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

e3c596d9 · qq_25193841 · 357657f0 · efc09082 · e3c596d9 · e3c596d9
29 changed file
--- a/PPOCRLabel/README.md
+++ b/PPOCRLabel/README.md
@@ -79,7 +79,7 @@ PPOCRLabel # run
 ```bash
 cd PaddleOCR/PPOCRLabel
-python3 setup.py bdist_wheel 
+python3 setup.py bdist_wheel
 pip3 install dist/PPOCRLabel-1.0.2-py2.py3-none-any.whl
 ```
@@ -171,7 +171,7 @@ python PPOCRLabel.py
 - Model language switching: Changing the built-in model language is supportable by clicking "PaddleOCR"-"Choose OCR Model" in the menu bar. Currently supported languagesinclude 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/release/ 2.3/PPOCRLabel/PPOCRLabel.py#L116) :
+- **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) :
  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) `
@@ -235,4 +235,4 @@ For some data that are difficult to recognize, the recognition results will not
 ### 4. Related
 1.[Tzutalin. LabelImg. Git code (2015)](https://github.com/tzutalin/labelImg)
\ No newline at end of file
--- a/README.md
+++ b/README.md
@@ -92,7 +92,7 @@ Mobile DEMO experience (based on EasyEdge and Paddle-Lite, supports iOS and Andr
 | ------------------------------------------------------------ | ---------------------------- | ----------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
 | Chinese and English ultra-lightweight PP-OCRv2 model（11.6M） |  ch_PP-OCRv2_xx |Mobile & Server|[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_distill_train.tar)| [inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_train.tar)|
 | Chinese and English ultra-lightweight PP-OCR model (9.4M)       | ch_ppocr_mobile_v2.0_xx      | Mobile & server   |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar)|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_train.tar)      |
-| Chinese and English general PP-OCR model (143.4M)               | ch_ppocr_server_v2.0_xx      | Server            |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_train.tar)    |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_traingit.tar)    |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_train.tar)  |
+| Chinese and English general PP-OCR model (143.4M)               | ch_ppocr_server_v2.0_xx      | Server            |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_train.tar)    |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar)    |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_train.tar)  |
 For more model downloads (including multiple languages), please refer to [PP-OCR series model downloads](./doc/doc_en/models_list_en.md).

--- a/README_ch.md
+++ b/README_ch.md
@@ -99,7 +99,7 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力
 - [PP-Structure信息提取](./ppstructure/README_ch.md)
    - [版面分析](./ppstructure/layout/README_ch.md)
    - [表格识别](./ppstructure/table/README_ch.md)
-    - [DocVQA](./ppstructure/vqa/README_ch.md)
+    - [DocVQA](./ppstructure/vqa/README.md)
    - [关键信息提取](./ppstructure/docs/kie.md)
 - OCR学术圈
    - [两阶段模型介绍与下载](./doc/doc_ch/algorithm_overview.md)

--- a/deploy/lite/readme.md
+++ b/deploy/lite/readme.md
@@ -42,7 +42,7 @@ git checkout release/v2.9
 注意：编译Paddle-Lite获得预测库时，需要打开`--with_cv=ON --with_extra=ON`两个选项，`--arch`表示`arm`版本，这里指定为armv8，
 更多编译命令
-介绍请参考 [链接](https://paddle-lite.readthedocs.io/zh/latest/source_compile/compile_andriod.html) 。
+介绍请参考 [链接](https://paddle-lite.readthedocs.io/zh/release-v2.10_a/source_compile/linux_x86_compile_android.html) 。
 直接下载预测库并解压后，可以得到`inference_lite_lib.android.armv8/`文件夹，通过编译Paddle-Lite得到的预测库位于
 `Paddle-Lite/build.lite.android.armv8.gcc/inference_lite_lib.android.armv8/`文件夹下。

--- a/deploy/lite/readme_en.md
+++ b/deploy/lite/readme_en.md
@@ -44,7 +44,7 @@ git checkout release/v2.8
 Note: When compiling Paddle-Lite to obtain the Paddle-Lite library, you need to turn on the two options `--with_cv=ON --with_extra=ON`, `--arch` means the `arm` version, here is designated as armv8,
-More compilation commands refer to the introduction [link](https://paddle-lite.readthedocs.io/zh/latest/source_compile/compile_andriod.html) 。
+More compilation commands refer to the introduction [link](https://paddle-lite.readthedocs.io/zh/release-v2.10_a/source_compile/linux_x86_compile_android.html) 。
 After directly downloading the Paddle-Lite library and decompressing it, you can get the `inference_lite_lib.android.armv8/` folder, and the Paddle-Lite library obtained by compiling Paddle-Lite is located
 `Paddle-Lite/build.lite.android.armv8.gcc/inference_lite_lib.android.armv8/` folder.

--- a/deploy/pdserving/README.md
+++ b/deploy/pdserving/README.md
@@ -19,10 +19,14 @@ The introduction and tutorial of Paddle Serving service deployment framework ref
 ## Contents
- [Environmental preparation](#environmental-preparation)
+- [OCR Pipeline WebService](#ocr-pipeline-webservice)
- [Model conversion](#model-conversion)
+- [Service deployment based on PaddleServing](#service-deployment-based-on-paddleserving)
- [Paddle Serving pipeline deployment](#paddle-serving-pipeline-deployment)
+  - [Contents](#contents)
- [FAQ](#faq)
+  - [Environmental preparation](#environmental-preparation)
+  - [Model conversion](#model-conversion)
+  - [Paddle Serving pipeline deployment](#paddle-serving-pipeline-deployment)
+  - [WINDOWS Users](#windows-users)
+  - [FAQ](#faq)
 <a name="environmental-preparation"></a>
 ## Environmental preparation
@@ -201,7 +205,7 @@ The recognition model is the same.
 ## WINDOWS Users
-Windows does not support Pipeline Serving, if we want to lauch paddle serving on Windows, we should use Web Service, for more infomation please refer to [Paddle Serving for Windows Users](https://github.com/PaddlePaddle/Serving/blob/develop/doc/WINDOWS_TUTORIAL.md)
+Windows does not support Pipeline Serving, if we want to lauch paddle serving on Windows, we should use Web Service, for more infomation please refer to [Paddle Serving for Windows Users](https://github.com/PaddlePaddle/Serving/blob/develop/doc/Windows_Tutorial_EN.md)
 **WINDOWS user can only use version 0.5.0 CPU Mode**

--- a/doc/doc_ch/thirdparty.md
+++ b/doc/doc_ch/thirdparty.md
@@ -28,9 +28,12 @@ PaddleOCR希望可以通过AI的力量助力任何一位有梦想的开发者实
 | 垂类工具 | [id_card_ocr](https://github.com/baseli/id_card_ocr)         | 身份证复印件识别           | [baseli](https://github.com/baseli)                          |
 | 垂类工具 | [Paddle_Table_Image_Reader](https://github.com/thunder95/Paddle_Table_Image_Reader) | 能看懂表格图片的数据助手   | [thunder95](https://github.com/thunder95])                   |
 | 垂类工具 | [AI Studio项目](https://aistudio.baidu.com/aistudio/projectdetail/3382897) | OCR流程中对手写体进行过滤       | [daassh](https://github.com/daassh) |
+| 垂类场景调优 | [AI Studio项目](https://aistudio.baidu.com/aistudio/projectdetail/2803693) | 电表读数和编号识别      | [深渊上的坑](https://github.com/edencfc) |
+| 垂类工具 | [AI Studio项目](https://aistudio.baidu.com/aistudio/projectdetail/3284199) | LCD液晶字符检测      | [Dream拒杰](https://github.com/zhangyingying520) |
 | 前后处理 | [paddleOCRCorrectOutputs](https://github.com/yuranusduke/paddleOCRCorrectOutputs) | 获取OCR识别结果的key-value | [yuranusduke](https://github.com/yuranusduke)                |
 |前处理| [optlab](https://github.com/GreatV/optlab) |OCR前处理工具箱，基于Qt和Leptonica。|[GreatV](https://github.com/GreatV)|
 |应用部署| [PaddleOCRSharp](https://github.com/raoyutian/PaddleOCRSharp) |PaddleOCR的.NET封装与应用部署。|[raoyutian](https://github.com/raoyutian/PaddleOCRSharp)|
+|应用部署| [PaddleSharp](https://github.com/sdcb/PaddleSharp) |PaddleOCR的.NET封装与应用部署，支持跨平台、GPU|[sdcb](https://github.com/sdcb)|
 | 学术前沿模型训练与推理 | [AI Studio项目](https://aistudio.baidu.com/aistudio/projectdetail/3397137) | StarNet-MobileNetV3算法–中文训练 | [xiaoyangyang2](https://github.com/xiaoyangyang2)                |
 ### 1.2 为PaddleOCR新增功能

--- a/doc/doc_en/models_list_en.md
+++ b/doc/doc_en/models_list_en.md
@@ -4,13 +4,14 @@
 > 2. Compared with [models 1.1](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_en/models_list_en.md), which are trained with static graph programming paradigm, models 2.0 are the dynamic graph trained version and achieve close performance.
 > 3. All models in this tutorial are all ppocr-series models, for more introduction of algorithms and models based on public dataset, you can refer to [algorithm overview tutorial](./algorithm_overview_en.md).
- [1. Text Detection Model](#Detection)
+- [OCR Model List（V2.1, updated on 2021.9.6）](#ocr-model-listv21-updated-on-202196)
- [2. Text Recognition Model](#Recognition)
+  - [1. Text Detection Model](#1-text-detection-model)
-    - [2.1 Chinese Recognition Model](#Chinese)
+  - [2. Text Recognition Model](#2-text-recognition-model)
-    - [2.2 English Recognition Model](#English)
+    - [2.1 Chinese Recognition Model](#21-chinese-recognition-model)
-    - [2.3 Multilingual Recognition Model](#Multilingual)
+    - [2.2 English Recognition Model](#22-english-recognition-model)
- [3. Text Angle Classification Model](#Angle)
+    - [2.3 Multilingual Recognition Model（Updating...）](#23-multilingual-recognition-modelupdating)
- [4. Paddle-Lite Model](#Paddle-Lite)
+  - [3. Text Angle Classification Model](#3-text-angle-classification-model)
+  - [4. Paddle-Lite Model](#4-paddle-lite-model)
 The downloadable models provided by PaddleOCR include `inference model`, `trained model`, `pre-trained model` and `slim model`. The differences between the models are as follows:
@@ -44,7 +45,7 @@ Relationship of the above models is as follows.
 |model name|description|config|model size|download|
 | --- | --- | --- | --- | --- |
 |ch_PP-OCRv2_rec_slim|[New] Slim qunatization with distillation lightweight model, supporting Chinese, English, multilingual text recognition|[ch_PP-OCRv2_rec.yml](../../configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec.yml)| 9M |[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_slim_quant_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_slim_quant_train.tar) |
-|ch_PP-OCRv2_rec|[New] Original lightweight model, supporting Chinese, English, multilingual text recognition|[ch_PP-OCRv2_rec_distillation.yml](../../configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec_distillation.yml)|8.5M|[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_train.tar) |
+|ch_PP-OCRv2_rec|[New] Original lightweight model, supporting Chinese, English, multilingual text recognition|[ch_PP-OCRv2_rec_distillation.yml](../../configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec_distillation.yml)|8.5M|[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_train.tar) |
 |ch_ppocr_mobile_slim_v2.0_rec|Slim pruned and quantized lightweight model, supporting Chinese, English and number recognition|[rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml)| 6M | [inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_slim_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_slim_train.tar) |
 |ch_ppocr_mobile_v2.0_rec|Original lightweight model, supporting Chinese, English and number recognition|[rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml)|5.2M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_train.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_pre.tar) |
 |ch_ppocr_server_v2.0_rec|General model, supporting Chinese, English and number recognition|[rec_chinese_common_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_common_train_v2.0.yml)|94.8M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_train.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_pre.tar) |

--- a/ppocr/losses/rec_ctc_loss.py
+++ b/ppocr/losses/rec_ctc_loss.py
@@ -31,7 +31,8 @@ class CTCLoss(nn.Layer):
            predicts = predicts[-1]
        predicts = predicts.transpose((1, 0, 2))
        N, B, _ = predicts.shape
-        preds_lengths = paddle.to_tensor([N] * B, dtype='int64')
+        preds_lengths = paddle.to_tensor(
+            [N] * B, dtype='int64', place=paddle.CPUPlace())
        labels = batch[1].astype("int32")
        label_lengths = batch[2].astype('int64')
        loss = self.loss_func(predicts, labels, preds_lengths, label_lengths)

--- a/ppstructure/README.md
+++ b/ppstructure/README.md
 English | [简体中文](README_ch.md)
- [1. Introduction](#1)
+- [1. Introduction](#1-introduction)
- [2. Update log](#2)
+- [2. Update log](#2-update-log)
- [3. Features](#3)
+- [3. Features](#3-features)
- [4. Results](#4)
+- [4. Results](#4-results)
-  * [4.1 Layout analysis and table recognition](#41)
+  - [4.1 Layout analysis and table recognition](#41-layout-analysis-and-table-recognition)
-  * [4.2 DOC-VQA](#42)
+  - [4.2 DOC-VQA](#42-doc-vqa)
- [5. Quick start](#5)
+- [5. Quick start](#5-quick-start)
- [6. PP-Structure System](#6)
+- [6. PP-Structure System](#6-pp-structure-system)
-  * [6.1 Layout analysis and table recognition](#61)
+  - [6.1 Layout analysis and table recognition](#61-layout-analysis-and-table-recognition)
-  * [6.2 DOC-VQA](#62)
+    - [6.1.1 Layout analysis](#611-layout-analysis)
- [7. Model List](#7)
+    - [6.1.2 Table recognition](#612-table-recognition)
+  - [6.2 DOC-VQA](#62-doc-vqa)
+- [7. Model List](#7-model-list)
 <a name="1"></a>
@@ -54,8 +56,8 @@ The figure shows the pipeline of layout analysis + table recognition. The image
 ### 4.2 DOC-VQA
 * SER
+*
-![](./vqa/images/result_ser/zh_val_0_ser.jpg) | ![](./vqa/images/result_ser/zh_val_42_ser.jpg)
+![](../doc/vqa/result_ser/zh_val_0_ser.jpg) | ![](../doc/vqa/result_ser/zh_val_42_ser.jpg)
 ---|---
 Different colored boxes in the figure represent different categories. For xfun dataset, there are three categories: query, answer and header:
@@ -69,7 +71,7 @@ The corresponding category and OCR recognition results are also marked at the to
 * RE
-![](./vqa/images/result_re/zh_val_21_re.jpg) | ![](./vqa/images/result_re/zh_val_40_re.jpg)
+![](../doc/vqa/result_re/zh_val_21_re.jpg) | ![](../doc/vqa/result_re/zh_val_40_re.jpg)
 ---|---
@@ -96,7 +98,7 @@ In PP-Structure, the image will be divided into 5 types of areas **text, title,
 #### 6.1.1 Layout analysis
-Layout analysis classifies image by region, including the use of Python scripts of layout analysis tools, extraction of designated category detection boxes, performance indicators, and custom training layout analysis models. For details, please refer to [document](layout/README_en.md).
+Layout analysis classifies image by region, including the use of Python scripts of layout analysis tools, extraction of designated category detection boxes, performance indicators, and custom training layout analysis models. For details, please refer to [document](layout/README.md).
 #### 6.1.2 Table recognition

--- a/ppstructure/README_ch.md
+++ b/ppstructure/README_ch.md
 [English](README.md) | 简体中文
- [1. 简介](#1)
+- [1. 简介](#1-简介)
- [2. 近期更新](#2)
+- [2. 近期更新](#2-近期更新)
- [3. 特性](#3)
+- [3. 特性](#3-特性)
- [4. 效果展示](#4)
+- [4. 效果展示](#4-效果展示)
-  * [4.1 版面分析和表格识别](#41)
+  - [4.1 版面分析和表格识别](#41-版面分析和表格识别)
-  * [4.2 DOC-VQA](#42)
+  - [4.2 DOC-VQA](#42-doc-vqa)
- [5. 快速体验](#5)
+- [5. 快速体验](#5-快速体验)
- [6. PP-Structure 介绍](#6)
+- [6. PP-Structure 介绍](#6-pp-structure-介绍)
-  * [6.1 版面分析+表格识别](#61)
+  - [6.1 版面分析+表格识别](#61-版面分析表格识别)
-  * [6.2 DOC-VQA](#62)
+    - [6.1.1 版面分析](#611-版面分析)
- [7. 模型库](#7)
+    - [6.1.2 表格识别](#612-表格识别)
+  - [6.2 DOC-VQA](#62-doc-vqa)
+- [7. 模型库](#7-模型库)
 <a name="1"></a>
@@ -54,7 +56,7 @@ PP-Structure的主要特性如下：
 * SER
-![](./vqa/images/result_ser/zh_val_0_ser.jpg) | ![](./vqa/images/result_ser/zh_val_42_ser.jpg)
+![](../doc/vqa/result_ser/zh_val_0_ser.jpg) | ![](../doc/vqa/result_ser/zh_val_42_ser.jpg)
 ---|---
 图中不同颜色的框表示不同的类别，对于XFUN数据集，有`QUESTION`, `ANSWER`, `HEADER` 3种类别
@@ -67,7 +69,7 @@ PP-Structure的主要特性如下：
 * RE
-![](./vqa/images/result_re/zh_val_21_re.jpg) | ![](./vqa/images/result_re/zh_val_40_re.jpg)
+![](../doc/vqa/result_re/zh_val_21_re.jpg) | ![](../doc/vqa/result_re/zh_val_40_re.jpg)
 ---|---
@@ -134,4 +136,4 @@ PP-Structure系列模型列表（更新中）
 |PP-Layout_v1.0_re_pretrained|基于LayoutXLM在xfun中文数据集上训练的RE模型|1.4G|[推理模型 coming soon]() / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/PP-Layout_v1.0_re_pretrained.tar) |
 更多模型下载，可以参考 [PPOCR model_list](../doc/doc_en/models_list.md) and  [PPStructure model_list](./docs/model_list.md)
\ No newline at end of file
--- a/test_tipc/benchmark_train.sh
+++ b/test_tipc/benchmark_train.sh
+#!/bin/bash
+source test_tipc/common_func.sh
+# set env
+python=python
+export model_branch=`git symbolic-ref HEAD 2>/dev/null | cut -d"/" -f 3`
+export model_commit=$(git log|head -n1|awk '{print $2}') 
+export str_tmp=$(echo `pip list|grep paddlepaddle-gpu|awk -F ' ' '{print $2}'`)
+export frame_version=${str_tmp%%.post*}
+export frame_commit=$(echo `${python} -c "import paddle;print(paddle.version.commit)"`)
+# run benchmark sh 
+# Usage:
+# bash run_benchmark_train.sh config.txt params
+# or 
+# bash run_benchmark_train.sh config.txt
+function func_parser_params(){
+    strs=$1
+    IFS="="
+    array=(${strs})
+    tmp=${array[1]}
+    echo ${tmp}
+}
+function func_sed_params(){
+    filename=$1
+    line=$2
+    param_value=$3
+    params=`sed -n "${line}p" $filename`
+    IFS=":"
+    array=(${params})
+    key=${array[0]}
+    value=${array[1]}
+    new_params="${key}:${param_value}"
+    IFS=";"
+    cmd="sed -i '${line}s/.*/${new_params}/' '${filename}'"
+    eval $cmd
+}
+function set_gpu_id(){
+    string=$1
+    _str=${string:1:6}
+    IFS="C"
+    arr=(${_str})
+    M=${arr[0]}
+    P=${arr[1]}
+    gn=`expr $P - 1`
+    gpu_num=`expr $gn / $M`
+    seq=`seq -s "," 0 $gpu_num`
+    echo $seq
+}
+function get_repo_name(){
+    IFS=";"
+    cur_dir=$(pwd)
+    IFS="/"
+    arr=(${cur_dir})
+    echo ${arr[-1]}
+}
+FILENAME=$1
+# copy FILENAME as new
+new_filename="./test_tipc/benchmark_train.txt"
+cmd=`yes|cp $FILENAME $new_filename`
+FILENAME=$new_filename
+# MODE must be one of ['benchmark_train']
+MODE=$2
+PARAMS=$3
+# bash test_tipc/benchmark_train.sh test_tipc/configs/det_mv3_db_v2_0/train_benchmark.txt  benchmark_train dynamic_bs8_null_DP_N1C1
+IFS=$'\n'
+# parser params from train_benchmark.txt
+dataline=`cat $FILENAME`
+# parser params
+IFS=$'\n'
+lines=(${dataline})
+model_name=$(func_parser_value "${lines[1]}")
+# 获取benchmark_params所在的行数
+line_num=`grep -n "train_benchmark_params" $FILENAME  | cut -d ":" -f 1`
+# for train log parser
+batch_size=$(func_parser_value "${lines[line_num]}")
+line_num=`expr $line_num + 1`
+fp_items=$(func_parser_value "${lines[line_num]}")
+line_num=`expr $line_num + 1`
+epoch=$(func_parser_value "${lines[line_num]}")
+line_num=`expr $line_num + 1`
+profile_option_key=$(func_parser_key "${lines[line_num]}")
+profile_option_params=$(func_parser_value "${lines[line_num]}")
+profile_option="${profile_option_key}:${profile_option_params}"
+line_num=`expr $line_num + 1`
+flags_value=$(func_parser_value "${lines[line_num]}")
+# set flags
+IFS=";"
+flags_list=(${flags_value})
+for _flag in ${flags_list[*]}; do
+    cmd="export ${_flag}"
+    eval $cmd
+done
+# set log_name
+repo_name=$(get_repo_name )
+SAVE_LOG=${BENCHMARK_LOG_DIR:-$(pwd)}   # */benchmark_log
+mkdir -p "${SAVE_LOG}/benchmark_log/"
+status_log="${SAVE_LOG}/benchmark_log/results.log"
+# The number of lines in which train params can be replaced.
+line_python=3
+line_gpuid=4
+line_precision=6
+line_epoch=7
+line_batchsize=9
+line_profile=13
+line_eval_py=24
+line_export_py=30
+func_sed_params "$FILENAME" "${line_eval_py}" "null"
+func_sed_params "$FILENAME" "${line_export_py}" "null"
+func_sed_params "$FILENAME" "${line_python}"  "$python"
+# if params
+if  [ ! -n "$PARAMS" ] ;then
+    # PARAMS input is not a word.
+    IFS="|"
+    batch_size_list=(${batch_size})
+    fp_items_list=(${fp_items})
+    device_num_list=(N1C4)
+    run_mode="DP"
+else
+    # parser params from input: modeltype_bs${bs_item}_${fp_item}_${run_mode}_${device_num}
+    IFS="_"
+    params_list=(${PARAMS})
+    model_type=${params_list[0]}
+    batch_size=${params_list[1]}
+    batch_size=`echo  ${batch_size} | tr -cd "[0-9]" `
+    precision=${params_list[2]}
+    # run_process_type=${params_list[3]}
+    run_mode=${params_list[3]}
+    device_num=${params_list[4]}
+    IFS=";"
+    if [ ${precision} = "null" ];then
+        precision="fp32"
+    fi
+    fp_items_list=($precision)
+    batch_size_list=($batch_size)
+    device_num_list=($device_num)
+fi
+IFS="|"
+for batch_size in ${batch_size_list[*]}; do 
+    for precision in ${fp_items_list[*]}; do
+        for device_num in ${device_num_list[*]}; do
+            # sed batchsize and precision
+            func_sed_params "$FILENAME" "${line_precision}" "$precision"
+            func_sed_params "$FILENAME" "${line_batchsize}" "$MODE=$batch_size"
+            func_sed_params "$FILENAME" "${line_epoch}" "$MODE=$epoch"
+            gpu_id=$(set_gpu_id $device_num)
+            if [ ${#gpu_id} -le 1 ];then
+                run_process_type="SingleP"
+                log_path="$SAVE_LOG/profiling_log"
+                mkdir -p $log_path
+                log_name="${repo_name}_${model_name}_bs${batch_size}_${precision}_${run_process_type}_${run_mode}_${device_num}_profiling"
+                func_sed_params "$FILENAME" "${line_gpuid}" "0"  # sed used gpu_id 
+                # set profile_option params
+                tmp=`sed -i "${line_profile}s/.*/${profile_option}/" "${FILENAME}"`
+                # run test_train_inference_python.sh
+                cmd="bash test_tipc/test_train_inference_python.sh ${FILENAME} benchmark_train > ${log_path}/${log_name} 2>&1 "
+                echo $cmd
+                eval $cmd
+                eval "cat ${log_path}/${log_name}"
+                # without profile
+                log_path="$SAVE_LOG/train_log"
+                speed_log_path="$SAVE_LOG/index"
+                mkdir -p $log_path
+                mkdir -p $speed_log_path
+                log_name="${repo_name}_${model_name}_bs${batch_size}_${precision}_${run_process_type}_${run_mode}_${device_num}_log"
+                speed_log_name="${repo_name}_${model_name}_bs${batch_size}_${precision}_${run_process_type}_${run_mode}_${device_num}_speed"
+                func_sed_params "$FILENAME" "${line_profile}" "null"  # sed profile_id as null
+                cmd="bash test_tipc/test_train_inference_python.sh ${FILENAME} benchmark_train > ${log_path}/${log_name} 2>&1 "
+                echo $cmd
+                job_bt=`date '+%Y%m%d%H%M%S'`
+                eval $cmd
+                job_et=`date '+%Y%m%d%H%M%S'`
+                export model_run_time=$((${job_et}-${job_bt}))
+                eval "cat ${log_path}/${log_name}"
+                # parser log
+                _model_name="${model_name}_bs${batch_size}_${precision}_${run_process_type}_${run_mode}"
+                cmd="${python} ${BENCHMARK_ROOT}/scripts/analysis.py --filename ${log_path}/${log_name} \
+                        --speed_log_file '${speed_log_path}/${speed_log_name}' \
+                        --model_name ${_model_name} \
+                        --base_batch_size ${batch_size} \
+                        --run_mode ${run_mode} \
+                        --run_process_type ${run_process_type} \
+                        --fp_item ${precision} \
+                        --keyword ips: \
+                        --skip_steps 2 \
+                        --device_num ${device_num} \
+                        --speed_unit samples/s \
+                        --convergence_key loss: "
+                echo $cmd
+                eval $cmd
+                last_status=${PIPESTATUS[0]}
+                status_check $last_status "${cmd}" "${status_log}"
+            else
+                IFS=";"
+                unset_env=`unset CUDA_VISIBLE_DEVICES`
+                run_process_type="MultiP"
+                log_path="$SAVE_LOG/train_log"
+                speed_log_path="$SAVE_LOG/index"
+                mkdir -p $log_path
+                mkdir -p $speed_log_path
+                log_name="${repo_name}_${model_name}_bs${batch_size}_${precision}_${run_process_type}_${run_mode}_${device_num}_log"
+                speed_log_name="${repo_name}_${model_name}_bs${batch_size}_${precision}_${run_process_type}_${run_mode}_${device_num}_speed"
+                func_sed_params "$FILENAME" "${line_gpuid}" "$gpu_id"  # sed used gpu_id 
+                func_sed_params "$FILENAME" "${line_profile}" "null"  # sed --profile_option as null
+                cmd="bash test_tipc/test_train_inference_python.sh ${FILENAME} benchmark_train > ${log_path}/${log_name} 2>&1 "
+                echo $cmd
+                job_bt=`date '+%Y%m%d%H%M%S'`
+                eval $cmd
+                job_et=`date '+%Y%m%d%H%M%S'`
+                export model_run_time=$((${job_et}-${job_bt}))
+                eval "cat ${log_path}/${log_name}"
+                # parser log
+                _model_name="${model_name}_bs${batch_size}_${precision}_${run_process_type}_${run_mode}"
+                cmd="${python} ${BENCHMARK_ROOT}/scripts/analysis.py --filename ${log_path}/${log_name} \
+                        --speed_log_file '${speed_log_path}/${speed_log_name}' \
+                        --model_name ${_model_name} \
+                        --base_batch_size ${batch_size} \
+                        --run_mode ${run_mode} \
+                        --run_process_type ${run_process_type} \
+                        --fp_item ${precision} \
+                        --keyword ips: \
+                        --skip_steps 2 \
+                        --device_num ${device_num} \
+                        --speed_unit images/s \
+                        --convergence_key loss: "
+                echo $cmd
+                eval $cmd
+                last_status=${PIPESTATUS[0]}
+                status_check $last_status "${cmd}" "${status_log}"
+            fi
+        done
+    done
+done
--- a/test_tipc/configs/det_mv3_db_v2.0/train_infer_python.txt
+++ b/test_tipc/configs/det_mv3_db_v2.0/train_infer_python.txt
 ===========================train_params===========================
-model_name:det_mv3_db_v2.0
+model_name:det_mv3_db_v2_0
 python:python3.7
 gpu_list:0|0,1
 Global.use_gpu:True|True
@@ -48,4 +48,10 @@ inference:tools/infer/predict_det.py
 --image_dir:./inference/ch_det_data_50/all-sum-510/
 null:null
 --benchmark:True
 null:null
\ No newline at end of file
+===========================train_benchmark_params==========================
+batch_size:8|16
+fp_items:fp32|fp16
+epoch:2
+--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
+flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096
\ No newline at end of file
--- a/test_tipc/configs/det_r18_vd_v2_0/train_infer_python.txt
+++ b/test_tipc/configs/det_r18_vd_v2_0/train_infer_python.txt
+===========================train_params===========================
+model_name:det_r18_db_v2_0
+python:python3.7
+gpu_list:0|0,1
+Global.use_gpu:True|True
+Global.auto_cast:null
+Global.epoch_num:lite_train_lite_infer=2|whole_train_whole_infer=300
+Global.save_model_dir:./output/
+Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_lite_infer=4
+Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/
+null:null
+##
+trainer:norm_train
+norm_train:tools/train.py -c configs/det/det_res18_db_v2.0.yml -o 
+quant_export:null
+fpgm_export:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:null
+null:null
+##
+===========================infer_params===========================
+Global.save_inference_dir:./output/
+Global.checkpoints:
+norm_export:null
+quant_export:null 
+fpgm_export:null
+distill_export:null
+export1:null
+export2:null
+##
+train_model:null
+infer_export:null
+infer_quant:False
+inference:tools/infer/predict_det.py
+--use_gpu:True|False
+--enable_mkldnn:True|False
+--cpu_threads:1|6
+--rec_batch_num:1
+--use_tensorrt:False|True
+--precision:fp32|fp16|int8
+--det_model_dir:
+--image_dir:./inference/ch_det_data_50/all-sum-510/
+--save_log_path:null
+--benchmark:True
+null:null
+===========================train_benchmark_params==========================
+batch_size:8|16
+fp_items:fp32|fp16
+epoch:2
+--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
--- a/test_tipc/configs/det_r50_vd_east_v2.0/det_r50_vd_east.yml
+++ b/test_tipc/configs/det_r50_vd_east_v2.0/det_r50_vd_east.yml
--- a/test_tipc/configs/det_r50_vd_east_v2.0/train_infer_python.txt
+++ b/test_tipc/configs/det_r50_vd_east_v2.0/train_infer_python.txt
 ===========================train_params===========================
-model_name:det_r50_vd_east_v2.0
+model_name:det_r50_vd_east_v2_0
 python:python3.7
 gpu_list:0
 Global.use_gpu:True|True
@@ -13,7 +13,7 @@ train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/
 null:null
 ##
 trainer:norm_train
-norm_train:tools/train.py -c test_tipc/configs/det_r50_vd_east_v2.0/det_r50_vd_east.yml -o 
+norm_train:tools/train.py -c test_tipc/configs/det_r50_vd_east_v2_0/det_r50_vd_east.yml -o 
 pact_train:null
 fpgm_train:null
 distill_train:null
@@ -27,7 +27,7 @@ null:null
 ===========================infer_params===========================
 Global.save_inference_dir:./output/
 Global.checkpoints:
-norm_export:tools/export_model.py -c test_tipc/configs/det_r50_vd_east_v2.0/det_r50_vd_east.yml -o 
+norm_export:tools/export_model.py -c test_tipc/configs/det_r50_vd_east_v2_0/det_r50_vd_east.yml -o 
 quant_export:null 
 fpgm_export:null
 distill_export:null
@@ -35,7 +35,7 @@ export1:null
 export2:null
 ##
 train_model:./inference/det_r50_vd_east_v2.0_train/best_accuracy
-infer_export:tools/export_model.py -c test_tipc/configs/det_r50_vd_east_v2.0/det_r50_vd_east.yml  -o
+infer_export:tools/export_model.py -c test_tipc/configs/det_r50_vd_east_v2_0/det_r50_vd_east.yml  -o
 infer_quant:False
 inference:tools/infer/predict_det.py
 --use_gpu:True|False
@@ -49,3 +49,8 @@ inference:tools/infer/predict_det.py
 --save_log_path:null
 --benchmark:True
 --det_algorithm:EAST
+===========================train_benchmark_params==========================
+batch_size:8
+fp_items:fp32|fp16
+epoch:2
+--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
\ No newline at end of file
--- a/test_tipc/configs/det_r50_vd_pse_v2.0/det_r50_vd_pse.yml
+++ b/test_tipc/configs/det_r50_vd_pse_v2.0/det_r50_vd_pse.yml
--- a/test_tipc/configs/det_r50_vd_pse_v2.0/train_infer_python.txt
+++ b/test_tipc/configs/det_r50_vd_pse_v2.0/train_infer_python.txt
 ===========================train_params===========================
-model_name:det_r50_vd_pse_v2.0
+model_name:det_r50_vd_pse_v2_0
 python:python3.7
 gpu_list:0
 Global.use_gpu:True|True
@@ -13,7 +13,7 @@ train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/
 null:null
 ##
 trainer:norm_train
-norm_train:tools/train.py -c test_tipc/configs/det_r50_vd_pse_v2.0/det_r50_vd_pse.yml -o 
+norm_train:tools/train.py -c test_tipc/configs/det_r50_vd_pse_v2_0/det_r50_vd_pse.yml -o 
 pact_train:null
 fpgm_train:null
 distill_train:null
@@ -27,7 +27,7 @@ null:null
 ===========================infer_params===========================
 Global.save_inference_dir:./output/
 Global.checkpoints:
-norm_export:tools/export_model.py -c test_tipc/configs/det_r50_vd_pse_v2.0/det_r50_vd_pse.yml -o 
+norm_export:tools/export_model.py -c test_tipc/configs/det_r50_vd_pse_v2_0/det_r50_vd_pse.yml -o 
 quant_export:null 
 fpgm_export:null
 distill_export:null
@@ -35,7 +35,7 @@ export1:null
 export2:null
 ##
 train_model:./inference/det_r50_vd_pse_v2.0_train/best_accuracy
-infer_export:tools/export_model.py -c test_tipc/configs/det_r50_vd_pse_v2.0/det_r50_vd_pse.yml  -o
+infer_export:tools/export_model.py -c test_tipc/configs/det_r50_vd_pse_v2_0/det_r50_vd_pse.yml  -o
 infer_quant:False
 inference:tools/infer/predict_det.py
 --use_gpu:True|False
@@ -49,3 +49,8 @@ inference:tools/infer/predict_det.py
 --save_log_path:null
 --benchmark:True
 --det_algorithm:PSE
+===========================train_benchmark_params==========================
+batch_size:8
+fp_items:fp32|fp16
+epoch:2
+--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
--- a/test_tipc/docs/benchmark_train.md
+++ b/test_tipc/docs/benchmark_train.md
+# TIPC Linux端Benchmark测试文档
+该文档为Benchmark测试说明，Benchmark预测功能测试的主程序为`benchmark_train.sh`，用于验证监控模型训练的性能。
+# 1. 测试流程
+## 1.1 准备数据和环境安装
+运行`test_tipc/prepare.sh`，完成训练数据准备和安装环境流程。
+```shell
+# 运行格式：bash test_tipc/prepare.sh  train_benchmark.txt  mode
+bash test_tipc/prepare.sh test_tipc/configs/det_mv3_db_v2_0/train_benchmark.txt benchmark_train
+```
+## 1.2 功能测试
+执行`test_tipc/benchmark_train.sh`，完成模型训练和日志解析
+```shell
+# 运行格式：bash test_tipc/benchmark_train.sh train_benchmark.txt mode
+bash test_tipc/benchmark_train.sh test_tipc/configs/det_mv3_db_v2_0/train_infer_python.txt benchmark_train
+```
+`test_tipc/benchmark_train.sh`支持根据传入的第三个参数实现只运行某一个训练配置，如下：
+```shell
+# 运行格式：bash test_tipc/benchmark_train.sh train_benchmark.txt mode
+bash test_tipc/benchmark_train.sh test_tipc/configs/det_mv3_db_v2_0/train_infer_python.txt benchmark_train  dynamic_bs8_fp32_DP_N1C1
+```
+dynamic_bs8_fp32_DP_N1C1为test_tipc/benchmark_train.sh传入的参数，格式如下：
+`${modeltype}_${batch_size}_${fp_item}_${run_mode}_${device_num}`
+包含的信息有：模型类型、batchsize大小、训练精度如fp32,fp16等、分布式运行模式以及分布式训练使用的机器信息如单机单卡（N1C1）。
+## 2. 日志输出
+运行后将保存模型的训练日志和解析日志，使用 `test_tipc/configs/det_mv3_db_v2_0/train_benchmark.txt` 参数文件的训练日志解析结果是：
+```
+{"model_branch": "dygaph", "model_commit": "7c39a1996b19087737c05d883fd346d2f39dbcc0", "model_name": "det_mv3_db_v2_0_bs8_fp32_SingleP_DP", "batch_size": 8, "fp_item": "fp32", "run_process_type": "SingleP", "run_mode": "DP", "convergence_value": "5.413110", "convergence_key": "loss:", "ips": 19.333, "speed_unit": "samples/s", "device_num": "N1C1", "model_run_time": "0", "frame_commit": "8cc09552473b842c651ead3b9848d41827a3dbab", "frame_version": "0.0.0"}
+```
+训练日志和日志解析结果保存在benchmark_log目录下，文件组织格式如下：
+```
+train_log/
+├── index
+│   ├── PaddleOCR_det_mv3_db_v2_0_bs8_fp32_SingleP_DP_N1C1_speed
+│   └── PaddleOCR_det_mv3_db_v2_0_bs8_fp32_SingleP_DP_N1C4_speed
+├── profiling_log
+│   └── PaddleOCR_det_mv3_db_v2_0_bs8_fp32_SingleP_DP_N1C1_profiling
+└── train_log
+    ├── PaddleOCR_det_mv3_db_v2_0_bs8_fp32_SingleP_DP_N1C1_log
+    └── PaddleOCR_det_mv3_db_v2_0_bs8_fp32_SingleP_DP_N1C4_log
+```
--- a/test_tipc/prepare.sh
+++ b/test_tipc/prepare.sh
@@ -20,6 +20,15 @@ model_name=$(func_parser_value "${lines[1]}")
 trainer_list=$(func_parser_value "${lines[14]}")
+if [ ${MODE} = "benchmark_train" ];then
+    pip install -r requirements.txt
+    if [[ ${model_name} =~ "det_mv3_db_v2_0" || ${model_name} =~ "det_r50_vd_east_v2_0" || ${model_name} =~ "det_r50_vd_pse_v2_0" || ${model_name} =~ "det_r18_db_v2_0" ]];then
+        rm -rf ./train_data/icdar2015
+        wget -nc -P  ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams  --no-check-certificate
+        wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015.tar --no-check-certificate
+        cd ./train_data/ && tar xf icdar2015.tar && cd ../
+    fi
+fi
 if [ ${MODE} = "lite_train_lite_infer" ];then
    # pretrain lite train data
@@ -52,7 +61,7 @@ if [ ${MODE} = "lite_train_lite_infer" ];then
        wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/total_text_lite.tar --no-check-certificate
        cd ./train_data && tar xf total_text_lite.tar && ln -s total_text_lite total_text  && cd ../
    fi
-    if [ ${model_name} == "det_mv3_db_v2.0" ]; then
+    if [ ${model_name} == "det_mv3_db_v2_0" ]; then
        wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar  --no-check-certificate
        cd ./inference/ && tar xf det_mv3_db_v2.0_train.tar && cd ../
    fi
@@ -211,7 +220,7 @@ elif [ ${MODE} = "whole_infer" ];then
        wget -nc -P  ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_totaltext_v2.0_train.tar --no-check-certificate
        cd ./inference/ && tar xf det_r50_vd_sast_totaltext_v2.0_train.tar && cd ../
    fi
-    if [ ${model_name} == "det_mv3_db_v2.0" ]; then
+    if [ ${model_name} == "det_mv3_db_v2_0" ]; then
        wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar  --no-check-certificate
        cd ./inference/ && tar xf det_mv3_db_v2.0_train.tar && tar xf ch_det_data_50.tar && cd ../
    fi
@@ -223,7 +232,7 @@ elif [ ${MODE} = "whole_infer" ];then
        wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_mv3_pse_v2.0_train.tar --no-check-certificate
        cd ./inference/ && tar xf det_mv3_pse_v2.0_train.tar & cd ../
    fi
-    if [ ${model_name} == "det_r50_vd_pse_v2.0" ]; then
+    if [ ${model_name} == "det_r50_vd_pse_v2_0" ]; then
        wget -nc -P ./inference/  https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_r50_vd_pse_v2.0_train.tar  --no-check-certificate
        cd ./inference/ && tar xf det_r50_vd_pse_v2.0_train.tar & cd ../
    fi
@@ -231,7 +240,7 @@ elif [ ${MODE} = "whole_infer" ];then
        wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_east_v2.0_train.tar --no-check-certificate
        cd ./inference/ && tar xf det_mv3_east_v2.0_train.tar & cd ../
    fi
-    if [ ${model_name} == "det_r50_vd_east_v2.0" ]; then
+    if [ ${model_name} == "det_r50_vd_east_v2_0" ]; then
        wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_east_v2.0_train.tar --no-check-certificate
        cd ./inference/ && tar xf det_r50_vd_east_v2.0_train.tar & cd ../
    fi

--- a/test_tipc/supplementary/readme.md
+++ b/test_tipc/supplementary/readme.md
@@ -47,6 +47,13 @@ bash test_tipc/test_train_python.sh ./test_tipc/train_infer_python_PACT.txt 'lit
 bash test_tipc/test_train_python.sh ./test_tipc/train_infer_python_FPGM.txt 'lite_train_lite_infer'
 ```
+多机多卡的运行配置文件分别为 `train_infer_python_fleet.txt`, `train_infer_python_FPGM_fleet.txt` 和 `train_infer_python_PACT_fleet.txt`。
+运行时，需要修改配置文件中的 `gpu_list:xx.xx.xx.xx,yy.yy.yy.yy;0,1`。 将 `xx.xx.xx.xx` 替换为具体的 `ip` 地址，各个`ip`地址之间用`,`分隔。 另外，和单机训练
+不同，启动多机多卡训练需要在多机的每个节点上分别运行命令。以多机多卡量化训练为例，指令如下：
+```
+bash test_tipc/test_train_python.sh ./test_tipc/train_infer_python_PACT_fleet.txt 'lite_train_lite_infer'
+```
 运行相应指令后，在`test_tipc/output`文件夹下自动会保存运行日志。如'lite_train_lite_infer'模式运行后，在test_tipc/extra_output文件夹有以下文件：
 ```

--- a/test_tipc/supplementary/test_tipc/test_train_python.sh
+++ b/test_tipc/supplementary/test_tipc/test_train_python.sh
@@ -35,7 +35,6 @@ use_share_conv_key=$(func_parser_key "${lines[13]}")
 use_share_conv_list=$(func_parser_value "${lines[13]}")
 run_train_py=$(func_parser_value "${lines[14]}")
 LOG_PATH="./test_tipc/extra_output"
 mkdir -p ${LOG_PATH}
 status_log="${LOG_PATH}/results_python.log"
@@ -98,6 +97,8 @@ if [ ${MODE} = "lite_train_lite_infer" ] || [ ${MODE} = "whole_train_whole_infer
                            cmd="${python} ${run_train_py} ${set_use_gpu}  ${set_save_model}  ${set_epoch}  ${set_pretrain} ${set_checkpoints}  ${set_autocast} ${set_batchsize}  ${set_use_custom_op} ${set_model_type} ${set_use_share_conv} ${set_amp_config}"
                        elif [ ${#ips} -le 26 ];then  # train with multi-gpu
                            cmd="${python} -m paddle.distributed.launch --gpus=${gpu} ${run_train_py} ${set_use_gpu}  ${set_save_model}  ${set_epoch}  ${set_pretrain} ${set_checkpoints}  ${set_autocast} ${set_batchsize}  ${set_use_custom_op} ${set_model_type} ${set_use_share_conv} ${set_amp_config}"
+                        else
+                            cmd="${python} -m paddle.distributed.launch --ips=${ips} --gpus=${gpu} ${run_train_py} ${set_use_gpu}  ${set_save_model}  ${set_epoch}  ${set_pretrain} ${set_checkpoints}  ${set_autocast} ${set_batchsize}  ${set_use_custom_op} ${set_model_type} ${set_use_share_conv} ${set_amp_config}"
                        fi
                        # run train

--- a/test_tipc/supplementary/test_tipc/train_infer_python_FPGM.txt
+++ b/test_tipc/supplementary/test_tipc/train_infer_python_FPGM.txt
@@ -4,9 +4,9 @@ python:python3.7
 gpu_list:0|0,1
 use_gpu:True|True
 AMP.use_amp:True|False
-epoch:lite_train_lite_infer=20|whole_train_whole_infer=1000
+epoch:lite_train_lite_infer=2|whole_train_whole_infer=1000
 save_model_dir:./output/
-TRAIN.batch_size:lite_train_lite_infer=2|whole_train_whole_infer=4
+TRAIN.batch_size:lite_train_lite_infer=1280|whole_train_whole_infer=1280
 pretrained_model:null
 checkpoints:null
 use_custom_relu:False|True

--- a/test_tipc/supplementary/test_tipc/train_infer_python_FPGM_fleet.txt
+++ b/test_tipc/supplementary/test_tipc/train_infer_python_FPGM_fleet.txt
+===========================train_params===========================
+model_name:ch_PPOCRv2_det
+python:python3.7
+gpu_list:xx.xx.xx.xx,yy.yy.yy.yy;0,1
+use_gpu:True
+AMP.use_amp:True|False
+epoch:lite_train_lite_infer=2|whole_train_whole_infer=1000
+save_model_dir:./output/
+TRAIN.batch_size:lite_train_lite_infer=1280|whole_train_whole_infer=1280
+pretrained_model:null
+checkpoints:null
+use_custom_relu:False|True
+model_type:cls|cls_distill|cls_distill_multiopt
+MODEL.siamese:False|True
+norm_train:train.py -c mv3_large_x0_5.yml -o prune_train=True
+quant_train:False
+prune_train:False
--- a/test_tipc/supplementary/test_tipc/train_infer_python_PACT.txt
+++ b/test_tipc/supplementary/test_tipc/train_infer_python_PACT.txt
@@ -4,9 +4,9 @@ python:python3.7
 gpu_list:0|0,1
 use_gpu:True|True
 AMP.use_amp:True|False
-epoch:lite_train_lite_infer=20|whole_train_whole_infer=1000
+epoch:lite_train_lite_infer=2|whole_train_whole_infer=1000
 save_model_dir:./output/
-TRAIN.batch_size:lite_train_lite_infer=2|whole_train_whole_infer=4
+TRAIN.batch_size:lite_train_lite_infer=1280|whole_train_whole_infer=1280
 pretrained_model:null
 checkpoints:null
 use_custom_relu:False|True

--- a/test_tipc/supplementary/test_tipc/train_infer_python_PACT_fleet.txt
+++ b/test_tipc/supplementary/test_tipc/train_infer_python_PACT_fleet.txt
+===========================train_params===========================
+model_name:ch_PPOCRv2_det
+python:python3.7
+gpu_list:xx.xx.xx.xx,yy.yy.yy.yy;0,1
+use_gpu:True
+AMP.use_amp:True|False
+epoch:lite_train_lite_infer=2|whole_train_whole_infer=1000
+save_model_dir:./output/
+TRAIN.batch_size:lite_train_lite_infer=1280|whole_train_whole_infer=1280
+pretrained_model:null
+checkpoints:null
+use_custom_relu:False|True
+model_type:cls|cls_distill|cls_distill_multiopt
+MODEL.siamese:False|True
+norm_train:train.py -c mv3_large_x0_5.yml -o quant_train=True
+quant_train:False
+prune_train:False
--- a/test_tipc/supplementary/test_tipc/train_infer_python_fleet.txt
+++ b/test_tipc/supplementary/test_tipc/train_infer_python_fleet.txt
+===========================train_params===========================
+model_name:ch_PPOCRv2_det
+python:python3.7
+gpu_list:xx.xx.xx.xx,yy.yy.yy.yy;0,1
+use_gpu:True
+AMP.use_amp:True|False
+epoch:lite_train_lite_infer=2|whole_train_whole_infer=1000
+save_model_dir:./output/
+TRAIN.batch_size:lite_train_lite_infer=1280|whole_train_whole_infer=1280
+pretrained_model:null
+checkpoints:null
+use_custom_relu:False|True
+model_type:cls|cls_distill|cls_distill_multiopt
+MODEL.siamese:False|True
+norm_train: train.py -c mv3_large_x0_5.yml -o
+quant_train:False
+prune_train:False
--- a/tools/infer/predict_system.py
+++ b/tools/infer/predict_system.py
@@ -24,6 +24,7 @@ os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
 import cv2
 import copy
 import numpy as np
+import json
 import time
 import logging
 from PIL import Image
@@ -128,6 +129,9 @@ def main(args):
    is_visualize = True
    font_path = args.vis_font_path
    drop_score = args.drop_score
+    draw_img_save_dir = args.draw_img_save_dir
+    os.makedirs(draw_img_save_dir, exist_ok=True)
+    save_results = []
    # warm up 10 times
    if args.warmup:
@@ -157,6 +161,14 @@ def main(args):
        for text, score in rec_res:
            logger.debug("{}, {:.3f}".format(text, score))
+        res = [{
+            "transcription": rec_res[idx][0],
+            "points": np.array(dt_boxes[idx]).astype(np.int32).tolist(),
+        } for idx in range(len(dt_boxes))]
+        save_pred = os.path.basename(image_file) + "\t" + json.dumps(
+            res, ensure_ascii=False) + "\n"
+        save_results.append(save_pred)
        if is_visualize:
            image = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
            boxes = dt_boxes
@@ -170,8 +182,6 @@ def main(args):
                scores,
                drop_score=drop_score,
                font_path=font_path)
-            draw_img_save_dir = args.draw_img_save_dir
-            os.makedirs(draw_img_save_dir, exist_ok=True)
            if flag:
                image_file = image_file[:-3] + "png"
            cv2.imwrite(
@@ -185,6 +195,9 @@ def main(args):
        text_sys.text_detector.autolog.report()
        text_sys.text_recognizer.autolog.report()
+    with open(os.path.join(draw_img_save_dir, "system_results.txt"), 'w') as f:
+        f.writelines(save_results)
 if __name__ == "__main__":
    args = utility.parse_args()

--- a/tools/program.py
+++ b/tools/program.py
@@ -146,6 +146,7 @@ def train(config,
          scaler=None):
    cal_metric_during_train = config['Global'].get('cal_metric_during_train',
                                                   False)
+    calc_epoch_interval = config['Global'].get('calc_epoch_interval', 1)
    log_smooth_window = config['Global']['log_smooth_window']
    epoch_num = config['Global']['epoch_num']
    print_batch_step = config['Global']['print_batch_step']
@@ -244,6 +245,16 @@ def train(config,
                optimizer.step()
            optimizer.clear_grad()
+            if cal_metric_during_train and epoch % calc_epoch_interval == 0:  # only rec and cls need
+                batch = [item.numpy() for item in batch]
+                if model_type in ['table', 'kie']:
+                    eval_class(preds, batch)
+                else:
+                    post_result = post_process_class(preds, batch[1])
+                    eval_class(post_result, batch)
+                metric = eval_class.get_metric()
+                train_stats.update(metric)
            train_batch_time = time.time() - reader_start
            train_batch_cost += train_batch_time
            eta_meter.update(train_batch_time)
@@ -258,16 +269,6 @@ def train(config,
            stats['lr'] = lr
            train_stats.update(stats)
-            if cal_metric_during_train:  # only rec and cls need
-                batch = [item.numpy() for item in batch]
-                if model_type in ['table', 'kie']:
-                    eval_class(preds, batch)
-                else:
-                    post_result = post_process_class(preds, batch[1])
-                    eval_class(post_result, batch)
-                metric = eval_class.get_metric()
-                train_stats.update(metric)
            if vdl_writer is not None and dist.get_rank() == 0:
                for k, v in train_stats.get().items():
                    vdl_writer.add_scalar('TRAIN/{}'.format(k), v, global_step)
@@ -277,12 +278,13 @@ def train(config,
                (global_step > 0 and global_step % print_batch_step == 0) or
                (idx >= len(train_dataloader) - 1)):
                logs = train_stats.log()
                eta_sec = ((epoch_num + 1 - epoch) * \
                    len(train_dataloader) - idx - 1) * eta_meter.avg
                eta_sec_format = str(datetime.timedelta(seconds=int(eta_sec)))
                strs = 'epoch: [{}/{}], global_step: {}, {}, avg_reader_cost: ' \
                       '{:.5f} s, avg_batch_cost: {:.5f} s, avg_samples: {}, ' \
-                       'ips: {:.5f}, eta: {}'.format(
+                       'ips: {:.5f} samples/s, eta: {}'.format(
                    epoch, epoch_num, global_step, logs,
                    train_reader_cost / print_batch_step,
                    train_batch_cost / print_batch_step,