getFlags()
+        else:
+            return None, None

PPOCRLabel/libs/labelColor.py

+import PIL.Image
+import numpy as np
+
+
+def rgb2hsv(rgb):
+    # type: (np.ndarray) -> np.ndarray
+    """Convert rgb to hsv.
+
+    Parameters
+    ----------
+    rgb: numpy.ndarray, (H, W, 3), np.uint8
+        Input rgb image.
+
+    Returns
+    -------
+    hsv: numpy.ndarray, (H, W, 3), np.uint8
+        Output hsv image.
+
+    """
+    hsv = PIL.Image.fromarray(rgb, mode="RGB")
+    hsv = hsv.convert("HSV")
+    hsv = np.array(hsv)
+    return hsv
+
+
+def hsv2rgb(hsv):
+    # type: (np.ndarray) -> np.ndarray
+    """Convert hsv to rgb.
+
+    Parameters
+    ----------
+    hsv: numpy.ndarray, (H, W, 3), np.uint8
+        Input hsv image.
+
+    Returns
+    -------
+    rgb: numpy.ndarray, (H, W, 3), np.uint8
+        Output rgb image.
+
+    """
+    rgb = PIL.Image.fromarray(hsv, mode="HSV")
+    rgb = rgb.convert("RGB")
+    rgb = np.array(rgb)
+    return rgb
+
+
+def label_colormap(n_label=256, value=None):
+    """Label colormap.
+
+    Parameters
+    ----------
+    n_label: int
+        Number of labels (default: 256).
+    value: float or int
+        Value scale or value of label color in HSV space.
+
+    Returns
+    -------
+    cmap: numpy.ndarray, (N, 3), numpy.uint8
+        Label id to colormap.
+
+    """
+
+    def bitget(byteval, idx):
+        return (byteval & (1 << idx)) != 0
+
+    cmap = np.zeros((n_label, 3), dtype=np.uint8)
+    for i in range(0, n_label):
+        id = i
+        r, g, b = 0, 0, 0
+        for j in range(0, 8):
+            r = np.bitwise_or(r, (bitget(id, 0) << 7 - j))
+            g = np.bitwise_or(g, (bitget(id, 1) << 7 - j))
+            b = np.bitwise_or(b, (bitget(id, 2) << 7 - j))
+            id = id >> 3
+        cmap[i, 0] = r
+        cmap[i, 1] = g
+        cmap[i, 2] = b
+
+    if value is not None:
+        hsv = rgb2hsv(cmap.reshape(1, -1, 3))
+        if isinstance(value, float):
+            hsv[:, 1:, 2] = hsv[:, 1:, 2].astype(float) * value
+        else:
+            assert isinstance(value, int)
+            hsv[:, 1:, 2] = value
+        cmap = hsv2rgb(hsv).reshape(-1, 3)
+    return cmap

PPOCRLabel/libs/shape.py

@@ -46,12 +46,13 @@ class Shape(object):
     point_size = 8
     scale = 1.0
 
-    def __init__(self, label=None, line_color=None, difficult=False, paintLabel=False):
+    def __init__(self, label=None, line_color=None, difficult=False, key_cls="None", paintLabel=False):
         self.label = label
         self.points = []
         self.fill = False
         self.selected = False
         self.difficult = difficult
+        self.key_cls = key_cls
         self.paintLabel = paintLabel
         self.locked = False
         self.direction = 0
@@ -224,6 +225,7 @@ class Shape(object):
         if self.fill_color != Shape.fill_color:
             shape.fill_color = self.fill_color
         shape.difficult = self.difficult
+        shape.key_cls = self.key_cls
         return shape
 
     def __len__(self):

PPOCRLabel/libs/unique_label_qlist_widget.py

+# -*- encoding: utf-8 -*-
+
+from PyQt5.QtCore import Qt
+from PyQt5 import QtWidgets
+
+
+class EscapableQListWidget(QtWidgets.QListWidget):
+    def keyPressEvent(self, event):
+        super(EscapableQListWidget, self).keyPressEvent(event)
+        if event.key() == Qt.Key_Escape:
+            self.clearSelection()
+
+
+class UniqueLabelQListWidget(EscapableQListWidget):
+    def mousePressEvent(self, event):
+        super(UniqueLabelQListWidget, self).mousePressEvent(event)
+        if not self.indexAt(event.pos()).isValid():
+            self.clearSelection()
+
+    def findItemsByLabel(self, label, get_row=False):
+        items = []
+        for row in range(self.count()):
+            item = self.item(row)
+            if item.data(Qt.UserRole) == label:
+                items.append(item)
+                if get_row:
+                    return row
+        return items
+
+    def createItemFromLabel(self, label):
+        item = QtWidgets.QListWidgetItem()
+        item.setData(Qt.UserRole, label)
+        return item
+
+    def setItemLabel(self, item, label, color=None):
+        qlabel = QtWidgets.QLabel()
+        if color is None:
+            qlabel.setText(f"{label}")
+        else:
+            qlabel.setText('<font color="#{:02x}{:02x}{:02x}">●</font> {} '.format(*color, label))
+        qlabel.setAlignment(Qt.AlignBottom)
+
+        item.setSizeHint(qlabel.sizeHint())
+
+        self.setItemWidget(item, qlabel)

PPOCRLabel/libs/utils.py

@@ -10,30 +10,26 @@
 # SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF
 # CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
 # THE SOFTWARE.
-from math import sqrt
-from libs.ustr import ustr
 import hashlib
+import os
 import re
 import sys
+from math import sqrt
+
 import cv2
 import numpy as np
-import os
+from PyQt5.QtCore import QRegExp, QT_VERSION_STR
+from PyQt5.QtGui import QIcon, QRegExpValidator, QColor
+from PyQt5.QtWidgets import QPushButton, QAction, QMenu
+from libs.ustr import ustr
 
-__dir__ = os.path.dirname(os.path.abspath(__file__)) # 获取本程序文件路径
+__dir__ = os.path.dirname(os.path.abspath(__file__))  # 获取本程序文件路径
 __iconpath__ = os.path.abspath(os.path.join(__dir__, '../resources/icons'))
 
-try:
-    from PyQt5.QtGui import *
-    from PyQt5.QtCore import *
-    from PyQt5.QtWidgets import *
-except ImportError:
-    from PyQt4.QtGui import *
-    from PyQt4.QtCore import *
-
 
 def newIcon(icon, iconSize=None):
     if iconSize is not None:
-        return QIcon(QIcon(__iconpath__ + "/" + icon + ".png").pixmap(iconSize,iconSize))
+        return QIcon(QIcon(__iconpath__ + "/" + icon + ".png").pixmap(iconSize, iconSize))
     else:
         return QIcon(__iconpath__ + "/" + icon + ".png")
 
@@ -105,24 +101,25 @@ def generateColorByText(text):
     s = ustr(text)
     hashCode = int(hashlib.sha256(s.encode('utf-8')).hexdigest(), 16)
     r = int((hashCode / 255) % 255)
-    g = int((hashCode / 65025)  % 255)
-    b = int((hashCode / 16581375)  % 255)
+    g = int((hashCode / 65025) % 255)
+    b = int((hashCode / 16581375) % 255)
     return QColor(r, g, b, 100)
 
+
 def have_qstring():
     '''p3/qt5 get rid of QString wrapper as py3 has native unicode str type'''
     return not (sys.version_info.major >= 3 or QT_VERSION_STR.startswith('5.'))
 
-def util_qt_strlistclass():
-    return QStringList if have_qstring() else list
 
-def natural_sort(list, key=lambda s:s):
+def natural_sort(list, key=lambda s: s):
     """
     Sort the list into natural alphanumeric order.
     """
+
     def get_alphanum_key_func(key):
         convert = lambda text: int(text) if text.isdigit() else text
         return lambda s: [convert(c) for c in re.split('([0-9]+)', key(s))]
+
     sort_key = get_alphanum_key_func(key)
     list.sort(key=sort_key)
 
@@ -133,8 +130,8 @@ def get_rotate_crop_image(img, points):
     d = 0.0
     for index in range(-1, 3):
         d += -0.5 * (points[index + 1][1] + points[index][1]) * (
-                    points[index + 1][0] - points[index][0])
-    if d < 0: # counterclockwise
+                points[index + 1][0] - points[index][0])
+    if d < 0:  # counterclockwise
         tmp = np.array(points)
         points[1], points[3] = tmp[3], tmp[1]
 
@@ -163,10 +160,11 @@ def get_rotate_crop_image(img, points):
     except Exception as e:
         print(e)
 
+
 def stepsInfo(lang='en'):
     if lang == 'ch':
         msg = "1. 安装与运行：使用上述命令安装与运行程序。\n" \
-              "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.\n"\
+              "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.\n" \
               "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为 “X” 的图片进行自动标注。\n" \
               "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P，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。\n" \
@@ -181,25 +179,26 @@ def stepsInfo(lang='en'):
 
     else:
         msg = "1. Build and launch using the instructions above.\n" \
-              "2. Click 'Open Dir' in Menu/File to select the folder of the picture.\n"\
-              "3. Click 'Auto recognition', use PPOCR model to automatically annotate images which marked with 'X' before the file name."\
-              "4. Create Box:\n"\
-                   "4.1 Click 'Create RectBox' or press 'W' in English keyboard mode to draw a new rectangle detection box. Click and release left mouse to select a region to annotate the text area.\n"\
-                   "4.2 Press 'P' to enter four-point labeling mode which enables you to create any four-point shape by clicking four points with the left mouse button in succession and DOUBLE CLICK the left mouse as the signal of labeling completion.\n"\
-              "5. After the marking frame is drawn, the user clicks 'OK', and the detection frame will be pre-assigned a TEMPORARY label.\n"\
-              "6. Click re-Recognition, model will rewrite ALL recognition results in ALL detection box.\n"\
-              "7. Double click the result in 'recognition result' list to manually change inaccurate recognition results.\n"\
-              "8. Click 'Save', the image status will switch to '√',then the program automatically jump to the next.\n"\
-              "9. Click 'Delete Image' and the image will be deleted to the recycle bin.\n"\
-              "10. Labeling result: After closing the application or switching the file path, the manually saved label will be stored in *Label.txt* under the opened picture folder.\n"\
-              "    Click PaddleOCR-Save Recognition Results in the menu bar, the recognition training data of such pictures will be saved in the *crop_img* folder, and the recognition label will be saved in *rec_gt.txt*.\n" 
+              "2. Click 'Open Dir' in Menu/File to select the folder of the picture.\n" \
+              "3. Click 'Auto recognition', use PPOCR model to automatically annotate images which marked with 'X' before the file name." \
+              "4. Create Box:\n" \
+              "4.1 Click 'Create RectBox' or press 'W' in English keyboard mode to draw a new rectangle detection box. Click and release left mouse to select a region to annotate the text area.\n" \
+              "4.2 Press 'P' to enter four-point labeling mode which enables you to create any four-point shape by clicking four points with the left mouse button in succession and DOUBLE CLICK the left mouse as the signal of labeling completion.\n" \
+              "5. After the marking frame is drawn, the user clicks 'OK', and the detection frame will be pre-assigned a TEMPORARY label.\n" \
+              "6. Click re-Recognition, model will rewrite ALL recognition results in ALL detection box.\n" \
+              "7. Double click the result in 'recognition result' list to manually change inaccurate recognition results.\n" \
+              "8. Click 'Save', the image status will switch to '√',then the program automatically jump to the next.\n" \
+              "9. Click 'Delete Image' and the image will be deleted to the recycle bin.\n" \
+              "10. Labeling result: After closing the application or switching the file path, the manually saved label will be stored in *Label.txt* under the opened picture folder.\n" \
+              "    Click PaddleOCR-Save Recognition Results in the menu bar, the recognition training data of such pictures will be saved in the *crop_img* folder, and the recognition label will be saved in *rec_gt.txt*.\n"
 
     return msg
 
+
 def keysInfo(lang='en'):
     if lang == 'ch':
         msg = "快捷键\t\t\t说明\n" \
-              "———————————————————————\n"\
+              "———————————————————————\n" \
               "Ctrl + shift + R\t\t对当前图片的所有标记重新识别\n" \
               "W\t\t\t新建矩形框\n" \
               "Q\t\t\t新建四点框\n" \
@@ -223,17 +222,17 @@ def keysInfo(lang='en'):
               "———————————————————————\n" \
               "Ctrl + shift + R\t\tRe-recognize all the labels\n" \
               "\t\t\tof the current image\n" \
-              "\n"\
+              "\n" \
               "W\t\t\tCreate a rect box\n" \
               "Q\t\t\tCreate a four-points box\n" \
               "Ctrl + E\t\tEdit label of the selected box\n" \
               "Ctrl + R\t\tRe-recognize the selected box\n" \
               "Ctrl + C\t\tCopy and paste the selected\n" \
               "\t\t\tbox\n" \
-              "\n"\
+              "\n" \
               "Ctrl + Left Mouse\tMulti select the label\n" \
               "Button\t\t\tbox\n" \
-              "\n"\
+              "\n" \
               "Backspace\t\tDelete the selected box\n" \
               "Ctrl + V\t\tCheck image\n" \
               "Ctrl + Shift + d\tDelete image\n" \
@@ -245,4 +244,4 @@ def keysInfo(lang='en'):
               "———————————————————————\n" \
               "Notice:For Mac users, use the 'Command' key instead of the 'Ctrl' key"
 
-    return msg
+    return msg
\ No newline at end of file

PPOCRLabel/resources/strings/strings-en.properties

@@ -106,4 +106,7 @@ undo=Undo
 undoLastPoint=Undo Last Point
 autoSaveMode=Auto Export Label Mode
 lockBox=Lock selected box/Unlock all box
-lockBoxDetail=Lock selected box/Unlock all box
+lockBoxDetail=Lock selected box/Unlock all box
\ No newline at end of file
+keyListTitle=Key List
+keyDialogTip=Enter object label
+keyChange=Change Box Key

PPOCRLabel/resources/strings/strings-zh-CN.properties

@@ -107,3 +107,6 @@ undoLastPoint=撤销上个点
 autoSaveMode=自动导出标记结果
 lockBox=锁定框/解除锁定框
 lockBoxDetail=若当前没有框处于锁定状态则锁定选中的框，若存在锁定框则解除所有锁定框的锁定状态
+keyListTitle=关键词列表
+keyDialogTip=请输入类型名称
+keyChange=更改Box关键字类别
\ No newline at end of file

README.md

@@ -152,7 +152,7 @@ For a new language request, please refer to [Guideline for new language_requests
 
 [1] PP-OCR is a practical ultra-lightweight OCR system. It is mainly composed of three parts: DB text detection, detection frame correction and CRNN text recognition. The system adopts 19 effective strategies from 8 aspects including backbone network selection and adjustment, prediction head design, data augmentation, learning rate transformation strategy, regularization parameter selection, pre-training model use, and automatic model tailoring and quantization to optimize and slim down the models of each module (as shown in the green box above). The final results are an ultra-lightweight Chinese and English OCR model with an overall size of 3.5M and a 2.8M English digital OCR model. For more details, please refer to the PP-OCR technical article (https://arxiv.org/abs/2009.09941).
 
-[2] On the basis of PP-OCR, PP-OCRv2 is further optimized in five aspects. The detection model adopts CML(Collaborative Mutual Learning) knowledge distillation strategy and CopyPaste data expansion strategy. The recognition model adopts LCNet lightweight backbone network, U-DML knowledge distillation strategy and enhanced CTC loss function improvement (as shown in the red box above), which further improves the inference speed and prediction effect. For more details, please refer to the technical report of PP-OCRv2 (arXiv link is coming soon).
+[2] On the basis of PP-OCR, PP-OCRv2 is further optimized in five aspects. The detection model adopts CML(Collaborative Mutual Learning) knowledge distillation strategy and CopyPaste data expansion strategy. The recognition model adopts LCNet lightweight backbone network, U-DML knowledge distillation strategy and enhanced CTC loss function improvement (as shown in the red box above), which further improves the inference speed and prediction effect. For more details, please refer to the technical report of PP-OCRv2 (https://arxiv.org/abs/2109.03144).
 
 
 
@@ -181,16 +181,11 @@ For a new language request, please refer to [Guideline for new language_requests
 <a name="language_requests"></a>
 ## Guideline for New Language Requests
 
-If you want to request a new language support, a PR with 2 following files are needed：
+If you want to request a new language support, a PR with 1 following files are needed：
 
 1. In folder [ppocr/utils/dict](./ppocr/utils/dict),
 it is necessary to submit the dict text to this path and name it with `{language}_dict.txt` that contains a list of all characters. Please see the format example from other files in that folder.
 
-2. In folder [ppocr/utils/corpus](./ppocr/utils/corpus),
-it is necessary to submit the corpus to this path and name it with `{language}_corpus.txt` that contains a list of words in your language.
-Maybe, 50000 words per language is necessary at least.
-Of course, the more, the better.
-
 If your language has unique elements, please tell me in advance within any way, such as useful links, wikipedia and so on.
 
 More details, please refer to [Multilingual OCR Development Plan](https://github.com/PaddlePaddle/PaddleOCR/issues/1048).

benchmark/analysis.py

@@ -26,35 +26,57 @@ def parse_args():
     parser.add_argument(
         "--filename", type=str, help="The name of log which need to analysis.")
     parser.add_argument(
-        "--log_with_profiler", type=str, help="The path of train log with profiler")
+        "--log_with_profiler",
+        type=str,
+        help="The path of train log with profiler")
     parser.add_argument(
         "--profiler_path", type=str, help="The path of profiler timeline log.")
     parser.add_argument(
         "--keyword", type=str, help="Keyword to specify analysis data")
     parser.add_argument(
-        "--separator", type=str, default=None, help="Separator of different field in log")
+        "--separator",
+        type=str,
+        default=None,
+        help="Separator of different field in log")
     parser.add_argument(
         '--position', type=int, default=None, help='The position of data field')
     parser.add_argument(
-        '--range', type=str, default="", help='The range of data field to intercept')
+        '--range',
+        type=str,
+        default="",
+        help='The range of data field to intercept')
     parser.add_argument(
         '--base_batch_size', type=int, help='base_batch size on gpu')
     parser.add_argument(
-        '--skip_steps', type=int, default=0, help='The number of steps to be skipped')
+        '--skip_steps',
+        type=int,
+        default=0,
+        help='The number of steps to be skipped')
     parser.add_argument(
-        '--model_mode', type=int, default=-1, help='Analysis mode, default value is -1')
+        '--model_mode',
+        type=int,
+        default=-1,
+        help='Analysis mode, default value is -1')
+    parser.add_argument('--ips_unit', type=str, default=None, help='IPS unit')
     parser.add_argument(
-        '--ips_unit', type=str, default=None, help='IPS unit')
-    parser.add_argument(
-        '--model_name', type=str, default=0, help='training model_name, transformer_base')
+        '--model_name',
+        type=str,
+        default=0,
+        help='training model_name, transformer_base')
     parser.add_argument(
         '--mission_name', type=str, default=0, help='training mission name')
     parser.add_argument(
         '--direction_id', type=int, default=0, help='training direction_id')
     parser.add_argument(
-        '--run_mode', type=str, default="sp", help='multi process or single process')
+        '--run_mode',
+        type=str,
+        default="sp",
+        help='multi process or single process')
     parser.add_argument(
-        '--index', type=int, default=1, help='{1: speed, 2:mem, 3:profiler, 6:max_batch_size}')
+        '--index',
+        type=int,
+        default=1,
+        help='{1: speed, 2:mem, 3:profiler, 6:max_batch_size}')
     parser.add_argument(
         '--gpu_num', type=int, default=1, help='nums of training gpus')
     args = parser.parse_args()
@@ -72,7 +94,12 @@ def _is_number(num):
 
 
 class TimeAnalyzer(object):
-    def __init__(self, filename, keyword=None, separator=None, position=None, range="-1"):
+    def __init__(self,
+                 filename,
+                 keyword=None,
+                 separator=None,
+                 position=None,
+                 range="-1"):
         if filename is None:
             raise Exception("Please specify the filename!")
 
@@ -99,7 +126,8 @@ class TimeAnalyzer(object):
 
                     # Distil the string from a line.
                     line = line.strip()
-                    line_words = line.split(self.separator) if self.separator else line.split()
+                    line_words = line.split(
+                        self.separator) if self.separator else line.split()
                     if args.position:
                         result = line_words[self.position]
                     else:
@@ -108,27 +136,36 @@ class TimeAnalyzer(object):
                             if line_words[i] == self.keyword:
                                 result = line_words[i + 1]
                                 break
-    
+
                     # Distil the result from the picked string.
                     if not self.range:
                         result = result[0:]
                     elif _is_number(self.range):
-                        result = result[0: int(self.range)]
+                        result = result[0:int(self.range)]
                     else:
-                        result = result[int(self.range.split(":")[0]): int(self.range.split(":")[1])]
+                        result = result[int(self.range.split(":")[0]):int(
+                            self.range.split(":")[1])]
                     self.records.append(float(result))
                 except Exception as exc:
-                    print("line is: {}; separator={}; position={}".format(line, self.separator, self.position))
+                    print("line is: {}; separator={}; position={}".format(
+                        line, self.separator, self.position))
 
-        print("Extract {} records: separator={}; position={}".format(len(self.records), self.separator, self.position))
+        print("Extract {} records: separator={}; position={}".format(
+            len(self.records), self.separator, self.position))
 
-    def _get_fps(self, mode, batch_size, gpu_num, avg_of_records, run_mode, unit=None):
+    def _get_fps(self,
+                 mode,
+                 batch_size,
+                 gpu_num,
+                 avg_of_records,
+                 run_mode,
+                 unit=None):
         if mode == -1 and run_mode == 'sp':
             assert unit, "Please set the unit when mode is -1."
             fps = gpu_num * avg_of_records
         elif mode == -1 and run_mode == 'mp':
             assert unit, "Please set the unit when mode is -1."
-            fps = gpu_num * avg_of_records #temporarily, not used now
+            fps = gpu_num * avg_of_records  #temporarily, not used now
             print("------------this is mp")
         elif mode == 0:
             # s/step -> samples/s
@@ -155,12 +192,20 @@ class TimeAnalyzer(object):
 
         return fps, unit
 
-    def analysis(self, batch_size, gpu_num=1, skip_steps=0, mode=-1, run_mode='sp', unit=None):
+    def analysis(self,
+                 batch_size,
+                 gpu_num=1,
+                 skip_steps=0,
+                 mode=-1,
+                 run_mode='sp',
+                 unit=None):
         if batch_size <= 0:
             print("base_batch_size should larger than 0.")
             return 0, ''
 
-        if len(self.records) <= skip_steps:  # to address the condition which item of log equals to skip_steps
+        if len(
+                self.records
+        ) <= skip_steps:  # to address the condition which item of log equals to skip_steps
             print("no records")
             return 0, ''
 
@@ -180,16 +225,20 @@ class TimeAnalyzer(object):
                     skip_max = self.records[i]
 
         avg_of_records = sum_of_records / float(count)
-        avg_of_records_skipped = sum_of_records_skipped / float(count - skip_steps)
+        avg_of_records_skipped = sum_of_records_skipped / float(count -
+                                                                skip_steps)
 
-        fps, fps_unit = self._get_fps(mode, batch_size, gpu_num, avg_of_records, run_mode, unit)
-        fps_skipped, _ = self._get_fps(mode, batch_size, gpu_num, avg_of_records_skipped, run_mode, unit)
+        fps, fps_unit = self._get_fps(mode, batch_size, gpu_num, avg_of_records,
+                                      run_mode, unit)
+        fps_skipped, _ = self._get_fps(mode, batch_size, gpu_num,
+                                       avg_of_records_skipped, run_mode, unit)
         if mode == -1:
             print("average ips of %d steps, skip 0 step:" % count)
             print("\tAvg: %.3f %s" % (avg_of_records, fps_unit))
             print("\tFPS: %.3f %s" % (fps, fps_unit))
             if skip_steps > 0:
-                print("average ips of %d steps, skip %d steps:" % (count, skip_steps))
+                print("average ips of %d steps, skip %d steps:" %
+                      (count, skip_steps))
                 print("\tAvg: %.3f %s" % (avg_of_records_skipped, fps_unit))
                 print("\tMin: %.3f %s" % (skip_min, fps_unit))
                 print("\tMax: %.3f %s" % (skip_max, fps_unit))
@@ -199,7 +248,8 @@ class TimeAnalyzer(object):
             print("\tAvg: %.3f steps/s" % avg_of_records)
             print("\tFPS: %.3f %s" % (fps, fps_unit))
             if skip_steps > 0:
-                print("average latency of %d steps, skip %d steps:" % (count, skip_steps))
+                print("average latency of %d steps, skip %d steps:" %
+                      (count, skip_steps))
                 print("\tAvg: %.3f steps/s" % avg_of_records_skipped)
                 print("\tMin: %.3f steps/s" % skip_min)
                 print("\tMax: %.3f steps/s" % skip_max)
@@ -209,7 +259,8 @@ class TimeAnalyzer(object):
             print("\tAvg: %.3f s/step" % avg_of_records)
             print("\tFPS: %.3f %s" % (fps, fps_unit))
             if skip_steps > 0:
-                print("average latency of %d steps, skip %d steps:" % (count, skip_steps))
+                print("average latency of %d steps, skip %d steps:" %
+                      (count, skip_steps))
                 print("\tAvg: %.3f s/step" % avg_of_records_skipped)
                 print("\tMin: %.3f s/step" % skip_min)
                 print("\tMax: %.3f s/step" % skip_max)
@@ -236,7 +287,8 @@ if __name__ == "__main__":
             if args.gpu_num == 1:
                 run_info["log_with_profiler"] = args.log_with_profiler
                 run_info["profiler_path"] = args.profiler_path
-            analyzer = TimeAnalyzer(args.filename, args.keyword, args.separator, args.position, args.range)
+            analyzer = TimeAnalyzer(args.filename, args.keyword, args.separator,
+                                    args.position, args.range)
             run_info["FINAL_RESULT"], run_info["UNIT"] = analyzer.analysis(
                 batch_size=args.base_batch_size,
                 gpu_num=args.gpu_num,
@@ -245,29 +297,50 @@ if __name__ == "__main__":
                 run_mode=args.run_mode,
                 unit=args.ips_unit)
             try:
-                if int(os.getenv('job_fail_flag')) == 1 or int(run_info["FINAL_RESULT"]) == 0:
+                if int(os.getenv('job_fail_flag')) == 1 or int(run_info[
+                        "FINAL_RESULT"]) == 0:
                     run_info["JOB_FAIL_FLAG"] = 1
             except:
                 pass
         elif args.index == 3:
             run_info["FINAL_RESULT"] = {}
-            records_fo_total = TimeAnalyzer(args.filename, 'Framework overhead', None, 3, '').records
-            records_fo_ratio = TimeAnalyzer(args.filename, 'Framework overhead', None, 5).records
-            records_ct_total = TimeAnalyzer(args.filename, 'Computation time', None, 3, '').records
-            records_gm_total = TimeAnalyzer(args.filename, 'GpuMemcpy                Calls', None, 4, '').records
-            records_gm_ratio = TimeAnalyzer(args.filename, 'GpuMemcpy                Calls', None, 6).records
-            records_gmas_total = TimeAnalyzer(args.filename, 'GpuMemcpyAsync         Calls', None, 4, '').records
-            records_gms_total = TimeAnalyzer(args.filename, 'GpuMemcpySync          Calls', None, 4, '').records
-            run_info["FINAL_RESULT"]["Framework_Total"] = records_fo_total[0] if records_fo_total else 0
-            run_info["FINAL_RESULT"]["Framework_Ratio"] = records_fo_ratio[0] if records_fo_ratio else 0
-            run_info["FINAL_RESULT"]["ComputationTime_Total"] = records_ct_total[0] if records_ct_total else 0
-            run_info["FINAL_RESULT"]["GpuMemcpy_Total"] = records_gm_total[0] if records_gm_total else 0
-            run_info["FINAL_RESULT"]["GpuMemcpy_Ratio"] = records_gm_ratio[0] if records_gm_ratio else 0
-            run_info["FINAL_RESULT"]["GpuMemcpyAsync_Total"] = records_gmas_total[0] if records_gmas_total else 0
-            run_info["FINAL_RESULT"]["GpuMemcpySync_Total"] = records_gms_total[0] if records_gms_total else 0
+            records_fo_total = TimeAnalyzer(args.filename, 'Framework overhead',
+                                            None, 3, '').records
+            records_fo_ratio = TimeAnalyzer(args.filename, 'Framework overhead',
+                                            None, 5).records
+            records_ct_total = TimeAnalyzer(args.filename, 'Computation time',
+                                            None, 3, '').records
+            records_gm_total = TimeAnalyzer(args.filename,
+                                            'GpuMemcpy                Calls',
+                                            None, 4, '').records
+            records_gm_ratio = TimeAnalyzer(args.filename,
+                                            'GpuMemcpy                Calls',
+                                            None, 6).records
+            records_gmas_total = TimeAnalyzer(args.filename,
+                                              'GpuMemcpyAsync         Calls',
+                                              None, 4, '').records
+            records_gms_total = TimeAnalyzer(args.filename,
+                                             'GpuMemcpySync          Calls',
+                                             None, 4, '').records
+            run_info["FINAL_RESULT"]["Framework_Total"] = records_fo_total[
+                0] if records_fo_total else 0
+            run_info["FINAL_RESULT"]["Framework_Ratio"] = records_fo_ratio[
+                0] if records_fo_ratio else 0
+            run_info["FINAL_RESULT"][
+                "ComputationTime_Total"] = records_ct_total[
+                    0] if records_ct_total else 0
+            run_info["FINAL_RESULT"]["GpuMemcpy_Total"] = records_gm_total[
+                0] if records_gm_total else 0
+            run_info["FINAL_RESULT"]["GpuMemcpy_Ratio"] = records_gm_ratio[
+                0] if records_gm_ratio else 0
+            run_info["FINAL_RESULT"][
+                "GpuMemcpyAsync_Total"] = records_gmas_total[
+                    0] if records_gmas_total else 0
+            run_info["FINAL_RESULT"]["GpuMemcpySync_Total"] = records_gms_total[
+                0] if records_gms_total else 0
         else:
             print("Not support!")
     except Exception:
-            traceback.print_exc()
-    print("{}".format(json.dumps(run_info)))  # it's required, for the log file path  insert to the database
-
+        traceback.print_exc()
+    print("{}".format(json.dumps(run_info))
+          )  # it's required, for the log file path  insert to the database

benchmark/run_benchmark_det.sh

@@ -58,3 +58,4 @@ source ${BENCHMARK_ROOT}/scripts/run_model.sh      # 在该脚本中会对符合
 _set_params $@
 #_train      # 如果只想产出训练log,不解析,可取消注释
 _run         # 该函数在run_model.sh中,执行时会调用_train; 如果不联调只想要产出训练log可以注掉本行,提交时需打开
+

benchmark/run_det.sh

@@ -36,3 +36,4 @@ for model_mode in ${model_mode_list[@]}; do
 done
 
 
+

configs/det/det_mv3_db.yml

 Global:
   use_gpu: true
+  use_xpu: false
   epoch_num: 1200
   log_smooth_window: 20
   print_batch_step: 10

configs/vqa/re/layoutlmv2.yml

+Global:
+  use_gpu: True
+  epoch_num: &epoch_num 200
+  log_smooth_window: 10
+  print_batch_step: 10
+  save_model_dir: ./output/re_layoutlmv2/
+  save_epoch_step: 2000
+  # evaluation is run every 10 iterations after the 0th iteration
+  eval_batch_step: [ 0, 19 ]
+  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/
+
+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/XFUND/zh_train/image
+    label_file_list: 
+      - train_data/XFUND/zh_train/xfun_normalize_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 ppstructure/vqa/labels/labels_ser.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:
+          keep_keys: [ 'input_ids', 'bbox', 'image', 'attention_mask', 'token_type_ids','entities', 'relations'] # dataloader will return list in this order
+  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/XFUND/zh_val/image
+    label_file_list:
+      - train_data/XFUND/zh_val/xfun_normalize_val.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:
+          keep_keys: [ 'input_ids', 'bbox', 'image', 'attention_mask', 'token_type_ids','entities', 'relations'] # dataloader will return list in this order
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 8
+    num_workers: 8
+    collate_fn: ListCollator

configs/vqa/re/layoutxlm.yml

@@ -21,7 +21,7 @@ Architecture:
   Backbone:
     name: LayoutXLMForRe
     pretrained: True
-    checkpoints: 
+    checkpoints:
 
 Loss:
   name: LossFromOutput
@@ -35,6 +35,7 @@ Optimizer:
   clip_norm: 10
   lr:
     learning_rate: 0.00005
+    warmup_epoch: 10
   regularizer:
     name: L2
     factor: 0.00000
@@ -81,7 +82,7 @@ Train:
     shuffle: True
     drop_last: False
     batch_size_per_card: 8
-    num_workers: 4
+    num_workers: 8
     collate_fn: ListCollator
 
 Eval:
@@ -118,5 +119,5 @@ Eval:
     shuffle: False
     drop_last: False
     batch_size_per_card: 8
-    num_workers: 4
+    num_workers: 8
     collate_fn: ListCollator

configs/vqa/ser/layoutlmv2.yml

+Global:
+  use_gpu: True
+  epoch_num: &epoch_num 200
+  log_smooth_window: 10
+  print_batch_step: 10
+  save_model_dir: ./output/ser_layoutlmv2/
+  save_epoch_step: 2000
+  # evaluation is run every 10 iterations after the 0th iteration
+  eval_batch_step: [ 0, 19 ]
+  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/
+
+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 ppstructure/vqa/labels/labels_ser.txt
+
+Metric:
+  name: VQASerTokenMetric
+  main_indicator: hmean
+
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: train_data/XFUND/zh_train/image
+    label_file_list: 
+      - train_data/XFUND/zh_train/xfun_normalize_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:
+          keep_keys: [ 'input_ids','labels', 'bbox', 'image', 'attention_mask', 'token_type_ids'] # dataloader will return list in this order
+  loader:
+    shuffle: True
+    drop_last: False
+    batch_size_per_card: 8
+    num_workers: 4
+
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: train_data/XFUND/zh_val/image
+    label_file_list:
+      - train_data/XFUND/zh_val/xfun_normalize_val.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:
+          keep_keys: [ 'input_ids', 'labels', 'bbox', 'image', 'attention_mask', 'token_type_ids'] # dataloader will return list in this order
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 8
+    num_workers: 4