Merge branch 'dygraph' into cherry-pick

doc/doc_ch/inference.md

@@ -13,7 +13,6 @@ inference 模型（`paddle.jit.save`保存的模型）
     - [检测模型转inference模型](#检测模型转inference模型)
     - [识别模型转inference模型](#识别模型转inference模型)  
     - [方向分类模型转inference模型](#方向分类模型转inference模型)
-    - [端到端模型转inference模型](#端到端模型转inference模型)
 
 - [二、文本检测模型推理](#文本检测模型推理)
     - [1. 超轻量中文检测模型推理](#超轻量中文检测模型推理)
@@ -119,32 +118,6 @@ python3 tools/export_model.py -c configs/cls/cls_mv3.yml -o Global.pretrained_mo
     ├── inference.pdiparams.info    # 分类inference模型的参数信息，可忽略
     └── inference.pdmodel           # 分类inference模型的program文件
 ```
-<a name="端到端模型转inference模型"></a>
-### 端到端模型转inference模型
-
-下载端到端模型：
-```
-wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar && tar xf ./ch_lite/ch_ppocr_mobile_v2.0_cls_train.tar -C ./ch_lite/
-```
-
-端到端模型转inference模型与检测的方式相同，如下：
-```
-# -c 后面设置训练算法的yml配置文件
-# -o 配置可选参数
-# Global.pretrained_model 参数设置待转换的训练模型地址，不用添加文件后缀 .pdmodel，.pdopt或.pdparams。
-# Global.load_static_weights 参数需要设置为 False。
-# Global.save_inference_dir参数设置转换的模型将保存的地址。
-
-python3 tools/export_model.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.pretrained_model=./ch_lite/ch_ppocr_mobile_v2.0_cls_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/e2e/
-```
-
-转换成功后，在目录下有三个文件：
-```
-/inference/e2e/
-    ├── inference.pdiparams         # 分类inference模型的参数文件
-    ├── inference.pdiparams.info    # 分类inference模型的参数信息，可忽略
-    └── inference.pdmodel           # 分类inference模型的program文件
-```
 
 <a name="文本检测模型推理"></a>
 ## 二、文本检测模型推理

doc/doc_ch/pgnet.md

@@ -26,6 +26,14 @@ PGNet算法细节详见[论文](https://www.aaai.org/AAAI21Papers/AAAI-2885.Wang
 ![](../imgs_results/e2e_res_img293_pgnet.png)
 ![](../imgs_results/e2e_res_img295_pgnet.png)
 
+### 性能指标
+| |det_precision|det_recall|det_f_score|e2e_precision|e2e_recall|e2e_f_score|FPS (size=640)|下载链接|
+| --- | --- | --- | --- | --- | --- | --- | --- | --- |
+|Paper|85.30|86.80|86.1|-|-|61.7|38.20|-|
+|Ours|87.03|82.48|84.69|61.71|58.43|60.03|62.61|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/en_server_pgnetA.tar)|
+
+*note：PaddleOCR里的PGNet实现针对预测速度做了优化，在精度下降可接受范围内，可以显著提升端对端预测速度*
+
 <a name="环境配置"></a>
 ## 二、环境配置
 请先参考[快速安装](./installation.md)配置PaddleOCR运行环境。
@@ -170,10 +178,3 @@ python3 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/im
 可视化文本端到端结果默认保存到`./inference_results`文件夹里面，结果文件的名称前缀为'e2e_res'。结果示例如下：
 
 ![](../imgs_results/e2e_res_img623_pgnet.jpg)
-
-#### (3). 性能指标
-| |det_precision|det_recall|det_f_score|e2e_precision|e2e_recall|e2e_f_score|FPS (size=640)|
-| --- | --- | --- | --- | --- | --- | --- | --- |
-|Paper|85.30|86.80|86.1|-|-|61.7|38.20|
-|Ours|87.03|82.48|84.69|61.71|58.43|60.03|62.61|
-*note：PaddleOCR里的PGNet实现针对预测速度做了优化，在精度下降可接受范围内，可以显著提升端对端预测速度*

doc/doc_en/pgnet_en.md

@@ -23,6 +23,13 @@ The output of TBO and TCL can get text detection results after post-processing,
 The results of detection and recognition are as follows:
 ![](../imgs_results/e2e_res_img293_pgnet.png)
 ![](../imgs_results/e2e_res_img295_pgnet.png)
+### Performance
+| |det_precision|det_recall|det_f_score|e2e_precision|e2e_recall|e2e_f_score|FPS (size=640)|download|
+| --- | --- | --- | --- | --- | --- | --- | --- | --- |
+|Paper|85.30|86.80|86.1|-|-|61.7|38.20|-|
+|Ours|87.03|82.48|84.69|61.71|58.43|60.03|62.61|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/en_server_pgnetA.tar)|
+
+*note：PGNet in PaddleOCR optimizes the prediction speed, and can significantly improve the end-to-end prediction speed within the acceptable range of accuracy reduction*
 
 <a name="Environment_Configuration"></a>
 ## 2. Environment Configuration
@@ -173,9 +180,3 @@ python3 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/im
 The visualized text detection results are saved to the `./inference_results` folder by default, and the name of the result file is prefixed with 'e2e_res'. Examples of results are as follows:
 
 ![](../imgs_results/e2e_res_img623_pgnet.jpg)
-#### (3). Performance
-| |det_precision|det_recall|det_f_score|e2e_precision|e2e_recall|e2e_f_score|FPS (size=640)|
-| --- | --- | --- | --- | --- | --- | --- | --- |
-|Paper|85.30|86.80|86.1|-|-|61.7|38.20|
-|Ours|87.03|82.48|84.69|61.71|58.43|60.03|62.61|
-*note：PGNet in PaddleOCR optimizes the prediction speed, and can significantly improve the end-to-end prediction speed within the acceptable range of accuracy reduction*