提交 315c07d5 编写于 作者: L LDOUBLEV

fix conflicts

......@@ -106,7 +106,7 @@ class MainWindow(QMainWindow, WindowMixin):
getStr = lambda strId: self.stringBundle.getString(strId)
self.defaultSaveDir = defaultSaveDir
self.ocr = PaddleOCR(use_pdserving=False, use_angle_cls=True, det=True, cls=True, use_gpu=True, lang=lang)
self.ocr = PaddleOCR(use_pdserving=False, use_angle_cls=True, det=True, cls=True, use_gpu=False, lang=lang)
if os.path.exists('./data/paddle.png'):
result = self.ocr.ocr('./data/paddle.png', cls=True, det=True)
......@@ -274,6 +274,7 @@ class MainWindow(QMainWindow, WindowMixin):
self.preButton.setIconSize(QSize(40, 100))
self.preButton.clicked.connect(self.openPrevImg)
self.preButton.setStyleSheet('border: none;')
self.preButton.setShortcut('a')
self.iconlist = QListWidget()
self.iconlist.setViewMode(QListView.IconMode)
self.iconlist.setFlow(QListView.TopToBottom)
......@@ -289,12 +290,12 @@ class MainWindow(QMainWindow, WindowMixin):
self.nextButton.setIconSize(QSize(40, 100))
self.nextButton.setStyleSheet('border: none;')
self.nextButton.clicked.connect(self.openNextImg)
self.nextButton.setShortcut('d')
hlayout.addWidget(self.preButton)
hlayout.addWidget(self.iconlist)
hlayout.addWidget(self.nextButton)
# self.setLayout(hlayout)
iconListContainer = QWidget()
iconListContainer.setLayout(hlayout)
......@@ -359,11 +360,6 @@ class MainWindow(QMainWindow, WindowMixin):
opendir = action(getStr('openDir'), self.openDirDialog,
'Ctrl+u', 'open', getStr('openDir'))
openNextImg = action(getStr('nextImg'), self.openNextImg,
'd', 'next', getStr('nextImgDetail'))
openPrevImg = action(getStr('prevImg'), self.openPrevImg,
'a', 'prev', getStr('prevImgDetail'))
save = action(getStr('save'), self.saveFile,
'Ctrl+V', 'verify', getStr('saveDetail'), enabled=False)
......@@ -371,7 +367,7 @@ class MainWindow(QMainWindow, WindowMixin):
alcm = action(getStr('choosemodel'), self.autolcm,
'Ctrl+M', 'next', getStr('tipchoosemodel'))
deleteImg = action(getStr('deleteImg'), self.deleteImg, 'Ctrl+D', 'close', getStr('deleteImgDetail'),
deleteImg = action(getStr('deleteImg'), self.deleteImg, 'Ctrl+Shift+D', 'close', getStr('deleteImgDetail'),
enabled=True)
resetAll = action(getStr('resetAll'), self.resetAll, None, 'resetall', getStr('resetAllDetail'))
......@@ -388,7 +384,7 @@ class MainWindow(QMainWindow, WindowMixin):
'w', 'new', getStr('crtBoxDetail'), enabled=False)
delete = action(getStr('delBox'), self.deleteSelectedShape,
'Delete', 'delete', getStr('delBoxDetail'), enabled=False)
'backspace', 'delete', getStr('delBoxDetail'), enabled=False)
copy = action(getStr('dupBox'), self.copySelectedShape,
'Ctrl+C', 'copy', getStr('dupBoxDetail'),
enabled=False)
......@@ -446,8 +442,11 @@ class MainWindow(QMainWindow, WindowMixin):
reRec = action(getStr('reRecognition'), self.reRecognition,
'Ctrl+Shift+R', 'reRec', getStr('reRecognition'), enabled=False)
singleRere = action(getStr('singleRe'), self.singleRerecognition,
'Ctrl+R', 'reRec', getStr('singleRe'), enabled=False)
createpoly = action(getStr('creatPolygon'), self.createPolygon,
'p', 'new', 'Creat Polygon', enabled=True)
'q', 'new', 'Creat Polygon', enabled=True)
saveRec = action(getStr('saveRec'), self.saveRecResult,
'', 'save', getStr('saveRec'), enabled=False)
......@@ -491,6 +490,7 @@ class MainWindow(QMainWindow, WindowMixin):
icon='color', tip=getStr('shapeFillColorDetail'),
enabled=False)
# Label list context menu.
labelMenu = QMenu()
addActions(labelMenu, (edit, delete))
......@@ -501,7 +501,6 @@ class MainWindow(QMainWindow, WindowMixin):
# Draw squares/rectangles
self.drawSquaresOption = QAction(getStr('drawSquares'), self)
self.drawSquaresOption.setShortcut('Ctrl+Shift+R')
self.drawSquaresOption.setCheckable(True)
self.drawSquaresOption.setChecked(settings.get(SETTING_DRAW_SQUARE, False))
self.drawSquaresOption.triggered.connect(self.toogleDrawSquare)
......@@ -509,7 +508,7 @@ class MainWindow(QMainWindow, WindowMixin):
# Store actions for further handling.
self.actions = struct(save=save, open=open, resetAll=resetAll, deleteImg=deleteImg,
lineColor=color1, create=create, delete=delete, edit=edit, copy=copy,
saveRec=saveRec,
saveRec=saveRec, singleRere=singleRere,AutoRec=AutoRec,reRec=reRec,
createMode=createMode, editMode=editMode,
shapeLineColor=shapeLineColor, shapeFillColor=shapeFillColor,
zoom=zoom, zoomIn=zoomIn, zoomOut=zoomOut, zoomOrg=zoomOrg,
......@@ -518,9 +517,9 @@ class MainWindow(QMainWindow, WindowMixin):
fileMenuActions=(
open, opendir, saveLabel, resetAll, quit),
beginner=(), advanced=(),
editMenu=(createpoly, edit, copy, delete,
editMenu=(createpoly, edit, copy, delete,singleRere,
None, color1, self.drawSquaresOption),
beginnerContext=(create, edit, copy, delete),
beginnerContext=(create, edit, copy, delete, singleRere),
advancedContext=(createMode, editMode, edit, copy,
delete, shapeLineColor, shapeFillColor),
onLoadActive=(
......@@ -562,7 +561,7 @@ class MainWindow(QMainWindow, WindowMixin):
zoomIn, zoomOut, zoomOrg, None,
fitWindow, fitWidth))
addActions(self.menus.autolabel, (alcm, None, help)) #
addActions(self.menus.autolabel, (AutoRec, reRec, alcm, None, help)) #
self.menus.file.aboutToShow.connect(self.updateFileMenu)
......@@ -572,6 +571,7 @@ class MainWindow(QMainWindow, WindowMixin):
action('&Copy here', self.copyShape),
action('&Move here', self.moveShape)))
self.statusBar().showMessage('%s started.' % __appname__)
self.statusBar().show()
......@@ -919,6 +919,7 @@ class MainWindow(QMainWindow, WindowMixin):
self.actions.edit.setEnabled(selected)
self.actions.shapeLineColor.setEnabled(selected)
self.actions.shapeFillColor.setEnabled(selected)
self.actions.singleRere.setEnabled(selected)
def addLabel(self, shape):
shape.paintLabel = self.displayLabelOption.isChecked()
......@@ -988,6 +989,19 @@ class MainWindow(QMainWindow, WindowMixin):
self.updateComboBox()
self.canvas.loadShapes(s)
def singleLabel(self, shape):
if shape is None:
# print('rm empty label')
return
item = self.shapesToItems[shape]
item.setText(shape.label)
self.updateComboBox()
# ADD:
item = self.shapesToItemsbox[shape]
item.setText(str([(int(p.x()), int(p.y())) for p in shape.points]))
self.updateComboBox()
def updateComboBox(self):
# Get the unique labels and add them to the Combobox.
itemsTextList = [str(self.labelList.item(i).text()) for i in range(self.labelList.count())]
......@@ -1441,6 +1455,8 @@ class MainWindow(QMainWindow, WindowMixin):
self.haveAutoReced = False
self.AutoRecognition.setEnabled(True)
self.reRecogButton.setEnabled(True)
self.actions.AutoRec.setEnabled(True)
self.actions.reRec.setEnabled(True)
self.actions.saveLabel.setEnabled(True)
......@@ -1755,6 +1771,7 @@ class MainWindow(QMainWindow, WindowMixin):
self.loadFile(self.filePath) # ADD
self.haveAutoReced = True
self.AutoRecognition.setEnabled(False)
self.actions.AutoRec.setEnabled(False)
self.setDirty()
self.saveCacheLabel()
......@@ -1794,6 +1811,27 @@ class MainWindow(QMainWindow, WindowMixin):
else:
QMessageBox.information(self, "Information", "Draw a box!")
def singleRerecognition(self):
img = cv2.imread(self.filePath)
shape = self.canvas.selectedShape
box = [[int(p.x()), int(p.y())] for p in shape.points]
assert len(box) == 4
img_crop = get_rotate_crop_image(img, np.array(box, np.float32))
if img_crop is None:
msg = 'Can not recognise the detection box in ' + self.filePath + '. Please change manually'
QMessageBox.information(self, "Information", msg)
return
result = self.ocr.ocr(img_crop, cls=True, det=False)
if result[0][0] is not '':
result.insert(0, box)
print('result in reRec is ', result)
if result[1][0] == shape.label:
print('label no change')
else:
shape.label = result[1][0]
self.singleLabel(shape)
self.setDirty()
print(box)
def autolcm(self):
vbox = QVBoxLayout()
......@@ -1825,6 +1863,7 @@ class MainWindow(QMainWindow, WindowMixin):
self.dialog.exec_()
if self.filePath:
self.AutoRecognition.setEnabled(True)
self.actions.AutoRec.setEnabled(True)
def modelChoose(self):
......
......@@ -6,6 +6,10 @@ PPOCRLabel is a semi-automatic graphic annotation tool suitable for OCR field. I
<img src="./data/gif/steps_en.gif" width="100%"/>
### Recent Update
- 2020.12.18: Support re-recognition of a single label box (by [ninetailskim](https://github.com/ninetailskim) ), perfect shortcut keys.
## Installation
### 1. Install PaddleOCR
......@@ -92,6 +96,25 @@ Therefore, if the recognition result has been manually changed before, it may ch
## Explanation
### Shortcut keys
| Shortcut keys | Description |
| ---------------- | ------------------------------------------------ |
| Ctrl + shift + A | Automatically label all unchecked images |
| Ctrl + shift + R | Re-recognize all the labels of the current image |
| W | Create a rect box |
| Q | Create a four-points box |
| Ctrl + E | Edit label of the selected box |
| Ctrl + R | Re-recognize the selected box |
| Backspace | Delete the selected box |
| Ctrl + V | Check image |
| Ctrl + Shift + d | Delete image |
| D | Next image |
| A | Previous image |
| Ctrl++ | Zoom in |
| Ctrl-- | Zoom out |
| ↑→↓← | Move selected box |
### Built-in Model
- Default model: PPOCRLabel uses the Chinese and English ultra-lightweight OCR model in PaddleOCR by default, supports Chinese, English and number recognition, and multiple language detection.
......
......@@ -6,6 +6,10 @@ PPOCRLabel是一款适用于OCR领域的半自动化图形标注工具,使用p
<img src="./data/gif/steps.gif" width="100%"/>
#### 近期更新
- 2020.12.18: 支持对单个标记框进行重新识别(by [ninetailskim](https://github.com/ninetailskim) ),完善快捷键。
## 安装
### 1. 安装PaddleOCR
......@@ -72,6 +76,26 @@ python3 PPOCRLabel.py --lang ch
| crop_img | 识别数据。按照检测框切割后的图片。与rec_gt.txt同时产生。 |
## 说明
### 快捷键
| 快捷键 | 说明 |
| ---------------- | ---------------------------- |
| Ctrl + shift + A | 自动标注所有未确认过的图片 |
| Ctrl + shift + R | 对当前图片的所有标记重新识别 |
| W | 新建矩形框 |
| Q | 新建四点框 |
| Ctrl + E | 编辑所选框标签 |
| Ctrl + R | 重新识别所选标记 |
| Backspace | 删除所选框 |
| Ctrl + V | 确认本张图片标记 |
| Ctrl + Shift + d | 删除本张图片 |
| D | 下一张图片 |
| A | 上一张图片 |
| Ctrl++ | 缩小 |
| Ctrl-- | 放大 |
| ↑→↓← | 移动标记框 |
### 内置模型
- 默认模型:PPOCRLabel默认使用PaddleOCR中的中英文超轻量OCR模型,支持中英文与数字识别,多种语言检测。
......
......@@ -46,8 +46,9 @@ class Worker(QThread):
chars = res[1][0]
cond = res[1][1]
posi = res[0]
strs += "Transcription: " + chars + " Probability: " + str(
cond) + " Location: " + json.dumps(posi) + '\n'
strs += "Transcription: " + chars + " Probability: " + str(cond) + \
" Location: " + json.dumps(posi) +'\n'
# Sending large amounts of data repeatedly through pyqtSignal may affect the program efficiency
self.listValue.emit(strs)
self.mainThread.result_dic = self.result_dic
self.mainThread.filePath = Imgpath
......
此差异已折叠。
......@@ -95,3 +95,4 @@ autolabeling=自动标注中
hideBox=隐藏所有标注
showBox=显示所有标注
saveLabel=保存标记结果
singleRe=重识别此区块
\ No newline at end of file
saveAsDetail=將標籤保存到其他文件
changeSaveDir=改變存放目錄
openFile=開啟檔案
shapeLineColorDetail=更改線條顏色
resetAll=重置
crtBox=創建區塊
crtBoxDetail=畫一個區塊
dupBoxDetail=複製區塊
verifyImg=驗證圖像
zoominDetail=放大
verifyImgDetail=驗證圖像
saveDetail=將標籤存到
openFileDetail=打開圖像
fitWidthDetail=調整到窗口寬度
tutorial=YouTube教學
editLabel=編輯標籤
openAnnotationDetail=打開標籤文件
quit=結束
shapeFillColorDetail=更改填充顏色
closeCurDetail=關閉目前檔案
closeCur=關閉
deleteImg=刪除圖像
deleteImgDetail=刪除目前圖像
fitWin=調整到跟窗口一樣大小
delBox=刪除選取區塊
boxLineColorDetail=選擇框線顏色
originalsize=原始大小
resetAllDetail=重設所有設定
zoomoutDetail=畫面放大
save=儲存
saveAs=另存為
fitWinDetail=縮放到窗口一樣
openDir=開啟目錄
copyPrevBounding=複製當前圖像中的上一個邊界框
showHide=顯示/隱藏標籤
changeSaveFormat=更改儲存格式
shapeFillColor=填充顏色
quitApp=離開本程式
dupBox=複製區塊
delBoxDetail=刪除區塊
zoomin=放大畫面
info=資訊
openAnnotation=開啟標籤
prevImgDetail=上一個圖像
fitWidth=縮放到跟畫面一樣寬
zoomout=縮小畫面
changeSavedAnnotationDir=更改預設標籤存的目錄
nextImgDetail=下一個圖像
originalsizeDetail=放大到原始大小
prevImg=上一個圖像
tutorialDetail=顯示示範內容
shapeLineColor=形狀線條顏色
boxLineColor=日期分隔線顏色
editLabelDetail=修改所選區塊的標籤
nextImg=下一張圖片
useDefaultLabel=使用預設標籤
useDifficult=有難度的
boxLabelText=區塊的標籤
labels=標籤
autoSaveMode=自動儲存模式
singleClsMode=單一類別模式
displayLabel=顯示類別
fileList=檔案清單
files=檔案
iconList=XX
icon=XX
advancedMode=進階模式
advancedModeDetail=切到進階模式
showAllBoxDetail=顯示所有區塊
hideAllBoxDetail=隱藏所有區塊
......@@ -95,3 +95,4 @@ autolabeling=Automatic Labeling
hideBox=Hide All Box
showBox=Show All Box
saveLabel=Save Label
singleRe=Re-recognition RectBox
\ No newline at end of file
......@@ -122,8 +122,7 @@ For a new language request, please refer to [Guideline for new language_requests
<img src="./doc/ppocr_framework.png" width="800">
</div>
PP-OCR is a practical ultra-lightweight OCR system. It is mainly composed of three parts: DB text detection, detection frame correction and CRNN text recognition. The system adopts 19 effective strategies from 8 aspects including backbone network selection and adjustment, prediction head design, data augmentation, learning rate transformation strategy, regularization parameter selection, pre-training model use, and automatic model tailoring and quantization to optimize and slim down the models of each module. The final results are an ultra-lightweight Chinese and English OCR model with an overall size of 3.5M and a 2.8M English digital OCR model. For more details, please refer to the PP-OCR technical article (https://arxiv.org/abs/2009.09941). Besides, The implementation of the FPGM Pruner and PACT quantization is based on [PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim).
PP-OCR is a practical ultra-lightweight OCR system. It is mainly composed of three parts: DB text detection[2], detection frame correction and CRNN text recognition[7]. The system adopts 19 effective strategies from 8 aspects including backbone network selection and adjustment, prediction head design, data augmentation, learning rate transformation strategy, regularization parameter selection, pre-training model use, and automatic model tailoring and quantization to optimize and slim down the models of each module. The final results are an ultra-lightweight Chinese and English OCR model with an overall size of 3.5M and a 2.8M English digital OCR model. For more details, please refer to the PP-OCR technical article (https://arxiv.org/abs/2009.09941). Besides, The implementation of the FPGM Pruner [8] and PACT quantization [9] is based on [PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim).
## Visualization [more](./doc/doc_en/visualization_en.md)
......
......@@ -8,8 +8,8 @@ PaddleOCR同时支持动态图与静态图两种编程范式
- 静态图版本:develop分支
**近期更新**
- 2020.12.21 [FAQ](./doc/doc_ch/FAQ.md)新增5个高频问题,总数132个,每周一都会更新,欢迎大家持续关注。
- 2020.12.15 更新数据合成工具[Style-Text](./StyleText/README_ch.md),可以批量合成大量与目标场景类似的图像,在多个场景验证,效果明显提升。
- 2020.12.14 [FAQ](./doc/doc_ch/FAQ.md)新增5个高频问题,总数127个,每周一都会更新,欢迎大家持续关注。
- 2020.11.25 更新半自动标注工具[PPOCRLabel](./PPOCRLabel/README_ch.md),辅助开发者高效完成标注任务,输出格式与PP-OCR训练任务完美衔接。
- 2020.9.22 更新PP-OCR技术文章,https://arxiv.org/abs/2009.09941
- [More](./doc/doc_ch/update.md)
......@@ -101,8 +101,8 @@ PaddleOCR同时支持动态图与静态图两种编程范式
- [效果展示](#效果展示)
- FAQ
- [【精选】OCR精选10个问题](./doc/doc_ch/FAQ.md)
- [【理论篇】OCR通用30个问题](./doc/doc_ch/FAQ.md)
- [【实战篇】PaddleOCR实战84个问题](./doc/doc_ch/FAQ.md)
- [【理论篇】OCR通用31个问题](./doc/doc_ch/FAQ.md)
- [【实战篇】PaddleOCR实战91个问题](./doc/doc_ch/FAQ.md)
- [技术交流群](#欢迎加入PaddleOCR技术交流群)
- [参考文献](./doc/doc_ch/reference.md)
- [许可证书](#许可证书)
......@@ -115,7 +115,7 @@ PaddleOCR同时支持动态图与静态图两种编程范式
<img src="./doc/ppocr_framework.png" width="800">
</div>
PP-OCR是一个实用的超轻量OCR系统。主要由DB文本检测、检测框矫正和CRNN文本识别三部分组成。该系统从骨干网络选择和调整、预测头部的设计、数据增强、学习率变换策略、正则化参数选择、预训练模型使用以及模型自动裁剪量化8个方面,采用19个有效策略,对各个模块的模型进行效果调优和瘦身,最终得到整体大小为3.5M的超轻量中英文OCR和2.8M的英文数字OCR。更多细节请参考PP-OCR技术方案 https://arxiv.org/abs/2009.09941 。其中FPGM裁剪器和PACT量化的实现可以参考[PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim)
PP-OCR是一个实用的超轻量OCR系统。主要由DB文本检测[2]、检测框矫正和CRNN文本识别三部分组成[7]。该系统从骨干网络选择和调整、预测头部的设计、数据增强、学习率变换策略、正则化参数选择、预训练模型使用以及模型自动裁剪量化8个方面,采用19个有效策略,对各个模块的模型进行效果调优和瘦身,最终得到整体大小为3.5M的超轻量中英文OCR和2.8M的英文数字OCR。更多细节请参考PP-OCR技术方案 https://arxiv.org/abs/2009.09941 。其中FPGM裁剪器[8]和PACT量化[9]的实现可以参考[PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim)
<a name="效果展示"></a>
## 效果展示 [more](./doc/doc_ch/visualization.md)
......
......@@ -22,7 +22,7 @@ English | [简体中文](README_ch.md)
</div>
The Style-Text data synthesis tool is a tool based on Baidu's self-developed text editing algorithm "Editing Text in the Wild" [https://arxiv.org/abs/1908.03047](https://arxiv.org/abs/1908.03047).
The Style-Text data synthesis tool is a tool based on Baidu and HUST cooperation research work, "Editing Text in the Wild" [https://arxiv.org/abs/1908.03047](https://arxiv.org/abs/1908.03047).
Different from the commonly used GAN-based data synthesis tools, the main framework of Style-Text includes:
* (1) Text foreground style transfer module.
......@@ -78,6 +78,7 @@ python3 tools/synth_image.py -c configs/config.yml --style_image examples/style_
* Note 3: You can modify `use_gpu` in `configs/config.yml` to determine whether to use GPU for prediction.
For example, enter the following image and corpus `PaddleOCR`.
<div align="center">
......@@ -142,6 +143,7 @@ We provide a general dataset containing Chinese, English and Korean (50,000 imag
``` bash
python3 tools/synth_dataset.py -c configs/dataset_config.yml
```
We also provide example corpus and images in `examples` folder.
<div align="center">
<img src="examples/style_images/1.jpg" width="300">
......
......@@ -21,7 +21,7 @@
</div>
Style-Text数据合成工具是基于百度自研的文本编辑算法《Editing Text in the Wild》https://arxiv.org/abs/1908.03047
Style-Text数据合成工具是基于百度和华科合作研发的文本编辑算法《Editing Text in the Wild》https://arxiv.org/abs/1908.03047
不同于常用的基于GAN的数据合成工具,Style-Text主要框架包括:1.文本前景风格迁移模块 2.背景抽取模块 3.融合模块。经过这样三步,就可以迅速实现图像文本风格迁移。下图是一些该数据合成工具效果图。
......@@ -128,7 +128,7 @@ python3 tools/synth_image.py -c configs/config.yml --style_image examples/style_
2. 运行`tools/synth_dataset`合成数据:
``` bash
python tools/synth_dataset.py -c configs/dataset_config.yml
python3 tools/synth_dataset.py -c configs/dataset_config.yml
```
我们在examples目录下提供了样例图片和语料。
<div align="center">
......
......@@ -107,10 +107,10 @@ make inference_lib_dist
For more compilation parameter options, please refer to the official website of the Paddle C++ inference library:[https://www.paddlepaddle.org.cn/documentation/docs/en/advanced_guide/inference_deployment/inference/build_and_install_lib_en.html](https://www.paddlepaddle.org.cn/documentation/docs/en/advanced_guide/inference_deployment/inference/build_and_install_lib_en.html).
* After the compilation process, you can see the following files in the folder of `build/fluid_inference_install_dir/`.
* After the compilation process, you can see the following files in the folder of `build/paddle_inference_install_dir/`.
```
build/fluid_inference_install_dir/
build/paddle_inference_install_dir/
|-- CMakeCache.txt
|-- paddle
|-- third_party
......
......@@ -81,14 +81,14 @@ void ResizeImgType0::Run(const cv::Mat &img, cv::Mat &resize_img,
else if (resize_h / 32 < 1 + 1e-5)
resize_h = 32;
else
resize_h = (resize_h / 32 - 1) * 32;
resize_h = (resize_h / 32) * 32;
if (resize_w % 32 == 0)
resize_w = resize_w;
else if (resize_w / 32 < 1 + 1e-5)
resize_w = 32;
else
resize_w = (resize_w / 32 - 1) * 32;
resize_w = (resize_w / 32) * 32;
cv::resize(img, resize_img, cv::Size(resize_w, resize_h));
......
......@@ -10,7 +10,7 @@ max_side_len 960
det_db_thresh 0.3
det_db_box_thresh 0.5
det_db_unclip_ratio 2.0
det_model_dir ../../../deploy/cpp_infer/inference/ch_ppocr_mobile_v2.0_det_infer/
det_model_dir ./inference/ch_ppocr_mobile_v2.0_det_infer/
# cls config
use_angle_cls 0
......
......@@ -9,42 +9,55 @@
## PaddleOCR常见问题汇总(持续更新)
* [近期更新(2020.12.14](#近期更新)
* [近期更新(2020.12.21](#近期更新)
* [【精选】OCR精选10个问题](#OCR精选10个问题)
* [【理论篇】OCR通用30个问题](#OCR通用问题)
* [【理论篇】OCR通用31个问题](#OCR通用问题)
* [基础知识7题](#基础知识)
* [数据集7题](#数据集2)
* [模型训练调优7题](#模型训练调优2)
* [预测部署9题](#预测部署2)
* [【实战篇】PaddleOCR实战87个问题](#PaddleOCR实战问题)
* [使用咨询21题](#使用咨询)
* [模型训练调优17题](#模型训练调优2)
* [【实战篇】PaddleOCR实战91个问题](#PaddleOCR实战问题)
* [使用咨询24题](#使用咨询)
* [数据集17题](#数据集3)
* [模型训练调优25题](#模型训练调优3)
* [预测部署24](#预测部署3)
* [预测部署25](#预测部署3)
<a name="近期更新"></a>
## 近期更新(2020.12.14
## 近期更新(2020.12.21
#### Q3.1.21:PaddleOCR支持动态图吗
**A**:动态图版本正在紧锣密鼓开发中,将于2020年12月16日发布,敬请关注
#### Q2.3.17: StyleText 合成数据效果不好
**A**: StyleText模型生成的数据主要用于OCR识别模型的训练。PaddleOCR目前识别模型的输入为32 x N,因此当前版本模型主要适用高度为32的数据。
建议要合成的数据尺寸设置为32 x N。尺寸相差不多的数据也可以生成,尺寸很大或很小的数据效果确实不佳
#### Q3.3.23:检测模型训练或预测时出现elementwise_add报错
#### Q3.1.22: ModuleNotFoundError: No module named 'paddle.nn',
**A**: paddle.nn是Paddle2.0版本特有的功能,请安装大于等于Paddle 2.0.0rc1的版本,安装方式为
```
python3 -m pip install paddlepaddle-gpu==2.0.0rc1 -i https://mirror.baidu.com/pypi/simple
```
**A**:设置的输入尺寸必须是32的倍数,否则在网络多次下采样和上采样后,feature map会产生1个像素的diff,从而导致elementwise_add时报shape不匹配的错误。
#### Q3.1.23: ImportError: /usr/lib/x86_64_linux-gnu/libstdc++.so.6:version `CXXABI_1.3.11` not found (required by /usr/lib/python3.6/site-package/paddle/fluid/core+avx.so)
**A**:这个问题是glibc版本不足导致的,Paddle2.0rc1版本对gcc版本和glib版本有更高的要求,推荐gcc版本为8.2,glibc版本2.12以上。
如果您的环境不满足这个要求,或者使用的docker镜像为:
`hub.baidubce.com/paddlepaddle/paddle:latest-gpu-cuda9.0-cudnn7-dev`
`hub.baidubce.com/paddlepaddle/paddle:latest-gpu-cuda9.0-cudnn7-dev`,安装Paddle2.0rc版本可能会出现上述错误,
2.0版本推荐使用新的docker镜像 `paddlepaddle/paddle:latest-dev-cuda10.1-cudnn7-gcc82`
或者访问[dockerhub](https://hub.docker.com/r/paddlepaddle/paddle/tags/)获得与您机器适配的镜像。
#### Q3.3.24: DB检测训练输入尺寸640,可以改大一些吗?
#### Q3.1.24: PaddleOCR develop分支和dygraph分支的区别?
**A**:目前PaddleOCR有四个分支,分别是:
- develop:基于Paddle静态图开发的分支,推荐使用paddle1.8 或者2.0版本,该分支具备完善的模型训练、预测、推理部署、量化裁剪等功能,领先于release/1.1分支。
- release/1.1:PaddleOCR 发布的第一个稳定版本,基于静态图开发,具备完善的训练、预测、推理部署、量化裁剪等功能。
- dygraph:基于Paddle动态图开发的分支,未来将作为主要开发分支,运行要求使用Paddle2.0rc1版本,目前仍在开发中。
- release/2.0-rc1-0:PaddleOCR发布的第二个稳定版本,基于动态图和paddle2.0版本开发,动态图开发的工程更易于调试,目前支,支持模型训练、预测,暂不支持移动端部署。
**A**: 不建议改大。检测模型训练输入尺寸是预处理中random crop后的尺寸,并非直接将原图进行resize,多数场景下这个尺寸并不小了,改大后可能反而并不合适,而且训练会变慢。另外,代码里可能有的地方参数按照预设输入尺寸适配的,改大后可能有隐藏风险
如果您已经上手过PaddleOCR,并且希望在各种环境上部署PaddleOCR,目前建议使用静态图分支,develop或者release/1.1分支。如果您是初学者,想快速训练,调试PaddleOCR中的算法,建议尝鲜PaddleOCR dygraph分支
#### Q3.3.25: 识别模型训练时,loss能正常下降,但acc一直为0
**注意**:develop和dygraph分支要求的Paddle版本、本地环境有差别,请注意不同分支环境安装部分的差异。
**A**: 识别模型训练初期acc为0是正常的,多训一段时间指标就上来了。
#### Q3.4.24:DB模型能正确推理预测,但换成EAST或SAST模型时报错或结果不正确
#### Q3.4.25 : PaddleOCR模型Python端预测和C++预测结果不一致?
**A**:正常来说,python端预测和C++预测文本是一致的,如果预测结果差异较大,建议首先排查diff出现在检测模型还是识别模型,或者尝试换其他模型是否有类似的问题。其次,检查python端和C++端数据处理部分是否存在差异,建议保存环境,更新PaddleOCR代码再试下。如果更新代码或者更新代码都没能解决,建议在PaddleOCR群里或者issue中抛出您的问题。
**A**:使用EAST或SAST模型进行推理预测时,需要在命令中指定参数--det_algorithm="EAST" 或 --det_algorithm="SAST",使用DB时不用指定是因为该参数默认值是"DB":https://github.com/PaddlePaddle/PaddleOCR/blob/e7a708e9fdaf413ed7a14da8e4a7b4ac0b211e42/tools/infer/utility.py#L43
<a name="OCR精选10个问题"></a>
## 【精选】OCR精选10个问题
......@@ -238,18 +251,15 @@
(2)调大系统的[l2 dcay值](https://github.com/PaddlePaddle/PaddleOCR/blob/a501603d54ff5513fc4fc760319472e59da25424/configs/rec/ch_ppocr_v1.1/rec_chinese_lite_train_v1.1.yml#L47)
<a name="预测部署2"></a>
### 预测部署
#### Q2.4.1:请问对于图片中的密集文字,有什么好的处理办法吗?
#### Q2.3.8:请问对于图片中的密集文字,有什么好的处理办法吗?
**A**:可以先试用预训练模型测试一下,例如DB+CRNN,判断下密集文字图片中是检测还是识别的问题,然后针对性的改善。还有一种是如果图象中密集文字较小,可以尝试增大图像分辨率,对图像进行一定范围内的拉伸,将文字稀疏化,提高识别效果。
#### Q2.4.2:对于一些在识别时稍微模糊的文本,有没有一些图像增强的方式?
#### Q2.3.9:对于一些在识别时稍微模糊的文本,有没有一些图像增强的方式?
**A**:在人类肉眼可以识别的前提下,可以考虑图像处理中的均值滤波、中值滤波或者高斯滤波等模糊算子尝试。也可以尝试从数据扩增扰动来强化模型鲁棒性,另外新的思路有对抗性训练和超分SR思路,可以尝试借鉴。但目前业界尚无普遍认可的最优方案,建议优先在数据采集阶段增加一些限制提升图片质量。
#### Q2.4.3:对于特定文字检测,例如身份证只检测姓名,检测指定区域文字更好,还是检测全部区域再筛选更好?
#### Q2.3.10:对于特定文字检测,例如身份证只检测姓名,检测指定区域文字更好,还是检测全部区域再筛选更好?
**A**:两个角度来说明一般检测全部区域再筛选更好。
......@@ -257,11 +267,11 @@
(2)产品的需求可能是变化的,不排除后续对于模型需求变化的可能性(比如又需要增加一个字段),相比于训练模型,后处理的逻辑会更容易调整。
#### Q2.4.4:对于小白如何快速入门中文OCR项目实践?
#### Q2.3.11:对于小白如何快速入门中文OCR项目实践?
**A**:建议可以先了解OCR方向的基础知识,大概了解基础的检测和识别模型算法。然后在Github上可以查看OCR方向相关的repo。目前来看,从内容的完备性来看,PaddleOCR的中英文双语教程文档是有明显优势的,在数据集、模型训练、预测部署文档详实,可以快速入手。而且还有微信用户群答疑,非常适合学习实践。项目地址:[PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)
#### Q2.4.5:如何识别带空格的英文行文本图像?
#### Q3.12:如何识别带空格的英文行文本图像?
**A**:空格识别可以考虑以下两种方案:
......@@ -269,22 +279,25 @@
(2)优化文本识别算法。在识别字典里面引入空格字符,然后在识别的训练数据中,如果用空行,进行标注。此外,合成数据时,通过拼接训练数据,生成含有空格的文本。
#### Q2.4.6:中英文一起识别时也可以加空格字符来训练吗
#### Q2.3.13:中英文一起识别时也可以加空格字符来训练吗
**A**:中文识别可以加空格当做分隔符训练,具体的效果如何没法给出直接评判,根据实际业务数据训练来判断。
#### Q2.4.7:低像素文字或者字号比较小的文字有什么超分辨率方法吗
#### Q2.3.14:低像素文字或者字号比较小的文字有什么超分辨率方法吗
**A**:超分辨率方法分为传统方法和基于深度学习的方法。基于深度学习的方法中,比较经典的有SRCNN,另外CVPR2020也有一篇超分辨率的工作可以参考文章:Unpaired Image Super-Resolution using Pseudo-Supervision,但是没有充分的实践验证过,需要看实际场景下的效果。
#### Q2.4.8:表格识别有什么好的模型 或者论文推荐么
#### Q2.3.15:表格识别有什么好的模型 或者论文推荐么
**A**:表格目前学术界比较成熟的解决方案不多 ,可以尝试下分割的论文方案。
#### Q2.4.9:弯曲文本有试过opencv的TPS进行弯曲校正吗?
#### Q2.3.16:弯曲文本有试过opencv的TPS进行弯曲校正吗?
**A**:opencv的tps需要标出上下边界对应的点,这个点很难通过传统方法或者深度学习方法获取。PaddleOCR里StarNet网络中的tps模块实现了自动学点,自动校正,可以直接尝试这个。
#### Q2.3.17: StyleText 合成数据效果不好?
**A**:StyleText模型生成的数据主要用于OCR识别模型的训练。PaddleOCR目前识别模型的输入为32 x N,因此当前版本模型主要适用高度为32的数据。
建议要合成的数据尺寸设置为32 x N。尺寸相差不多的数据也可以生成,尺寸很大或很小的数据效果确实不佳。
<a name="PaddleOCR实战问题"></a>
......@@ -392,6 +405,34 @@
**A**:动态图版本正在紧锣密鼓开发中,将于2020年12月16日发布,敬请关注。
#### Q3.1.22:ModuleNotFoundError: No module named 'paddle.nn',
**A**:paddle.nn是Paddle2.0版本特有的功能,请安装大于等于Paddle 2.0.0rc1的版本,安装方式为
```
python3 -m pip install paddlepaddle-gpu==2.0.0rc1 -i https://mirror.baidu.com/pypi/simple
```
#### Q3.1.23: ImportError: /usr/lib/x86_64_linux-gnu/libstdc++.so.6:version `CXXABI_1.3.11` not found (required by /usr/lib/python3.6/site-package/paddle/fluid/core+avx.so)
**A**:这个问题是glibc版本不足导致的,Paddle2.0rc1版本对gcc版本和glib版本有更高的要求,推荐gcc版本为8.2,glibc版本2.12以上。
如果您的环境不满足这个要求,或者使用的docker镜像为:
`hub.baidubce.com/paddlepaddle/paddle:latest-gpu-cuda9.0-cudnn7-dev`
`hub.baidubce.com/paddlepaddle/paddle:latest-gpu-cuda9.0-cudnn7-dev`,安装Paddle2.0rc版本可能会出现上述错误,2.0版本推荐使用新的docker镜像 `paddlepaddle/paddle:latest-dev-cuda10.1-cudnn7-gcc82`
或者访问[dockerhub](https://hub.docker.com/r/paddlepaddle/paddle/tags/)获得与您机器适配的镜像。
#### Q3.1.24: PaddleOCR develop分支和dygraph分支的区别?
**A** 目前PaddleOCR有四个分支,分别是:
- develop:基于Paddle静态图开发的分支,推荐使用paddle1.8 或者2.0版本,该分支具备完善的模型训练、预测、推理部署、量化裁剪等功能,领先于release/1.1分支。
- release/1.1:PaddleOCR 发布的第一个稳定版本,基于静态图开发,具备完善的训练、预测、推理部署、量化裁剪等功能。
- dygraph:基于Paddle动态图开发的分支,目前仍在开发中,未来将作为主要开发分支,运行要求使用Paddle2.0rc1版本,目前仍在开发中。
- release/2.0-rc1-0:PaddleOCR发布的第二个稳定版本,基于动态图和paddle2.0版本开发,动态图开发的工程更易于调试,目前支,支持模型训练、预测,暂不支持移动端部署。
如果您已经上手过PaddleOCR,并且希望在各种环境上部署PaddleOCR,目前建议使用静态图分支,develop或者release/1.1分支。如果您是初学者,想快速训练,调试PaddleOCR中的算法,建议尝鲜PaddleOCR dygraph分支。
**注意**:develop和dygraph分支要求的Paddle版本、本地环境有差别,请注意不同分支环境安装部分的差异。
<a name="数据集3"></a>
### 数据集
......@@ -729,3 +770,9 @@ ps -axu | grep train.py | awk '{print $2}' | xargs kill -9
#### Q3.4.24:DB模型能正确推理预测,但换成EAST或SAST模型时报错或结果不正确
**A**:使用EAST或SAST模型进行推理预测时,需要在命令中指定参数--det_algorithm="EAST" 或 --det_algorithm="SAST",使用DB时不用指定是因为该参数默认值是"DB":https://github.com/PaddlePaddle/PaddleOCR/blob/e7a708e9fdaf413ed7a14da8e4a7b4ac0b211e42/tools/infer/utility.py#L43
#### Q3.4.25 : PaddleOCR模型Python端预测和C++预测结果不一致?
正常来说,python端预测和C++预测文本是一致的,如果预测结果差异较大,
建议首先排查diff出现在检测模型还是识别模型,或者尝试换其他模型是否有类似的问题。
其次,检查python端和C++端数据处理部分是否存在差异,建议保存环境,更新PaddleOCR代码再试下。
如果更新代码或者更新代码都没能解决,建议在PaddleOCR微信群里或者issue中抛出您的问题。
......@@ -9,9 +9,9 @@
### 1.文本检测算法
PaddleOCR开源的文本检测算法列表:
- [x] DB([paper]( https://arxiv.org/abs/1911.08947) )(ppocr推荐)
- [x] EAST([paper](https://arxiv.org/abs/1704.03155))
- [x] SAST([paper](https://arxiv.org/abs/1908.05498))
- [x] DB([paper]( https://arxiv.org/abs/1911.08947)) [2](ppocr推荐)
- [x] EAST([paper](https://arxiv.org/abs/1704.03155))[1]
- [x] SAST([paper](https://arxiv.org/abs/1908.05498))[4]
在ICDAR2015文本检测公开数据集上,算法效果如下:
......@@ -38,13 +38,13 @@ PaddleOCR文本检测算法的训练和使用请参考文档教程中[模型训
### 2.文本识别算法
PaddleOCR基于动态图开源的文本识别算法列表:
- [x] CRNN([paper](https://arxiv.org/abs/1507.05717) )(ppocr推荐)
- [x] Rosetta([paper](https://arxiv.org/abs/1910.05085))
- [ ] STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html)) coming soon
- [ ] RARE([paper](https://arxiv.org/abs/1603.03915v1)) coming soon
- [ ] SRN([paper](https://arxiv.org/abs/2003.12294)) coming soon
- [x] CRNN([paper](https://arxiv.org/abs/1507.05717))[7](ppocr推荐)
- [x] Rosetta([paper](https://arxiv.org/abs/1910.05085))[10]
- [ ] STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html))[11] coming soon
- [ ] RARE([paper](https://arxiv.org/abs/1603.03915v1))[12] coming soon
- [ ] SRN([paper](https://arxiv.org/abs/2003.12294))[5] coming soon
参考[DTRB](https://arxiv.org/abs/1904.01906)文字识别训练和评估流程,使用MJSynth和SynthText两个文字识别数据集训练,在IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE数据集上进行评估,算法效果如下:
参考[DTRB][3](https://arxiv.org/abs/1904.01906)文字识别训练和评估流程,使用MJSynth和SynthText两个文字识别数据集训练,在IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE数据集上进行评估,算法效果如下:
|模型|骨干网络|Avg Accuracy|模型存储命名|下载链接|
|-|-|-|-|-|
......
......@@ -117,7 +117,7 @@ python3 tools/eval.py -c configs/cls/cls_mv3.yml -o Global.checkpoints={path/to/
```
# 预测分类结果
python3 tools/infer_cls.py -c configs/cls/cls_mv3.yml -o Global.checkpoints={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/ch/word_1.jpg
python3 tools/infer_cls.py -c configs/cls/cls_mv3.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.load_static_weights=false Global.infer_img=doc/imgs_words/ch/word_1.jpg
```
预测图片:
......
......@@ -120,16 +120,16 @@ python3 tools/eval.py -c configs/det/det_mv3_db.yml -o Global.checkpoints="{pat
测试单张图像的检测效果
```shell
python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/img_10.jpg" Global.checkpoints="./output/det_db/best_accuracy"
python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/img_10.jpg" Global.pretrained_model="./output/det_db/best_accuracy" Global.load_static_weights=false
```
测试DB模型时,调整后处理阈值,
```shell
python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/img_10.jpg" Global.checkpoints="./output/det_db/best_accuracy" PostProcess.box_thresh=0.6 PostProcess.unclip_ratio=1.5
python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/img_10.jpg" Global.pretrained_model="./output/det_db/best_accuracy" Global.load_static_weights=false PostProcess.box_thresh=0.6 PostProcess.unclip_ratio=1.5
```
测试文件夹下所有图像的检测效果
```shell
python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/" Global.checkpoints="./output/det_db/best_accuracy"
python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/" Global.pretrained_model="./output/det_db/best_accuracy" Global.load_static_weights=false
```
......@@ -245,7 +245,10 @@ python3 tools/infer/predict_det.py --det_algorithm="SAST" --image_dir="./doc/img
超轻量中文识别模型推理,可以执行如下命令:
```
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/ch/word_4.jpg" --rec_model_dir="./inference/rec_crnn/"
# 下载超轻量中文识别模型:
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar
tar xf ch_ppocr_mobile_v2.0_rec_infer.tar
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/ch/word_4.jpg" --rec_model_dir="ch_ppocr_mobile_v2.0_rec_infer"
```
![](../imgs_words/ch/word_4.jpg)
......@@ -266,7 +269,6 @@ Predicts of ./doc/imgs_words/ch/word_4.jpg:('实力活力', 0.98458153)
```
python3 tools/export_model.py -c configs/rec/rec_r34_vd_none_bilstm_ctc.yml -o Global.pretrained_model=./rec_r34_vd_none_bilstm_ctc_v2.0_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/rec_crnn
```
CRNN 文本识别模型推理,可以执行如下命令:
......@@ -327,7 +329,10 @@ Predicts of ./doc/imgs_words/korean/1.jpg:('바탕으로', 0.9948904)
方向分类模型推理,可以执行如下命令:
```
python3 tools/infer/predict_cls.py --image_dir="./doc/imgs_words/ch/word_4.jpg" --cls_model_dir="./inference/cls/"
# 下载超轻量中文方向分类器模型:
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar
tar xf ch_ppocr_mobile_v2.0_cls_infer.tar
python3 tools/infer/predict_cls.py --image_dir="./doc/imgs_words/ch/word_4.jpg" --cls_model_dir="ch_ppocr_mobile_v2.0_cls_infer"
```
![](../imgs_words/ch/word_1.jpg)
......
......@@ -324,7 +324,6 @@ Eval:
评估数据集可以通过 `configs/rec/rec_icdar15_train.yml` 修改Eval中的 `label_file_path` 设置。
*注意* 评估时必须确保配置文件中 infer_img 字段为空
```
# GPU 评估, Global.checkpoints 为待测权重
python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_icdar15_train.yml -o Global.checkpoints={path/to/weights}/best_accuracy
......@@ -342,7 +341,7 @@ python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec
```
# 预测英文结果
python3 tools/infer_rec.py -c configs/rec/rec_icdar15_train.yml -o Global.checkpoints={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/en/word_1.png
python3 tools/infer_rec.py -c configs/rec/rec_icdar15_train.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.load_static_weights=false Global.infer_img=doc/imgs_words/en/word_1.png
```
预测图片:
......@@ -361,7 +360,7 @@ infer_img: doc/imgs_words/en/word_1.png
```
# 预测中文结果
python3 tools/infer_rec.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.checkpoints={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/ch/word_1.jpg
python3 tools/infer_rec.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.load_static_weights=false Global.infer_img=doc/imgs_words/ch/word_1.jpg
```
预测图片:
......
......@@ -11,11 +11,12 @@
}
2. DB:
@article{liao2019real,
title={Real-time Scene Text Detection with Differentiable Binarization},
@inproceedings{liao2020real,
title={Real-Time Scene Text Detection with Differentiable Binarization.},
author={Liao, Minghui and Wan, Zhaoyi and Yao, Cong and Chen, Kai and Bai, Xiang},
journal={arXiv preprint arXiv:1911.08947},
year={2019}
booktitle={AAAI},
pages={11474--11481},
year={2020}
}
3. DTRB:
......@@ -37,10 +38,11 @@
}
5. SRN:
@article{yu2020towards,
title={Towards Accurate Scene Text Recognition with Semantic Reasoning Networks},
author={Yu, Deli and Li, Xuan and Zhang, Chengquan and Han, Junyu and Liu, Jingtuo and Ding, Errui},
journal={arXiv preprint arXiv:2003.12294},
@inproceedings{yu2020towards,
title={Towards accurate scene text recognition with semantic reasoning networks},
author={Yu, Deli and Li, Xuan and Zhang, Chengquan and Liu, Tao and Han, Junyu and Liu, Jingtuo and Ding, Errui},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={12113--12122},
year={2020}
}
......@@ -52,4 +54,62 @@
pages={9086--9095},
year={2019}
}
7. CRNN:
@article{shi2016end,
title={An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition},
author={Shi, Baoguang and Bai, Xiang and Yao, Cong},
journal={IEEE transactions on pattern analysis and machine intelligence},
volume={39},
number={11},
pages={2298--2304},
year={2016},
publisher={IEEE}
}
8. FPGM:
@inproceedings{he2019filter,
title={Filter pruning via geometric median for deep convolutional neural networks acceleration},
author={He, Yang and Liu, Ping and Wang, Ziwei and Hu, Zhilan and Yang, Yi},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={4340--4349},
year={2019}
}
9. PACT:
@article{choi2018pact,
title={Pact: Parameterized clipping activation for quantized neural networks},
author={Choi, Jungwook and Wang, Zhuo and Venkataramani, Swagath and Chuang, Pierce I-Jen and Srinivasan, Vijayalakshmi and Gopalakrishnan, Kailash},
journal={arXiv preprint arXiv:1805.06085},
year={2018}
}
10.Rosetta
@inproceedings{borisyuk2018rosetta,
title={Rosetta: Large scale system for text detection and recognition in images},
author={Borisyuk, Fedor and Gordo, Albert and Sivakumar, Viswanath},
booktitle={Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
pages={71--79},
year={2018}
}
11.STAR-Net
@inproceedings{liu2016star,
title={STAR-Net: A SpaTial Attention Residue Network for Scene Text Recognition.},
author={Liu, Wei and Chen, Chaofeng and Wong, Kwan-Yee K and Su, Zhizhong and Han, Junyu},
booktitle={BMVC},
volume={2},
pages={7},
year={2016}
}
12.RARE
@inproceedings{shi2016robust,
title={Robust scene text recognition with automatic rectification},
author={Shi, Baoguang and Wang, Xinggang and Lyu, Pengyuan and Yao, Cong and Bai, Xiang},
booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
pages={4168--4176},
year={2016}
}
```
......@@ -11,9 +11,9 @@ This tutorial lists the text detection algorithms and text recognition algorithm
### 1. Text Detection Algorithm
PaddleOCR open source text detection algorithms list:
- [x] EAST([paper](https://arxiv.org/abs/1704.03155))
- [x] DB([paper](https://arxiv.org/abs/1911.08947))
- [x] SAST([paper](https://arxiv.org/abs/1908.05498) )(Baidu Self-Research)
- [x] EAST([paper](https://arxiv.org/abs/1704.03155))[2]
- [x] DB([paper](https://arxiv.org/abs/1911.08947))[1]
- [x] SAST([paper](https://arxiv.org/abs/1908.05498))[4]
On the ICDAR2015 dataset, the text detection result is as follows:
......@@ -39,11 +39,11 @@ For the training guide and use of PaddleOCR text detection algorithms, please re
### 2. Text Recognition Algorithm
PaddleOCR open-source text recognition algorithms list:
- [x] CRNN([paper](https://arxiv.org/abs/1507.05717))
- [x] Rosetta([paper](https://arxiv.org/abs/1910.05085))
- [ ] STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html)) coming soon
- [ ] RARE([paper](https://arxiv.org/abs/1603.03915v1)) coming soon
- [ ] SRN([paper](https://arxiv.org/abs/2003.12294) )(Baidu Self-Research) coming soon
- [x] CRNN([paper](https://arxiv.org/abs/1507.05717))[7]
- [x] Rosetta([paper](https://arxiv.org/abs/1910.05085))[10]
- [ ] STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html))[11] coming soon
- [ ] RARE([paper](https://arxiv.org/abs/1603.03915v1))[12] coming soon
- [ ] SRN([paper](https://arxiv.org/abs/2003.12294))[5] coming soon
Refer to [DTRB](https://arxiv.org/abs/1904.01906), the training and evaluation result of these above text recognition (using MJSynth and SynthText for training, evaluate on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE) is as follow:
......
......@@ -119,7 +119,7 @@ Use `Global.infer_img` to specify the path of the predicted picture or folder, a
```
# Predict English results
python3 tools/infer_cls.py -c configs/cls/cls_mv3.yml -o Global.checkpoints={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words_en/word_10.png
python3 tools/infer_cls.py -c configs/cls/cls_mv3.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.load_static_weights=false Global.infer_img=doc/imgs_words_en/word_10.png
```
Input image:
......
......@@ -113,16 +113,16 @@ python3 tools/eval.py -c configs/det/det_mv3_db.yml -o Global.checkpoints="{pat
Test the detection result on a single image:
```shell
python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/img_10.jpg" Global.checkpoints="./output/det_db/best_accuracy"
python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/img_10.jpg" Global.pretrained_model="./output/det_db/best_accuracy" Global.load_static_weights=false
```
When testing the DB model, adjust the post-processing threshold:
```shell
python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/img_10.jpg" Global.checkpoints="./output/det_db/best_accuracy" PostProcess.box_thresh=0.6 PostProcess.unclip_ratio=1.5
python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/img_10.jpg" Global.pretrained_model="./output/det_db/best_accuracy" Global.load_static_weights=false PostProcess.box_thresh=0.6 PostProcess.unclip_ratio=1.5
```
Test the detection result on all images in the folder:
```shell
python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/" Global.checkpoints="./output/det_db/best_accuracy"
python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/" Global.pretrained_model="./output/det_db/best_accuracy" Global.load_static_weights=false
```
......@@ -255,15 +255,18 @@ The following will introduce the lightweight Chinese recognition model inference
For lightweight Chinese recognition model inference, you can execute the following commands:
```
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/ch/word_4.jpg" --rec_model_dir="./inference/rec_crnn/"
# download CRNN text recognition inference model
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar
tar xf ch_ppocr_mobile_v2.0_rec_infer.tar
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_10.png" --rec_model_dir="ch_ppocr_mobile_v2.0_rec_infer"
```
![](../imgs_words/ch/word_4.jpg)
![](../imgs_words_en/word_10.png)
After executing the command, the prediction results (recognized text and score) of the above image will be printed on the screen.
```bash
Predicts of ./doc/imgs_words/ch/word_4.jpg:('实力活力', 0.98458153)
Predicts of ./doc/imgs_words_en/word_10.png:('PAIN', 0.9897658)
```
<a name="CTC-BASED_RECOGNITION"></a>
......@@ -339,7 +342,12 @@ For angle classification model inference, you can execute the following commands
```
python3 tools/infer/predict_cls.py --image_dir="./doc/imgs_words_en/word_10.png" --cls_model_dir="./inference/cls/"
```
```
# download text angle class inference model:
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar
tar xf ch_ppocr_mobile_v2.0_cls_infer.tar
python3 tools/infer/predict_cls.py --image_dir="./doc/imgs_words_en/word_10.png" --cls_model_dir="ch_ppocr_mobile_v2.0_cls_infer"
```
![](../imgs_words_en/word_10.png)
After executing the command, the prediction results (classification angle and score) of the above image will be printed on the screen.
......
......@@ -317,11 +317,11 @@ Eval:
<a name="EVALUATION"></a>
### EVALUATION
The evaluation data set can be modified via `configs/rec/rec_icdar15_reader.yml` setting of `label_file_path` in EvalReader.
The evaluation dataset can be set by modifying the `Eval.dataset.label_file_list` field in the `configs/rec/rec_icdar15_train.yml` file.
```
# GPU evaluation, Global.checkpoints is the weight to be tested
python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_icdar15_reader.yml -o Global.checkpoints={path/to/weights}/best_accuracy
python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_icdar15_train.yml -o Global.checkpoints={path/to/weights}/best_accuracy
```
<a name="PREDICTION"></a>
......@@ -336,7 +336,7 @@ The default prediction picture is stored in `infer_img`, and the weight is speci
```
# Predict English results
python3 tools/infer_rec.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.checkpoints={path/to/weights}/best_accuracy TestReader.infer_img=doc/imgs_words/en/word_1.jpg
python3 tools/infer_rec.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.load_static_weights=false Global.infer_img=doc/imgs_words/en/word_1.jpg
```
Input image:
......@@ -354,7 +354,7 @@ The configuration file used for prediction must be consistent with the training.
```
# Predict Chinese results
python3 tools/infer_rec.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.checkpoints={path/to/weights}/best_accuracy TestReader.infer_img=doc/imgs_words/ch/word_1.jpg
python3 tools/infer_rec.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.load_static_weights=false Global.infer_img=doc/imgs_words/ch/word_1.jpg
```
Input image:
......
......@@ -262,8 +262,8 @@ class PaddleOCR(predict_system.TextSystem):
logger.error('rec_algorithm must in {}'.format(SUPPORT_REC_MODEL))
sys.exit(0)
postprocess_params.rec_char_dict_path = Path(
__file__).parent / postprocess_params.rec_char_dict_path
postprocess_params.rec_char_dict_path = str(
Path(__file__).parent / postprocess_params.rec_char_dict_path)
# init det_model and rec_model
super().__init__(postprocess_params)
......
......@@ -45,7 +45,6 @@ class BalanceLoss(nn.Layer):
self.balance_loss = balance_loss
self.main_loss_type = main_loss_type
self.negative_ratio = negative_ratio
self.main_loss_type = main_loss_type
self.return_origin = return_origin
self.eps = eps
......
......@@ -19,7 +19,6 @@ from __future__ import print_function
import paddle
from paddle import nn
from .det_basic_loss import DiceLoss
import paddle.fluid as fluid
import numpy as np
......@@ -27,9 +26,7 @@ class SASTLoss(nn.Layer):
"""
"""
def __init__(self,
eps=1e-6,
**kwargs):
def __init__(self, eps=1e-6, **kwargs):
super(SASTLoss, self).__init__()
self.dice_loss = DiceLoss(eps=eps)
......@@ -53,10 +50,12 @@ class SASTLoss(nn.Layer):
score_loss = 1.0 - 2 * intersection / (union + 1e-5)
#border loss
l_border_split, l_border_norm = paddle.split(l_border, num_or_sections=[4, 1], axis=1)
l_border_split, l_border_norm = paddle.split(
l_border, num_or_sections=[4, 1], axis=1)
f_border_split = f_border
border_ex_shape = l_border_norm.shape * np.array([1, 4, 1, 1])
l_border_norm_split = paddle.expand(x=l_border_norm, shape=border_ex_shape)
l_border_norm_split = paddle.expand(
x=l_border_norm, shape=border_ex_shape)
l_border_score = paddle.expand(x=l_score, shape=border_ex_shape)
l_border_mask = paddle.expand(x=l_mask, shape=border_ex_shape)
......@@ -72,7 +71,8 @@ class SASTLoss(nn.Layer):
(paddle.sum(l_border_score * l_border_mask) + 1e-5)
#tvo_loss
l_tvo_split, l_tvo_norm = paddle.split(l_tvo, num_or_sections=[8, 1], axis=1)
l_tvo_split, l_tvo_norm = paddle.split(
l_tvo, num_or_sections=[8, 1], axis=1)
f_tvo_split = f_tvo
tvo_ex_shape = l_tvo_norm.shape * np.array([1, 8, 1, 1])
l_tvo_norm_split = paddle.expand(x=l_tvo_norm, shape=tvo_ex_shape)
......@@ -91,7 +91,8 @@ class SASTLoss(nn.Layer):
(paddle.sum(l_tvo_score * l_tvo_mask) + 1e-5)
#tco_loss
l_tco_split, l_tco_norm = paddle.split(l_tco, num_or_sections=[2, 1], axis=1)
l_tco_split, l_tco_norm = paddle.split(
l_tco, num_or_sections=[2, 1], axis=1)
f_tco_split = f_tco
tco_ex_shape = l_tco_norm.shape * np.array([1, 2, 1, 1])
l_tco_norm_split = paddle.expand(x=l_tco_norm, shape=tco_ex_shape)
......@@ -109,7 +110,6 @@ class SASTLoss(nn.Layer):
tco_loss = paddle.sum(tco_out_loss * l_tco_score * l_tco_mask) / \
(paddle.sum(l_tco_score * l_tco_mask) + 1e-5)
# total loss
tvo_lw, tco_lw = 1.5, 1.5
score_lw, border_lw = 1.0, 1.0
......
......@@ -32,7 +32,7 @@ setup(
package_dir={'paddleocr': ''},
include_package_data=True,
entry_points={"console_scripts": ["paddleocr= paddleocr.paddleocr:main"]},
version='2.0.1',
version='2.0.2',
install_requires=requirements,
license='Apache License 2.0',
description='Awesome OCR toolkits based on PaddlePaddle (8.6M ultra-lightweight pre-trained model, support training and deployment among server, mobile, embeded and IoT devices',
......
......@@ -24,7 +24,6 @@ import numpy as np
import math
import time
import traceback
import paddle.fluid as fluid
import tools.infer.utility as utility
from ppocr.postprocess import build_post_process
......@@ -39,7 +38,6 @@ class TextClassifier(object):
self.cls_image_shape = [int(v) for v in args.cls_image_shape.split(",")]
self.cls_batch_num = args.cls_batch_num
self.cls_thresh = args.cls_thresh
self.use_zero_copy_run = args.use_zero_copy_run
postprocess_params = {
'name': 'ClsPostProcess',
"label_list": args.label_list,
......@@ -99,12 +97,8 @@ class TextClassifier(object):
norm_img_batch = norm_img_batch.copy()
starttime = time.time()
if self.use_zero_copy_run:
self.input_tensor.copy_from_cpu(norm_img_batch)
self.predictor.zero_copy_run()
else:
norm_img_batch = fluid.core.PaddleTensor(norm_img_batch)
self.predictor.run([norm_img_batch])
self.predictor.run()
prob_out = self.output_tensors[0].copy_to_cpu()
cls_result = self.postprocess_op(prob_out)
elapse += time.time() - starttime
......@@ -143,10 +137,11 @@ def main(args):
"Please set --rec_image_shape='3,32,100' and --rec_char_type='en' ")
exit()
for ino in range(len(img_list)):
logger.info("Predicts of {}:{}".format(valid_image_file_list[ino], cls_res[
ino]))
logger.info("Predicts of {}:{}".format(valid_image_file_list[ino],
cls_res[ino]))
logger.info("Total predict time for {} images, cost: {:.3f}".format(
len(img_list), predict_time))
if __name__ == "__main__":
main(utility.parse_args())
......@@ -22,7 +22,6 @@ import cv2
import numpy as np
import time
import sys
import paddle
import tools.infer.utility as utility
from ppocr.utils.logging import get_logger
......@@ -35,8 +34,8 @@ logger = get_logger()
class TextDetector(object):
def __init__(self, args):
self.args = args
self.det_algorithm = args.det_algorithm
self.use_zero_copy_run = args.use_zero_copy_run
pre_process_list = [{
'DetResizeForTest': {
'limit_side_len': args.det_limit_side_len,
......@@ -70,6 +69,11 @@ class TextDetector(object):
postprocess_params["cover_thresh"] = args.det_east_cover_thresh
postprocess_params["nms_thresh"] = args.det_east_nms_thresh
elif self.det_algorithm == "SAST":
pre_process_list[0] = {
'DetResizeForTest': {
'resize_long': args.det_limit_side_len
}
}
postprocess_params['name'] = 'SASTPostProcess'
postprocess_params["score_thresh"] = args.det_sast_score_thresh
postprocess_params["nms_thresh"] = args.det_sast_nms_thresh
......@@ -157,12 +161,8 @@ class TextDetector(object):
img = img.copy()
starttime = time.time()
if self.use_zero_copy_run:
self.input_tensor.copy_from_cpu(img)
self.predictor.zero_copy_run()
else:
im = paddle.fluid.core.PaddleTensor(img)
self.predictor.run([im])
self.predictor.run()
outputs = []
for output_tensor in self.output_tensors:
output = output_tensor.copy_to_cpu()
......
......@@ -23,7 +23,6 @@ import numpy as np
import math
import time
import traceback
import paddle.fluid as fluid
import tools.infer.utility as utility
from ppocr.postprocess import build_post_process
......@@ -39,7 +38,6 @@ class TextRecognizer(object):
self.character_type = args.rec_char_type
self.rec_batch_num = args.rec_batch_num
self.rec_algorithm = args.rec_algorithm
self.use_zero_copy_run = args.use_zero_copy_run
postprocess_params = {
'name': 'CTCLabelDecode',
"character_type": args.rec_char_type,
......@@ -101,12 +99,8 @@ class TextRecognizer(object):
norm_img_batch = np.concatenate(norm_img_batch)
norm_img_batch = norm_img_batch.copy()
starttime = time.time()
if self.use_zero_copy_run:
self.input_tensor.copy_from_cpu(norm_img_batch)
self.predictor.zero_copy_run()
else:
norm_img_batch = fluid.core.PaddleTensor(norm_img_batch)
self.predictor.run([norm_img_batch])
self.predictor.run()
outputs = []
for output_tensor in self.output_tensors:
output = output_tensor.copy_to_cpu()
......@@ -145,8 +139,8 @@ def main(args):
"Please set --rec_image_shape='3,32,100' and --rec_char_type='en' ")
exit()
for ino in range(len(img_list)):
logger.info("Predicts of {}:{}".format(valid_image_file_list[ino], rec_res[
ino]))
logger.info("Predicts of {}:{}".format(valid_image_file_list[ino],
rec_res[ino]))
logger.info("Total predict time for {} images, cost: {:.3f}".format(
len(img_list), predict_time))
......
......@@ -20,8 +20,7 @@ import numpy as np
import json
from PIL import Image, ImageDraw, ImageFont
import math
from paddle.fluid.core import AnalysisConfig
from paddle.fluid.core import create_paddle_predictor
from paddle import inference
def parse_args():
......@@ -33,6 +32,7 @@ def parse_args():
parser.add_argument("--use_gpu", type=str2bool, default=True)
parser.add_argument("--ir_optim", type=str2bool, default=True)
parser.add_argument("--use_tensorrt", type=str2bool, default=False)
parser.add_argument("--use_fp16", type=str2bool, default=False)
parser.add_argument("--gpu_mem", type=int, default=8000)
# params for text detector
......@@ -46,7 +46,7 @@ def parse_args():
parser.add_argument("--det_db_thresh", type=float, default=0.3)
parser.add_argument("--det_db_box_thresh", type=float, default=0.5)
parser.add_argument("--det_db_unclip_ratio", type=float, default=1.6)
parser.add_argument("--max_batch_size", type=int, default=10)
# EAST parmas
parser.add_argument("--det_east_score_thresh", type=float, default=0.8)
parser.add_argument("--det_east_cover_thresh", type=float, default=0.1)
......@@ -62,7 +62,7 @@ def parse_args():
parser.add_argument("--rec_model_dir", type=str)
parser.add_argument("--rec_image_shape", type=str, default="3, 32, 320")
parser.add_argument("--rec_char_type", type=str, default='ch')
parser.add_argument("--rec_batch_num", type=int, default=6)
parser.add_argument("--rec_batch_num", type=int, default=1)
parser.add_argument("--max_text_length", type=int, default=25)
parser.add_argument(
"--rec_char_dict_path",
......@@ -78,12 +78,10 @@ def parse_args():
parser.add_argument("--cls_model_dir", type=str)
parser.add_argument("--cls_image_shape", type=str, default="3, 48, 192")
parser.add_argument("--label_list", type=list, default=['0', '180'])
parser.add_argument("--cls_batch_num", type=int, default=30)
parser.add_argument("--cls_batch_num", type=int, default=6)
parser.add_argument("--cls_thresh", type=float, default=0.9)
parser.add_argument("--enable_mkldnn", type=str2bool, default=False)
parser.add_argument("--use_zero_copy_run", type=str2bool, default=False)
parser.add_argument("--use_pdserving", type=str2bool, default=False)
return parser.parse_args()
......@@ -109,10 +107,15 @@ def create_predictor(args, mode, logger):
logger.info("not find params file path {}".format(params_file_path))
sys.exit(0)
config = AnalysisConfig(model_file_path, params_file_path)
config = inference.Config(model_file_path, params_file_path)
if args.use_gpu:
config.enable_use_gpu(args.gpu_mem, 0)
if args.use_tensorrt:
config.enable_tensorrt_engine(
precision_mode=inference.PrecisionType.Half
if args.use_fp16 else inference.PrecisionType.Float32,
max_batch_size=args.max_batch_size)
else:
config.disable_gpu()
config.set_cpu_math_library_num_threads(6)
......@@ -124,20 +127,18 @@ def create_predictor(args, mode, logger):
# config.enable_memory_optim()
config.disable_glog_info()
if args.use_zero_copy_run:
config.delete_pass("conv_transpose_eltwiseadd_bn_fuse_pass")
config.switch_use_feed_fetch_ops(False)
else:
config.switch_use_feed_fetch_ops(True)
predictor = create_paddle_predictor(config)
# create predictor
predictor = inference.create_predictor(config)
input_names = predictor.get_input_names()
for name in input_names:
input_tensor = predictor.get_input_tensor(name)
input_tensor = predictor.get_input_handle(name)
output_names = predictor.get_output_names()
output_tensors = []
for output_name in output_names:
output_tensor = predictor.get_output_tensor(output_name)
output_tensor = predictor.get_output_handle(output_name)
output_tensors.append(output_tensor)
return predictor, input_tensor, output_tensors
......
......@@ -131,7 +131,7 @@ def check_gpu(use_gpu):
"model on CPU"
try:
if use_gpu and not paddle.fluid.is_compiled_with_cuda():
if use_gpu and not paddle.is_compiled_with_cuda():
print(err)
sys.exit(1)
except Exception as e:
......@@ -332,7 +332,7 @@ def eval(model, valid_dataloader, post_process_class, eval_class):
return metirc
def preprocess():
def preprocess(is_train=False):
FLAGS = ArgsParser().parse_args()
config = load_config(FLAGS.config)
merge_config(FLAGS.opt)
......@@ -350,15 +350,17 @@ def preprocess():
device = paddle.set_device(device)
config['Global']['distributed'] = dist.get_world_size() != 1
if is_train:
# save_config
save_model_dir = config['Global']['save_model_dir']
os.makedirs(save_model_dir, exist_ok=True)
with open(os.path.join(save_model_dir, 'config.yml'), 'w') as f:
yaml.dump(dict(config), f, default_flow_style=False, sort_keys=False)
logger = get_logger(
name='root', log_file='{}/train.log'.format(save_model_dir))
yaml.dump(
dict(config), f, default_flow_style=False, sort_keys=False)
log_file = '{}/train.log'.format(save_model_dir)
else:
log_file = None
logger = get_logger(name='root', log_file=log_file)
if config['Global']['use_visualdl']:
from visualdl import LogWriter
vdl_writer_path = '{}/vdl/'.format(save_model_dir)
......
......@@ -110,6 +110,6 @@ def test_reader(config, device, logger):
if __name__ == '__main__':
config, device, logger, vdl_writer = program.preprocess()
config, device, logger, vdl_writer = program.preprocess(is_train=True)
main(config, device, logger, vdl_writer)
# test_reader(config, device, logger)
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