Unverified Commit 7ed7d859 authored by andyjpaddle's avatar andyjpaddle Committed by GitHub
Browse files

Merge branch 'PaddlePaddle:release/2.5' into onnx

parents bd61fb5e d8a8ca81
......@@ -1149,7 +1149,10 @@ class MainWindow(QMainWindow):
for box in self.result_dic:
trans_dic = {"label": box[1][0], "points": box[0], "difficult": False}
if self.kie_mode:
trans_dic.update({"key_cls": "None"})
if len(box) == 3:
trans_dic.update({"key_cls": box[2]})
else:
trans_dic.update({"key_cls": "None"})
if trans_dic["label"] == "" and mode == 'Auto':
continue
shapes.append(trans_dic)
......@@ -2047,6 +2050,7 @@ class MainWindow(QMainWindow):
rec_flag = 0
for shape in self.canvas.shapes:
box = [[int(p.x()), int(p.y())] for p in shape.points]
kie_cls = shape.key_cls
if len(box) > 4:
box = self.gen_quad_from_poly(np.array(box))
......@@ -2062,17 +2066,27 @@ class MainWindow(QMainWindow):
if shape.line_color == DEFAULT_LOCK_COLOR:
shape.label = result[0][0]
result.insert(0, box)
if self.kie_mode:
result.append(kie_cls)
self.result_dic_locked.append(result)
else:
result.insert(0, box)
if self.kie_mode:
result.append(kie_cls)
self.result_dic.append(result)
else:
print('Can not recognise the box')
if shape.line_color == DEFAULT_LOCK_COLOR:
shape.label = result[0][0]
self.result_dic_locked.append([box, (self.noLabelText, 0)])
if self.kie_mode:
self.result_dic_locked.append([box, (self.noLabelText, 0), kie_cls])
else:
self.result_dic_locked.append([box, (self.noLabelText, 0)])
else:
self.result_dic.append([box, (self.noLabelText, 0)])
if self.kie_mode:
self.result_dic.append([box, (self.noLabelText, 0), kie_cls])
else:
self.result_dic.append([box, (self.noLabelText, 0)])
try:
if self.noLabelText == shape.label or result[1][0] == shape.label:
print('label no change')
......@@ -2322,13 +2336,6 @@ class MainWindow(QMainWindow):
else:
labeldict[file] = []
# if len(labeldict) != len(csv_paths):
# msg = 'ERROR, box label and excel label are not in the same number\n' + \
# 'box label: ' + str(len(labeldict)) + '\n' + \
# 'excel label: ' + str(len(csv_paths)) + '\n' + \
# 'Please check the label.txt and tableRec_excel_output\n'
# QMessageBox.information(self, "Information", msg)
# return
train_split, val_split, test_split = partitionDialog.getDataPartition()
# check validate
if train_split + val_split + test_split > 100:
......@@ -2351,14 +2358,8 @@ class MainWindow(QMainWindow):
filename, _ = os.path.splitext(os.path.basename(image_path))
csv_path = os.path.join(TableRec_excel_dir, filename + '.xlsx')
if not os.path.exists(csv_path):
msg = 'ERROR, Can not find ' + csv_path
QMessageBox.information(self, "Information", msg)
return
continue
# read xlsx file, convert to HTML
# xd = pd.ExcelFile(csv_path)
# df = xd.parse()
# structure = df.to_html(index = False)
excel = xlrd.open_workbook(csv_path)
sheet0 = excel.sheet_by_index(0) # only sheet 0
merged_cells = sheet0.merged_cells # (0,1,1,3) start row, end row, start col, end col
......@@ -2370,7 +2371,6 @@ class MainWindow(QMainWindow):
token_list = convert_token(html_list)
# load box annotations
cells = []
for anno in labeldict[image_path]:
......
......@@ -206,7 +206,7 @@ the table and pop up Excel at the same time.
- Model language switching: Changing the built-in model language is supportable by clicking "PaddleOCR"-"Choose OCR Model" in the menu bar. Currently supported languages​include French, German, Korean, and Japanese.
For specific model download links, please refer to [PaddleOCR Model List](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_en/models_list_en.md#multilingual-recognition-modelupdating)
- **Custom Model**: If users want to replace the built-in model with their own inference model, they can follow the [Custom Model Code Usage](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/doc/doc_en/whl_en.md#31-use-by-code) by modifying PPOCRLabel.py for [Instantiation of PaddleOCR class](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/PPOCRLabel/PPOCRLabel.py#L86) :
- **Custom Model**: If users want to replace the built-in model with their own inference model, they can follow the [Custom Model Code Usage](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/doc/doc_en/whl_en.md#31-use-by-code) by modifying PPOCRLabel.py for [Instantiation of PaddleOCR class](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.5/PPOCRLabel/PPOCRLabel.py#L97) :
add parameter `det_model_dir` in `self.ocr = PaddleOCR(use_pdserving=False, use_angle_cls=True, det=True, cls=True, use_gpu=gpu, lang=lang) `
......
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......@@ -221,10 +221,10 @@ def convert_token(html_list):
token_list.append("<td")
if 'colspan' in col:
_, n = col.split('colspan=')
token_list.append(" colspan=\"{}\"".format(n))
token_list.append(" colspan=\"{}\"".format(n[0]))
if 'rowspan' in col:
_, n = col.split('rowspan=')
token_list.append(" rowspan=\"{}\"".format(n))
token_list.append(" rowspan=\"{}\"".format(n[0]))
token_list.extend([">", "</td>"])
token_list.append("</tr>")
token_list.append("</tbody>")
......
......@@ -112,4 +112,4 @@ keyDialogTip=Enter object label
keyChange=Change Box Key
TableRecognition=Table Recognition
cellreRecognition=Cell Re-Recognition
exportJSON=export JSON(PubTabNet)
exportJSON=Export Excel Label(PubTabNet)
......@@ -84,7 +84,7 @@ mhelp=帮助
iconList=缩略图
detectionBoxposition=检测框位置
recognitionResult=识别结果
creatPolygon=边形标注
creatPolygon=标注
drawSquares=正方形标注
rotateLeft=图片左旋转90度
rotateRight=图片右旋转90度
......
......@@ -163,43 +163,41 @@ The recognition model is the same.
The predicted performance data will be automatically written into the `PipelineServingLogs/pipeline.tracer` file.
Tested on 200 real pictures, and limited the detection long side to 960. The average QPS on T4 GPU can reach around 23:
Tested on 200 real pictures, and limited the detection long side to 960. The average QPS on T4 GPU can reach around 62.0:
```
2021-05-13 03:42:36,895 ==================== TRACER ======================
2021-05-13 03:42:36,975 Op(rec):
2021-05-13 03:42:36,976 in[14.472382882882883 ms]
2021-05-13 03:42:36,976 prep[9.556855855855856 ms]
2021-05-13 03:42:36,976 midp[59.921905405405404 ms]
2021-05-13 03:42:36,976 postp[15.345945945945946 ms]
2021-05-13 03:42:36,976 out[1.9921216216216215 ms]
2021-05-13 03:42:36,976 idle[0.16254943864471572]
2021-05-13 03:42:36,976 Op(det):
2021-05-13 03:42:36,976 in[315.4468035714286 ms]
2021-05-13 03:42:36,976 prep[69.5980625 ms]
2021-05-13 03:42:36,976 midp[18.989535714285715 ms]
2021-05-13 03:42:36,976 postp[18.857803571428573 ms]
2021-05-13 03:42:36,977 out[3.1337544642857145 ms]
2021-05-13 03:42:36,977 idle[0.7477961159203756]
2021-05-13 03:42:36,977 DAGExecutor:
2021-05-13 03:42:36,977 Query count[224]
2021-05-13 03:42:36,977 QPS[22.4 q/s]
2021-05-13 03:42:36,977 Succ[0.9910714285714286]
2021-05-13 03:42:36,977 Error req[169, 170]
2021-05-13 03:42:36,977 Latency:
2021-05-13 03:42:36,977 ave[535.1678348214285 ms]
2021-05-13 03:42:36,977 .50[172.651 ms]
2021-05-13 03:42:36,977 .60[187.904 ms]
2021-05-13 03:42:36,977 .70[245.675 ms]
2021-05-13 03:42:36,977 .80[526.684 ms]
2021-05-13 03:42:36,977 .90[854.596 ms]
2021-05-13 03:42:36,977 .95[1722.728 ms]
2021-05-13 03:42:36,977 .99[3990.292 ms]
2021-05-13 03:42:36,978 Channel (server worker num[10]):
2021-05-13 03:42:36,978 chl0(In: ['@DAGExecutor'], Out: ['det']) size[0/0]
2021-05-13 03:42:36,979 chl1(In: ['det'], Out: ['rec']) size[6/0]
2021-05-13 03:42:36,979 chl2(In: ['rec'], Out: ['@DAGExecutor']) size[0/0]
2022-05-12 03:56:46,461 ==================== TRACER ======================
2022-05-12 03:56:46,860 Op(det):
2022-05-12 03:56:46,860 in[80.32286641221374 ms]
2022-05-12 03:56:46,860 prep[74.27364885496183 ms]
2022-05-12 03:56:46,860 midp[33.41587786259542 ms]
2022-05-12 03:56:46,860 postp[20.935980916030534 ms]
2022-05-12 03:56:46,860 out[1.551145038167939 ms]
2022-05-12 03:56:46,860 idle[0.3889510617728378]
2022-05-12 03:56:46,860 Op(rec):
2022-05-12 03:56:46,860 in[15.46498846153846 ms]
2022-05-12 03:56:46,861 prep[22.565715384615384 ms]
2022-05-12 03:56:46,861 midp[91.42518076923076 ms]
2022-05-12 03:56:46,861 postp[11.678453846153847 ms]
2022-05-12 03:56:46,861 out[1.1200576923076924 ms]
2022-05-12 03:56:46,861 idle[0.11658723106110291]
2022-05-12 03:56:46,862 DAGExecutor:
2022-05-12 03:56:46,862 Query count[620]
2022-05-12 03:56:46,862 QPS[62.0 q/s]
2022-05-12 03:56:46,862 Succ[0.4193548387096774]
2022-05-12 03:56:46,862 Latency:
2022-05-12 03:56:46,863 ave[165.54603709677417 ms]
2022-05-12 03:56:46,863 .50[77.863 ms]
2022-05-12 03:56:46,863 .60[158.414 ms]
2022-05-12 03:56:46,863 .70[237.28 ms]
2022-05-12 03:56:46,863 .80[316.022 ms]
2022-05-12 03:56:46,863 .90[424.416 ms]
2022-05-12 03:56:46,863 .95[515.566 ms]
2022-05-12 03:56:46,863 .99[762.256 ms]
2022-05-12 03:56:46,863 Channel (server worker num[10]):
2022-05-12 03:56:46,864 chl0(In: ['@DAGExecutor'], Out: ['det']) size[0/0]
2022-05-12 03:56:46,864 chl1(In: ['det'], Out: ['rec']) size[2/0]
2022-05-12 03:56:46,865 chl2(In: ['rec'], Out: ['@DAGExecutor']) size[0/0]
```
<a name="C++"></a>
......
......@@ -162,42 +162,41 @@ python3 -m paddle_serving_client.convert --dirname ./ch_PP-OCRv3_rec_infer/ \
预测性能数据会被自动写入 `PipelineServingLogs/pipeline.tracer` 文件中。
在200张真实图片上测试,把检测长边限制为960。T4 GPU 上 QPS 均值可达到23左右:
在200张真实图片上测试,把检测长边限制为960。T4 GPU 上 QPS 均值可达到62左右:
```
2021-05-13 03:42:36,895 ==================== TRACER ======================
2021-05-13 03:42:36,975 Op(rec):
2021-05-13 03:42:36,976 in[14.472382882882883 ms]
2021-05-13 03:42:36,976 prep[9.556855855855856 ms]
2021-05-13 03:42:36,976 midp[59.921905405405404 ms]
2021-05-13 03:42:36,976 postp[15.345945945945946 ms]
2021-05-13 03:42:36,976 out[1.9921216216216215 ms]
2021-05-13 03:42:36,976 idle[0.16254943864471572]
2021-05-13 03:42:36,976 Op(det):
2021-05-13 03:42:36,976 in[315.4468035714286 ms]
2021-05-13 03:42:36,976 prep[69.5980625 ms]
2021-05-13 03:42:36,976 midp[18.989535714285715 ms]
2021-05-13 03:42:36,976 postp[18.857803571428573 ms]
2021-05-13 03:42:36,977 out[3.1337544642857145 ms]
2021-05-13 03:42:36,977 idle[0.7477961159203756]
2021-05-13 03:42:36,977 DAGExecutor:
2021-05-13 03:42:36,977 Query count[224]
2021-05-13 03:42:36,977 QPS[22.4 q/s]
2021-05-13 03:42:36,977 Succ[0.9910714285714286]
2021-05-13 03:42:36,977 Error req[169, 170]
2021-05-13 03:42:36,977 Latency:
2021-05-13 03:42:36,977 ave[535.1678348214285 ms]
2021-05-13 03:42:36,977 .50[172.651 ms]
2021-05-13 03:42:36,977 .60[187.904 ms]
2021-05-13 03:42:36,977 .70[245.675 ms]
2021-05-13 03:42:36,977 .80[526.684 ms]
2021-05-13 03:42:36,977 .90[854.596 ms]
2021-05-13 03:42:36,977 .95[1722.728 ms]
2021-05-13 03:42:36,977 .99[3990.292 ms]
2021-05-13 03:42:36,978 Channel (server worker num[10]):
2021-05-13 03:42:36,978 chl0(In: ['@DAGExecutor'], Out: ['det']) size[0/0]
2021-05-13 03:42:36,979 chl1(In: ['det'], Out: ['rec']) size[6/0]
2021-05-13 03:42:36,979 chl2(In: ['rec'], Out: ['@DAGExecutor']) size[0/0]
2022-05-12 03:56:46,461 ==================== TRACER ======================
2022-05-12 03:56:46,860 Op(det):
2022-05-12 03:56:46,860 in[80.32286641221374 ms]
2022-05-12 03:56:46,860 prep[74.27364885496183 ms]
2022-05-12 03:56:46,860 midp[33.41587786259542 ms]
2022-05-12 03:56:46,860 postp[20.935980916030534 ms]
2022-05-12 03:56:46,860 out[1.551145038167939 ms]
2022-05-12 03:56:46,860 idle[0.3889510617728378]
2022-05-12 03:56:46,860 Op(rec):
2022-05-12 03:56:46,860 in[15.46498846153846 ms]
2022-05-12 03:56:46,861 prep[22.565715384615384 ms]
2022-05-12 03:56:46,861 midp[91.42518076923076 ms]
2022-05-12 03:56:46,861 postp[11.678453846153847 ms]
2022-05-12 03:56:46,861 out[1.1200576923076924 ms]
2022-05-12 03:56:46,861 idle[0.11658723106110291]
2022-05-12 03:56:46,862 DAGExecutor:
2022-05-12 03:56:46,862 Query count[620]
2022-05-12 03:56:46,862 QPS[62.0 q/s]
2022-05-12 03:56:46,862 Succ[0.4193548387096774]
2022-05-12 03:56:46,862 Latency:
2022-05-12 03:56:46,863 ave[165.54603709677417 ms]
2022-05-12 03:56:46,863 .50[77.863 ms]
2022-05-12 03:56:46,863 .60[158.414 ms]
2022-05-12 03:56:46,863 .70[237.28 ms]
2022-05-12 03:56:46,863 .80[316.022 ms]
2022-05-12 03:56:46,863 .90[424.416 ms]
2022-05-12 03:56:46,863 .95[515.566 ms]
2022-05-12 03:56:46,863 .99[762.256 ms]
2022-05-12 03:56:46,863 Channel (server worker num[10]):
2022-05-12 03:56:46,864 chl0(In: ['@DAGExecutor'], Out: ['det']) size[0/0]
2022-05-12 03:56:46,864 chl1(In: ['det'], Out: ['rec']) size[2/0]
2022-05-12 03:56:46,865 chl2(In: ['rec'], Out: ['@DAGExecutor']) size[0/0]
```
<a name="C++"></a>
......
......@@ -293,8 +293,8 @@ Loss:
以上述配置为例,最终蒸馏训练的损失函数包含下面5个部分。
- `Student``Teacher`最终输出(`head_out`)的CTC分支与gt的CTC loss,权重为1。在这里因为2个子网络都需要更新参数,因此2者都需要计算与g的loss。
- `Student``Teacher`最终输出(`head_out`)的SAR分支与gt的SAR loss,权重为1.0。在这里因为2个子网络都需要更新参数,因此2者都需要计算与g的loss。
- `Student``Teacher`最终输出(`head_out`)的CTC分支与gt的CTC loss,权重为1。在这里因为2个子网络都需要更新参数,因此2者都需要计算与gt的loss。
- `Student``Teacher`最终输出(`head_out`)的SAR分支与gt的SAR loss,权重为1.0。在这里因为2个子网络都需要更新参数,因此2者都需要计算与gt的loss。
- `Student``Teacher`最终输出(`head_out`)的CTC分支之间的DML loss,权重为1。
- `Student``Teacher`最终输出(`head_out`)的SAR分支之间的DML loss,权重为0.5。
- `Student``Teacher`的骨干网络输出(`backbone_out`)之间的l2 loss,权重为1。
......@@ -374,7 +374,7 @@ paddle.save(s_params, "ch_PP-OCRv3_rec_train/student.pdparams")
<a name="22"></a>
### 2.2 检测配置文件解析
检测模型蒸馏的配置文件在PaddleOCR/configs/det/ch_PP-OCRv3/目录下,包含两个蒸馏配置文件:
检测模型蒸馏的配置文件在PaddleOCR/configs/det/ch_PP-OCRv3/目录下,包含两个蒸馏配置文件:
- ch_PP-OCRv3_det_cml.yml,采用cml蒸馏,采用一个大模型蒸馏两个小模型,且两个小模型互相学习的方法
- ch_PP-OCRv3_det_dml.yml,采用DML的蒸馏,两个Student模型互蒸馏的方法
......@@ -383,7 +383,7 @@ paddle.save(s_params, "ch_PP-OCRv3_rec_train/student.pdparams")
知识蒸馏任务中,模型结构配置如下所示:
```
```yaml
Architecture:
name: DistillationModel # 结构名称,蒸馏任务中,为DistillationModel,用于构建对应的结构
algorithm: Distillation # 算法名称
......@@ -424,11 +424,11 @@ Architecture:
```
如果是采用DML,即两个小模型互相学习的方法,上述配置文件里的Teacher网络结构需要设置为Student模型一样的配置,具体参考配置文件[ch_PP-OCRv3_det_dml.yml](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.4/configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_dml.yml)

下面介绍[ch_PP-OCRv3_det_cml.yml](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.4/configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml)的配置文件参数:
如果是采用DML,即两个小模型互相学习的方法,上述配置文件里的Teacher网络结构需要设置为Student模型一样的配置,具体参考配置文件[ch_PP-OCRv3_det_dml.yml](../../configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_dml.yml)
```
下面介绍[ch_PP-OCRv3_det_cml.yml](../../configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml)的配置文件参数:
```yaml
Architecture:
name: DistillationModel
algorithm: Distillation
......
......@@ -22,7 +22,7 @@ PaddleOCR提供的可下载模型包括`推理模型`、`训练模型`、`预训
|模型类型|模型格式|简介|
|--- | --- | --- |
|推理模型|inference.pdmodel、inference.pdiparams|用于预测引擎推理,[详情](./inference.md)|
|推理模型|inference.pdmodel、inference.pdiparams|用于预测引擎推理,[详情](./inference_ppocr.md)|
|训练模型、预训练模型|\*.pdparams、\*.pdopt、\*.states |训练过程中保存的模型的参数、优化器状态和训练中间信息,多用于模型指标评估和恢复训练|
|nb模型|\*.nb|经过飞桨Paddle-Lite工具优化后的模型,适用于移动端/IoT端等端侧部署场景(需使用飞桨Paddle Lite部署)。|
......@@ -114,7 +114,7 @@ PaddleOCR提供的可下载模型包括`推理模型`、`训练模型`、`预训
| ka_PP-OCRv3_rec | ppocr/utils/dict/ka_dict.txt |卡纳达文识别|[ka_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/multi_language/ka_PP-OCRv3_rec.yml)|9.9M|[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/ka_PP-OCRv3_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/ka_PP-OCRv3_rec_train.tar) |
| ta_PP-OCRv3_rec | ppocr/utils/dict/ta_dict.txt |泰米尔文识别|[ta_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/multi_language/ta_PP-OCRv3_rec.yml)|9.6M|[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/ta_PP-OCRv3_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/ta_PP-OCRv3_rec_train.tar) |
| latin_PP-OCRv3_rec | ppocr/utils/dict/latin_dict.txt | 拉丁文识别 | [latin_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/multi_language/latin_PP-OCRv3_rec.yml) |9.7M|[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/latin_PP-OCRv3_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/latin_PP-OCRv3_rec_train.tar) |
| arabic_PP-OCRv3_rec | ppocr/utils/dict/arabic_dict.txt | 阿拉伯字母 | [arabic_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/multi_language/rec_arabic_lite_train.yml) |9.6M|[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/arabic_PP-OCRv3_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/arabic_PP-OCRv3_rec_train.tar) |
| arabic_PP-OCRv3_rec | ppocr/utils/dict/arabic_dict.txt | 阿拉伯字母 | [arabic_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/multi_language/arabic_PP-OCRv3_rec.yml) |9.6M|[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/arabic_PP-OCRv3_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/arabic_PP-OCRv3_rec_train.tar) |
| cyrillic_PP-OCRv3_rec | ppocr/utils/dict/cyrillic_dict.txt | 斯拉夫字母 | [cyrillic_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/multi_language/cyrillic_PP-OCRv3_rec.yml) |9.6M|[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/cyrillic_PP-OCRv3_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/cyrillic_PP-OCRv3_rec_train.tar) |
| devanagari_PP-OCRv3_rec | ppocr/utils/dict/devanagari_dict.txt |梵文字母 | [devanagari_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/multi_language/devanagari_PP-OCRv3_rec.yml) |9.9M|[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/devanagari_PP-OCRv3_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/devanagari_PP-OCRv3_rec_train.tar) |
......
......@@ -238,10 +238,10 @@ python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs
## 4 预测部署
除了安装whl包进行快速预测,ppocr 也提供了多种预测部署方式,如有需求可阅读相关文档:
- [基于Python脚本预测引擎推理](./inference.md)
- [基于C++预测引擎推理](../../deploy/cpp_infer/readme.md)
- [基于Python脚本预测引擎推理](./inference_ppocr.md)
- [基于C++预测引擎推理](../../deploy/cpp_infer/readme_ch.md)
- [服务化部署](../../deploy/hubserving/readme.md)
- [端侧部署](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/deploy/lite/readme.md)
- [端侧部署](../../deploy/lite/readme_ch.md)
- [Benchmark](./benchmark.md)
......
# 使用Paddle Serving预测推理
阅读本文档之前,请先阅读文档 [基于Python预测引擎推理](./inference.md)
同本地执行预测一样,我们需要保存一份可以用于Paddle Serving的模型。
接下来首先介绍如何将训练的模型转换成Paddle Serving模型,然后将依次介绍文本检测、文本识别以及两者串联基于预测引擎推理。
### 一、 准备环境
我们先安装Paddle Serving相关组件
我们推荐用户使用GPU来做Paddle Serving的OCR服务部署
**CUDA版本:9.X/10.X**
**CUDNN版本:7.X**
**操作系统版本:Linux/Windows**
**Python版本: 2.7/3.5/3.6/3.7**
**Python操作指南:**
目前Serving用于OCR的部分功能还在测试当中,因此在这里我们给出[Servnig latest package](https://github.com/PaddlePaddle/Serving/blob/develop/doc/Latest_Packages_CN.md)
大家根据自己的环境选择需要安装的whl包即可,例如以Python 3.5为例,执行下列命令
```
#CPU/GPU版本选择一个
#GPU版本服务端
#CUDA 9
python -m pip install -U https://paddle-serving.bj.bcebos.com/whl/paddle_serving_server_gpu-0.0.0.post9-py3-none-any.whl
#CUDA 10
python -m pip install -U https://paddle-serving.bj.bcebos.com/whl/paddle_serving_server_gpu-0.0.0.post10-py3-none-any.whl
#CPU版本服务端
python -m pip install -U https://paddle-serving.bj.bcebos.com/whl/paddle_serving_server-0.0.0-py3-none-any.whl
#客户端和App包使用以下链接(CPU,GPU通用)
python -m pip install -U https://paddle-serving.bj.bcebos.com/whl/paddle_serving_client-0.0.0-cp36-none-any.whl https://paddle-serving.bj.bcebos.com/whl/paddle_serving_app-0.0.0-py3-none-any.whl
```
## 二、训练模型转Serving模型
在前序文档 [基于Python预测引擎推理](./inference.md) 中,我们提供了如何把训练的checkpoint转换成Paddle模型。Paddle模型通常由一个文件夹构成,内含模型结构描述文件`model`和模型参数文件`params`。Serving模型由两个文件夹构成,用于存放客户端和服务端的配置。
我们以`ch_rec_r34_vd_crnn`模型作为例子,下载链接在:
```
wget --no-check-certificate https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_infer.tar
tar xf ch_rec_r34_vd_crnn_infer.tar
```
因此我们按照Serving模型转换教程,运行下列python文件。
```
python tools/inference_to_serving.py --model_dir ch_rec_r34_vd_crnn
```
最终会在`serving_client_dir``serving_server_dir`生成客户端和服务端的模型配置。其中`serving_server_dir``serving_client_dir`的名字可以自定义。最终文件结构如下
```
/ch_rec_r34_vd_crnn/
├── serving_client_dir # 客户端配置文件夹
└── serving_server_dir # 服务端配置文件夹
```
## 三、文本检测模型Serving推理
启动服务可以根据实际需求选择启动`标准版`或者`快速版`,两种方式的对比如下表:
|版本|特点|适用场景|
|-|-|-|
|标准版|稳定性高,分布式部署|适用于吞吐量大,需要跨机房部署的情况|
|快速版|部署方便,预测速度快|适用于对预测速度要求高,迭代速度快的场景,Windows用户只能选择快速版|
接下来的命令中,我们会指定快速版和标准版的命令。需要说明的是,标准版只能用Linux平台,快速版可以支持Linux/Windows。
文本检测模型推理,默认使用DB模型的配置参数,识别默认为CRNN。
配置文件在`params.py`中,我们贴出配置部分,如果需要做改动,也在这个文件内部进行修改。
```
def read_params():
cfg = Config()
#use gpu
cfg.use_gpu = False # 是否使用GPU
cfg.use_pdserving = True # 是否使用paddleserving,必须为True
#params for text detector
cfg.det_algorithm = "DB" # 检测算法, DB/EAST等
cfg.det_model_dir = "./det_mv_server/" # 检测算法模型路径
cfg.det_max_side_len = 960
#DB params
cfg.det_db_thresh =0.3
cfg.det_db_box_thresh =0.5
cfg.det_db_unclip_ratio =2.0
#EAST params
cfg.det_east_score_thresh = 0.8
cfg.det_east_cover_thresh = 0.1
cfg.det_east_nms_thresh = 0.2
#params for text recognizer
cfg.rec_algorithm = "CRNN" # 识别算法, CRNN/RARE等
cfg.rec_model_dir = "./ocr_rec_server/" # 识别算法模型路径
cfg.rec_image_shape = "3, 32, 320"
cfg.rec_batch_num = 30
cfg.max_text_length = 25
cfg.rec_char_dict_path = "./ppocr_keys_v1.txt" # 识别算法字典文件
cfg.use_space_char = True
#params for text classifier
cfg.use_angle_cls = True # 是否启用分类算法
cfg.cls_model_dir = "./ocr_clas_server/" # 分类算法模型路径
cfg.cls_image_shape = "3, 48, 192"
cfg.label_list = ['0', '180']
cfg.cls_batch_num = 30
cfg.cls_thresh = 0.9
return cfg
```
与本地预测不同的是,Serving预测需要一个客户端和一个服务端,因此接下来的教程都是两行代码。
在正式执行服务端启动命令之前,先export PYTHONPATH到工程主目录下。
```
export PYTHONPATH=$PWD:$PYTHONPATH
cd deploy/pdserving
```
为了方便用户复现Demo程序,我们提供了Chinese and English ultra-lightweight OCR model (8.1M)版本的Serving模型
```
wget --no-check-certificate https://paddleocr.bj.bcebos.com/deploy/pdserving/ocr_pdserving_suite.tar.gz
tar xf ocr_pdserving_suite.tar.gz
```
### 1. 超轻量中文检测模型推理
超轻量中文检测模型推理,可以执行如下命令启动服务端:
```
#根据环境只需要启动其中一个就可以
python det_rpc_server.py #标准版,Linux用户
python det_local_server.py #快速版,Windows/Linux用户
```
客户端
```
python det_web_client.py
```
Serving的推测和本地预测不同点在于,客户端发送请求到服务端,服务端需要检测到文字框之后返回框的坐标,此处没有后处理的图片,只能看到坐标值。
## 四、文本识别模型Serving推理
下面将介绍超轻量中文识别模型推理、基于CTC损失的识别模型推理和基于Attention损失的识别模型推理。对于中文文本识别,建议优先选择基于CTC损失的识别模型,实践中也发现基于Attention损失的效果不如基于CTC损失的识别模型。此外,如果训练时修改了文本的字典,请参考下面的自定义文本识别字典的推理。
### 1. 超轻量中文识别模型推理
超轻量中文识别模型推理,可以执行如下命令启动服务端:
需要注意params.py中的`--use_gpu`的值
```
#根据环境只需要启动其中一个就可以
python rec_rpc_server.py #标准版,Linux用户
python rec_local_server.py #快速版,Windows/Linux用户
```
如果需要使用CPU版本,还需增加 `--use_gpu False`
客户端
```
python rec_web_client.py
```
![](../imgs_words/ch/word_4.jpg)
执行命令后,上面图像的预测结果(识别的文本和得分)会打印到屏幕上,示例如下:
```
{u'result': {u'score': [u'0.89547354'], u'pred_text': ['实力活力']}}
```
## 五、方向分类模型推理
下面将介绍方向分类模型推理。
### 1. 方向分类模型推理
方向分类模型推理, 可以执行如下命令启动服务端:
需要注意params.py中的`--use_gpu`的值
```
#根据环境只需要启动其中一个就可以
python clas_rpc_server.py #标准版,Linux用户
python clas_local_server.py #快速版,Windows/Linux用户
```
客户端
```
python rec_web_client.py
```
![](../imgs_words/ch/word_4.jpg)
执行命令后,上面图像的预测结果(分类的方向和得分)会打印到屏幕上,示例如下:
```
{u'result': {u'direction': [u'0'], u'score': [u'0.9999963']}}
```
## 六、文本检测、方向分类和文字识别串联Serving推理
### 1. 超轻量中文OCR模型推理
在执行预测时,需要通过参数`image_dir`指定单张图像或者图像集合的路径、参数`det_model_dir`,`cls_model_dir``rec_model_dir`分别指定检测,方向分类和识别的inference模型路径。参数`use_angle_cls`用于控制是否启用方向分类模型。与本地预测不同的是,为了减少网络传输耗时,可视化识别结果目前不做处理,用户收到的是推理得到的文字字段。
执行如下命令启动服务端:
需要注意params.py中的`--use_gpu`的值
```
#标准版,Linux用户
#GPU用户
python -m paddle_serving_server_gpu.serve --model det_infer_server --port 9293 --gpu_id 0
python -m paddle_serving_server_gpu.serve --model cls_infer_server --port 9294 --gpu_id 0
python ocr_rpc_server.py
#CPU用户
python -m paddle_serving_server.serve --model det_infer_server --port 9293
python -m paddle_serving_server.serve --model cls_infer_server --port 9294
python ocr_rpc_server.py
#快速版,Windows/Linux用户
python ocr_local_server.py
```
客户端
```
python rec_web_client.py
```
# 更新
- 2022.5.9 发布PaddleOCR v2.5。发布内容包括:
- [PP-OCRv3](./doc/doc_ch/ppocr_introduction.md#pp-ocrv3),速度可比情况下,中文场景效果相比于PP-OCRv2再提升5%,英文场景提升11%,80语种多语言模型平均识别准确率提升5%以上;
- 半自动标注工具[PPOCRLabelv2](./PPOCRLabel):新增表格文字图像、图像关键信息抽取任务和不规则文字图像的标注功能;
- [PP-OCRv3](./ppocr_introduction.md#pp-ocrv3),速度可比情况下,中文场景效果相比于PP-OCRv2再提升5%,英文场景提升11%,80语种多语言模型平均识别准确率提升5%以上;
- 半自动标注工具[PPOCRLabelv2](../../PPOCRLabel):新增表格文字图像、图像关键信息抽取任务和不规则文字图像的标注功能;
- OCR产业落地工具集:打通22种训练部署软硬件环境与方式,覆盖企业90%的训练部署环境需求
- 交互式OCR开源电子书[《动手学OCR》](./doc/doc_ch/ocr_book.md),覆盖OCR全栈技术的前沿理论与代码实践,并配套教学视频。
- 交互式OCR开源电子书[《动手学OCR》](./ocr_book.md),覆盖OCR全栈技术的前沿理论与代码实践,并配套教学视频。
- 2022.5.7 添加对[Weights & Biases](https://docs.wandb.ai/)训练日志记录工具的支持。
- 2021.12.21 《OCR十讲》课程开讲,12月21日起每晚八点半线上授课! 【免费】报名地址:https://aistudio.baidu.com/aistudio/course/introduce/25207
- 2021.12.21 发布PaddleOCR v2.4。OCR算法新增1种文本检测算法(PSENet),3种文本识别算法(NRTR、SEED、SAR);文档结构化算法新增1种关键信息提取算法(SDMGR),3种DocVQA算法(LayoutLM、LayoutLMv2,LayoutXLM)。
......
......@@ -40,7 +40,7 @@ Please prepare your environment referring to [prepare the environment](./environ
The above EAST model is trained using the ICDAR2015 text detection public dataset. For the download of the dataset, please refer to [ocr_datasets](./dataset/ocr_datasets_en.md).
After the data download is complete, please refer to [Text Detection Training Tutorial](./detection.md) for training. PaddleOCR has modularized the code structure, so that you only need to **replace the configuration file** to train different detection models.
After the data download is complete, please refer to [Text Detection Training Tutorial](./detection_en.md) for training. PaddleOCR has modularized the code structure, so that you only need to **replace the configuration file** to train different detection models.
<a name="4"></a>
......
......@@ -37,7 +37,7 @@ Please prepare your environment referring to [prepare the environment](./environ
The above FCE model is trained using the CTW1500 text detection public dataset. For the download of the dataset, please refer to [ocr_datasets](./dataset/ocr_datasets_en.md).
After the data download is complete, please refer to [Text Detection Training Tutorial](./detection.md) for training. PaddleOCR has modularized the code structure, so that you only need to **replace the configuration file** to train different detection models.
After the data download is complete, please refer to [Text Detection Training Tutorial](./detection_en.md) for training. PaddleOCR has modularized the code structure, so that you only need to **replace the configuration file** to train different detection models.
<a name="4"></a>
## 4. Inference and Deployment
......
......@@ -39,7 +39,7 @@ Please prepare your environment referring to [prepare the environment](./environ
The above PSE model is trained using the ICDAR2015 text detection public dataset. For the download of the dataset, please refer to [ocr_datasets](./dataset/ocr_datasets_en.md).
After the data download is complete, please refer to [Text Detection Training Tutorial](./detection.md) for training. PaddleOCR has modularized the code structure, so that you only need to **replace the configuration file** to train different detection models.
After the data download is complete, please refer to [Text Detection Training Tutorial](./detection_en.md) for training. PaddleOCR has modularized the code structure, so that you only need to **replace the configuration file** to train different detection models.
<a name="4"></a>
## 4. Inference and Deployment
......
......@@ -36,7 +36,7 @@ The results of detection and recognition are as follows:
<a name="Environment_Configuration"></a>
## 2. Environment Configuration
Please refer to [Operation Environment Preparation](./environment_en.md) to configure PaddleOCR operating environment first, refer to [PaddleOCR Overview and Project Clone](./paddleOCR_overview_en.md) to clone the project
Please refer to [Operation Environment Preparation](./environment_en.md) to configure PaddleOCR operating environment first, refer to [Project Clone](./clone_en.md) to clone the project
<a name="Quick_Use"></a>
## 3. Quick Use
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......@@ -41,6 +41,12 @@ On Total-Text dataset, the text detection result is as follows:
| --- | --- | --- | --- | --- | --- |
|SAST|ResNet50_vd|89.63%|78.44%|83.66%|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_totaltext_v2.0_train.tar)|
On CTW1500 dataset, the text detection result is as follows:
|Model|Backbone|Precision|Recall|Hmean| Download link|
| --- | --- | --- | --- | --- |---|
|FCE|ResNet50_dcn|88.39%|82.18%|85.27%| [trained model](https://paddleocr.bj.bcebos.com/contribution/det_r50_dcn_fce_ctw_v2.0_train.tar) |
**Note:** Additional data, like icdar2013, icdar2017, COCO-Text, ArT, was added to the model training of SAST. Download English public dataset in organized format used by PaddleOCR from:
* [Baidu Drive](https://pan.baidu.com/s/12cPnZcVuV1zn5DOd4mqjVw) (download code: 2bpi).
* [Google Drive](https://drive.google.com/drive/folders/1ll2-XEVyCQLpJjawLDiRlvo_i4BqHCJe?usp=sharing)
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......@@ -33,13 +33,13 @@ Using MJSynth and SynthText two text recognition datasets for training, and eval
<a name="2"></a>
## 2. Environment
Please refer to ["Environment Preparation"](./environment.md) to configure the PaddleOCR environment, and refer to ["Project Clone"](./clone.md) to clone the project code.
Please refer to ["Environment Preparation"](./environment_en.md) to configure the PaddleOCR environment, and refer to ["Project Clone"](./clone_en.md) to clone the project code.
<a name="3"></a>
## 3. Model Training / Evaluation / Prediction
Please refer to [Text Recognition Tutorial](./recognition.md). PaddleOCR modularizes the code, and training different recognition models only requires **changing the configuration file**.
Please refer to [Text Recognition Tutorial](./recognition_en.md). PaddleOCR modularizes the code, and training different recognition models only requires **changing the configuration file**.
Training:
......
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