Merge branch 'dygraph' of https://github.com/PaddlePaddle/PaddleOCR into dygraph

configs/rec/ch_PP-OCRv3/ch_PP-OCRv3_rec.yml

+Global:
+  debug: false
+  use_gpu: true
+  epoch_num: 500
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec_ppocr_v3
+  save_epoch_step: 3
+  eval_batch_step: [0, 2000]
+  cal_metric_during_train: true
+  pretrained_model:
+  checkpoints:
+  save_inference_dir:
+  use_visualdl: false
+  infer_img: doc/imgs_words/ch/word_1.jpg
+  character_dict_path: ppocr/utils/ppocr_keys_v1.txt
+  max_text_length: &max_text_length 25
+  infer_mode: false
+  use_space_char: true
+  distributed: true
+  save_res_path: ./output/rec/predicts_ppocrv3.txt
+
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: Cosine
+    learning_rate: 0.001
+    warmup_epoch: 5
+  regularizer:
+    name: L2
+    factor: 3.0e-05
+
+
+Architecture:
+  model_type: rec
+  algorithm: SVTR
+  Transform:
+  Backbone:
+    name: MobileNetV1Enhance
+    scale: 0.5
+    last_conv_stride: [1, 2]
+    last_pool_type: avg
+  Head:
+    name: MultiHead
+    head_list:
+      - CTCHead:
+          Neck:
+            name: svtr
+            dims: 64
+            depth: 2
+            hidden_dims: 120
+            use_guide: True
+          Head:
+            fc_decay: 0.00001
+      - SARHead:
+          enc_dim: 512
+          max_text_length: *max_text_length
+
+Loss:
+  name: MultiLoss
+  loss_config_list:
+    - CTCLoss:
+    - SARLoss:
+
+PostProcess:  
+  name: CTCLabelDecode
+
+Metric:
+  name: RecMetric
+  main_indicator: acc
+  ignore_space: True
+
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/
+    ext_op_transform_idx: 1
+    label_file_list:
+    - ./train_data/train_list.txt
+    transforms:
+    - DecodeImage:
+        img_mode: BGR
+        channel_first: false
+    - RecConAug:
+        prob: 0.5
+        ext_data_num: 2
+        image_shape: [48, 320, 3]
+    - RecAug:
+    - MultiLabelEncode:
+    - RecResizeImg:
+        image_shape: [3, 48, 320]
+    - KeepKeys:
+        keep_keys:
+        - image
+        - label_ctc
+        - label_sar
+        - length
+        - valid_ratio
+  loader:
+    shuffle: true
+    batch_size_per_card: 128
+    drop_last: true
+    num_workers: 4
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data
+    label_file_list:
+    - ./train_data/val_list.txt
+    transforms:
+    - DecodeImage:
+        img_mode: BGR
+        channel_first: false
+    - MultiLabelEncode:
+    - RecResizeImg:
+        image_shape: [3, 48, 320]
+    - KeepKeys:
+        keep_keys:
+        - image
+        - label_ctc
+        - label_sar
+        - length
+        - valid_ratio
+  loader:
+    shuffle: false
+    drop_last: false
+    batch_size_per_card: 128
+    num_workers: 4

configs/rec/ch_PP-OCRv3/ch_PP-OCRv3_rec_distillation.yml

+Global:
+  debug: false
+  use_gpu: true
+  epoch_num: 800
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec_ppocr_v3_distillation
+  save_epoch_step: 3
+  eval_batch_step: [0, 2000]
+  cal_metric_during_train: true
+  pretrained_model:
+  checkpoints:
+  save_inference_dir:
+  use_visualdl: false
+  infer_img: doc/imgs_words/ch/word_1.jpg
+  character_dict_path: ppocr/utils/ppocr_keys_v1.txt
+  max_text_length: &max_text_length 25
+  infer_mode: false
+  use_space_char: true
+  distributed: true
+  save_res_path: ./output/rec/predicts_ppocrv3_distillation.txt
+
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: Piecewise
+    decay_epochs : [700, 800]
+    values : [0.0005, 0.00005]
+    warmup_epoch: 5
+  regularizer:
+    name: L2
+    factor: 3.0e-05
+
+
+Architecture:
+  model_type: &model_type "rec"
+  name: DistillationModel
+  algorithm: Distillation
+  Models:
+    Teacher:
+      pretrained:
+      freeze_params: false
+      return_all_feats: true
+      model_type: *model_type
+      algorithm: SVTR
+      Transform:
+      Backbone:
+        name: MobileNetV1Enhance
+        scale: 0.5
+        last_conv_stride: [1, 2]
+        last_pool_type: avg
+      Head:
+        name: MultiHead
+        head_list:
+          - CTCHead:
+              Neck:
+                name: svtr
+                dims: 64
+                depth: 2
+                hidden_dims: 120
+                use_guide: True
+              Head:
+                fc_decay: 0.00001
+          - SARHead:
+              enc_dim: 512
+              max_text_length: *max_text_length
+    Student:
+      pretrained:
+      freeze_params: false
+      return_all_feats: true
+      model_type: *model_type
+      algorithm: SVTR
+      Transform:
+      Backbone:
+        name: MobileNetV1Enhance
+        scale: 0.5
+        last_conv_stride: [1, 2]
+        last_pool_type: avg
+      Head:
+        name: MultiHead
+        head_list:
+          - CTCHead:
+              Neck:
+                name: svtr
+                dims: 64
+                depth: 2
+                hidden_dims: 120
+                use_guide: True
+              Head:
+                fc_decay: 0.00001
+          - SARHead:
+              enc_dim: 512
+              max_text_length: *max_text_length
+Loss:
+  name: CombinedLoss
+  loss_config_list:
+  - DistillationDMLLoss:
+      weight: 1.0
+      act: "softmax"
+      use_log: true
+      model_name_pairs:
+      - ["Student", "Teacher"]
+      key: head_out
+      multi_head: True
+      dis_head: ctc
+      name: dml_ctc
+  - DistillationDMLLoss:
+      weight: 0.5
+      act: "softmax"
+      use_log: true
+      model_name_pairs:
+      - ["Student", "Teacher"]
+      key: head_out
+      multi_head: True
+      dis_head: sar
+      name: dml_sar
+  - DistillationDistanceLoss:
+      weight: 1.0
+      mode: "l2"
+      model_name_pairs:
+      - ["Student", "Teacher"]
+      key: backbone_out
+  - DistillationCTCLoss:
+      weight: 1.0
+      model_name_list: ["Student", "Teacher"]
+      key: head_out
+      multi_head: True
+  - DistillationSARLoss:
+      weight: 1.0
+      model_name_list: ["Student", "Teacher"]
+      key: head_out
+      multi_head: True
+
+PostProcess:
+  name: DistillationCTCLabelDecode
+  model_name: ["Student", "Teacher"]
+  key: head_out
+  multi_head: True
+
+Metric:
+  name: DistillationMetric
+  base_metric_name: RecMetric
+  main_indicator: acc
+  key: "Student"
+  ignore_space: True
+
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/
+    ext_op_transform_idx: 1
+    label_file_list:
+    - ./train_data/train_list.txt
+    transforms:
+    - DecodeImage:
+        img_mode: BGR
+        channel_first: false
+    - RecConAug:
+        prob: 0.5
+        ext_data_num: 2
+        image_shape: [48, 320, 3]
+    - RecAug:
+    - MultiLabelEncode:
+    - RecResizeImg:
+        image_shape: [3, 48, 320]
+    - KeepKeys:
+        keep_keys:
+        - image
+        - label_ctc
+        - label_sar
+        - length
+        - valid_ratio
+  loader:
+    shuffle: true
+    batch_size_per_card: 128
+    drop_last: true
+    num_workers: 4
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data
+    label_file_list:
+    - ./train_data/val_list.txt
+    transforms:
+    - DecodeImage:
+        img_mode: BGR
+        channel_first: false
+    - MultiLabelEncode:
+    - RecResizeImg:
+        image_shape: [3, 48, 320]
+    - KeepKeys:
+        keep_keys:
+        - image
+        - label_ctc
+        - label_sar
+        - length
+        - valid_ratio
+  loader:
+    shuffle: false
+    drop_last: false
+    batch_size_per_card: 128
+    num_workers: 4

configs/rec/rec_svtrnet.yml

+Global:
+  use_gpu: True
+  epoch_num: 20
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec/svtr/
+  save_epoch_step: 1
+  # evaluation is run every 2000 iterations after the 0th iteration
+  eval_batch_step: [0, 2000]
+  cal_metric_during_train: True
+  pretrained_model:
+  checkpoints:
+  save_inference_dir:
+  use_visualdl: False
+  infer_img: doc/imgs_words_en/word_10.png
+  # for data or label process
+  character_dict_path:
+  character_type: en
+  max_text_length: 25
+  infer_mode: False
+  use_space_char: False
+  save_res_path: ./output/rec/predicts_svtr_tiny.txt
+
+
+Optimizer:
+  name: AdamW
+  beta1: 0.9
+  beta2: 0.99
+  epsilon: 0.00000008
+  weight_decay: 0.05
+  no_weight_decay_name: norm pos_embed
+  one_dim_param_no_weight_decay: true
+  lr:
+    name: Cosine
+    learning_rate: 0.0005
+    warmup_epoch: 2
+
+Architecture:
+  model_type: rec
+  algorithm: SVTR
+  Transform:
+    name: STN_ON
+    tps_inputsize: [32, 64]
+    tps_outputsize: [32, 100]
+    num_control_points: 20
+    tps_margins: [0.05,0.05]
+    stn_activation: none
+  Backbone:
+    name: SVTRNet
+    img_size: [32, 100]
+    out_char_num: 25
+    out_channels: 192
+    patch_merging: 'Conv'
+    embed_dim: [64, 128, 256]
+    depth: [3, 6, 3]
+    num_heads: [2, 4, 8]
+    mixer: ['Local','Local','Local','Local','Local','Local','Global','Global','Global','Global','Global','Global']
+    local_mixer: [[7, 11], [7, 11], [7, 11]]
+    last_stage: True
+    prenorm: false
+  Neck:
+    name: SequenceEncoder
+    encoder_type: reshape
+  Head:
+    name: CTCHead
+
+Loss:
+  name: CTCLoss
+
+PostProcess:
+  name: CTCLabelDecode
+
+Metric:
+  name: RecMetric
+  main_indicator: acc
+
+Train:
+  dataset:
+    name: LMDBDataSet
+    data_dir: ./train_data/data_lmdb_release/training/
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - CTCLabelEncode: # Class handling label
+      - RecResizeImg:
+          character_dict_path:
+          image_shape: [3, 64, 256]
+          padding: False
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  loader:
+    shuffle: True
+    batch_size_per_card: 512
+    drop_last: True
+    num_workers: 4
+
+Eval:
+  dataset:
+    name: LMDBDataSet
+    data_dir: ./train_data/data_lmdb_release/validation/
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - CTCLabelEncode: # Class handling label
+      - RecResizeImg:
+          character_dict_path:
+          image_shape: [3, 64, 256]
+          padding: False
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 256
+    num_workers: 2

deploy/cpp_infer/readme.md

-- [Server-side C++ Inference](#server-side-c-inference)
-  - [1. Prepare the Environment](#1-prepare-the-environment)
-    - [Environment](#environment)
-    - [1.1 Compile OpenCV](#11-compile-opencv)
-    - [1.2 Compile or Download or the Paddle Inference Library](#12-compile-or-download-or-the-paddle-inference-library)
-      - [1.2.1 Direct download and installation](#121-direct-download-and-installation)
-      - [1.2.2 Compile the inference source code](#122-compile-the-inference-source-code)
-  - [2. Compile and Run the Demo](#2-compile-and-run-the-demo)
-    - [2.1 Export the inference model](#21-export-the-inference-model)
-    - [2.2 Compile PaddleOCR C++ inference demo](#22-compile-paddleocr-c-inference-demo)
-    - [Run the demo](#run-the-demo)
-        - [1. det+cls+rec：](#1-detclsrec)
-        - [2. det+rec：](#2-detrec)
-        - [3. det](#3-det)
-        - [4. cls+rec：](#4-clsrec)
-        - [5. rec](#5-rec)
-        - [6. cls](#6-cls)
-  - [3. FAQ](#3-faq)
+English | [简体中文](readme_ch.md)
 
 # Server-side C++ Inference
 
-This chapter introduces the C++ deployment steps of the PaddleOCR model. The corresponding Python predictive deployment method refers to [document](../../doc/doc_ch/inference.md).
-C++ is better than python in terms of performance. Therefore, in CPU and GPU deployment scenarios, C++ deployment is mostly used.
+- [1. Prepare the Environment](#1)
+    - [1.1 Environment](#11)
+    - [1.2 Compile OpenCV](#12)
+    - [1.3 Compile or Download or the Paddle Inference Library](#13)
+- [2. Compile and Run the Demo](#2)
+    - [2.1 Export the inference model](#21)
+    - [2.2 Compile PaddleOCR C++ inference demo](#22)
+    - [2.3 Run the demo](#23)
+- [3. FAQ](#3)
+
+
+This chapter introduces the C++ deployment steps of the PaddleOCR model. C++ is better than Python in terms of performance. Therefore, in CPU and GPU deployment scenarios, C++ deployment is mostly used.
 This section will introduce how to configure the C++ environment and deploy PaddleOCR in Linux (CPU\GPU) environment. For Windows deployment please refer to [Windows](./docs/windows_vs2019_build.md) compilation guidelines.
 
 
+<a name="1"></a>
 ## 1. Prepare the Environment
 
-### Environment
+<a name="11"></a>
+### 1.1 Environment
 
 - Linux, docker is recommended.
 - Windows.
 
 
-### 1.1 Compile OpenCV
+<a name="12"></a>
+### 1.2 Compile OpenCV
 
 * First of all, you need to download the source code compiled package in the Linux environment from the OpenCV official website. Taking OpenCV 3.4.7 as an example, the download command is as follows.
 
@@ -92,11 +88,12 @@ opencv3/
 |-- share
 ```
 
-### 1.2 Compile or Download or the Paddle Inference Library
+<a name="13"></a>
+### 1.3 Compile or Download or the Paddle Inference Library
 
 * There are 2 ways to obtain the Paddle inference library, described in detail below.
 
-#### 1.2.1 Direct download and installation
+#### 1.3.1 Direct download and installation
 
 [Paddle inference library official website](https://paddleinference.paddlepaddle.org.cn/user_guides/download_lib.html#linux). You can review and select the appropriate version of the inference library on the official website.
 
@@ -109,7 +106,7 @@ tar -xf paddle_inference.tgz
 
 Finally you will see the the folder of `paddle_inference/` in the current path.
 
-#### 1.2.2 Compile the inference source code
+#### 1.3.2 Compile the inference source code
 * If you want to get the latest Paddle inference library features, you can download the latest code from Paddle GitHub repository and compile the inference library from the source code. It is recommended to download the inference library with paddle version greater than or equal to 2.0.1.
 * You can refer to [Paddle inference library] (https://www.paddlepaddle.org.cn/documentation/docs/en/advanced_guide/inference_deployment/inference/build_and_install_lib_en.html) to get the Paddle source code from GitHub, and then compile To generate the latest inference library. The method of using git to access the code is as follows.
 
@@ -155,8 +152,10 @@ build/paddle_inference_install_dir/
 `paddle` is the Paddle library required for C++ prediction later, and `version.txt` contains the version information of the current inference library.
 
 
+<a name="2"></a>
 ## 2. Compile and Run the Demo
 
+<a name="21"></a>
 ### 2.1 Export the inference model
 
 * You can refer to [Model inference](../../doc/doc_ch/inference.md) and export the inference model. After the model is exported, assuming it is placed in the `inference` directory, the directory structure is as follows.
@@ -175,9 +174,9 @@ inference/
 ```
 
 
+<a name="22"></a>
 ### 2.2 Compile PaddleOCR C++ inference demo
 
-
 * The compilation commands are as follows. The addresses of Paddle C++ inference library, opencv and other Dependencies need to be replaced with the actual addresses on your own machines.
 
 ```shell
@@ -201,7 +200,9 @@ or the generated Paddle inference library path (`build/paddle_inference_install_
 * After the compilation is completed, an executable file named `ppocr` will be generated in the `build` folder.
 
 
-### Run the demo
+<a name="23"></a>
+### 2.3 Run the demo
+
 Execute the built executable file:
 ```shell
 ./build/ppocr [--param1] [--param2] [...]
@@ -342,6 +343,7 @@ The detection visualized image saved in ./output//12.jpg
 ```
 
 
+<a name="3"></a>
 ## 3. FAQ
 
  1.  Encountered the error `unable to access 'https://github.com/LDOUBLEV/AutoLog.git/': gnutls_handshake() failed: The TLS connection was non-properly terminated.`, change the github address in `deploy/cpp_infer/external-cmake/auto-log.cmake` to the https://gitee.com/Double_V/AutoLog address.

deploy/cpp_infer/readme_ch.md

-- [服务器端C++预测](#服务器端c预测)
-  - [1. 准备环境](#1-准备环境)
-    - [1.0 运行准备](#10-运行准备)
-    - [1.1 编译opencv库](#11-编译opencv库)
-    - [1.2 下载或者编译Paddle预测库](#12-下载或者编译paddle预测库)
-      - [1.2.1 直接下载安装](#121-直接下载安装)
-      - [1.2.2 预测库源码编译](#122-预测库源码编译)
-  - [2 开始运行](#2-开始运行)
-    - [2.1 将模型导出为inference model](#21-将模型导出为inference-model)
-    - [2.2 编译PaddleOCR C++预测demo](#22-编译paddleocr-c预测demo)
-    - [2.3 运行demo](#23-运行demo)
-        - [1. 检测+分类+识别：](#1-检测分类识别)
-        - [2. 检测+识别：](#2-检测识别)
-        - [3. 检测：](#3-检测)
-        - [4. 分类+识别：](#4-分类识别)
-        - [5. 识别：](#5-识别)
-        - [6. 分类：](#6-分类)
-  - [3. FAQ](#3-faq)
+[English](readme.md) | 简体中文
 
 # 服务器端C++预测
 
-本章节介绍PaddleOCR 模型的的C++部署方法，与之对应的python预测部署方式参考[文档](../../doc/doc_ch/inference.md)。
-C++在性能计算上优于python，因此，在大多数CPU、GPU部署场景，多采用C++的部署方式，本节将介绍如何在Linux\Windows (CPU\GPU)环境下配置C++环境并完成
-PaddleOCR模型部署。
+- [1. 准备环境](#1)
+    - [1.1 运行准备](#11)
+    - [1.2 编译opencv库](#12)
+    - [1.3 下载或者编译Paddle预测库](#13)
+- [2 开始运行](#2)
+    - [2.1 准备模型](#21)
+    - [2.2 编译PaddleOCR C++预测demo](#22)
+    - [2.3 运行demo](#23)
+- [3. FAQ](#3)
+
+本章节介绍PaddleOCR 模型的的C++部署方法。C++在性能计算上优于Python，因此，在大多数CPU、GPU部署场景，多采用C++的部署方式，本节将介绍如何在Linux\Windows (CPU\GPU)环境下配置C++环境并完成PaddleOCR模型部署。
 
 
 <a name="1"></a>
 
 ## 1. 准备环境
 
-<a name="10"></a>
+<a name="11"></a>
 
-### 1.0 运行准备
+### 1.1 运行准备
 
 - Linux环境，推荐使用docker。
 - Windows环境。
 
 * 该文档主要介绍基于Linux环境的PaddleOCR C++预测流程，如果需要在Windows下基于预测库进行C++预测，具体编译方法请参考[Windows下编译教程](./docs/windows_vs2019_build.md)
 
-<a name="11"></a>
+<a name="12"></a>
 
-### 1.1 编译opencv库
+### 1.2 编译opencv库
 
 * 首先需要从opencv官网上下载在Linux环境下源码编译的包，以opencv3.4.7为例，下载命令如下。
 
@@ -103,35 +94,38 @@ opencv3/
 |-- share
 ```
 
-<a name="12"></a>
-
-### 1.2 下载或者编译Paddle预测库
+<a name="13"></a>
 
-* 有2种方式获取Paddle预测库，下面进行详细介绍。
+### 1.3 下载或者编译Paddle预测库
 
+可以选择直接下载安装或者从源码编译，下文分别进行具体说明。
 
-#### 1.2.1 直接下载安装
+<a name="131"></a>
+#### 1.3.1 直接下载安装
 
-* [Paddle预测库官网](https://paddleinference.paddlepaddle.org.cn/user_guides/download_lib.html#linux) 上提供了不同cuda版本的Linux预测库，可以在官网查看并选择合适的预测库版本（*建议选择paddle版本>=2.0.1版本的预测库* ）。
+[Paddle预测库官网](https://paddleinference.paddlepaddle.org.cn/user_guides/download_lib.html#linux) 上提供了不同cuda版本的Linux预测库，可以在官网查看并选择合适的预测库版本（*建议选择paddle版本>=2.0.1版本的预测库* ）。
 
-* 下载之后使用下面的方法解压。
+下载之后解压:
 
-```
+```shell
 tar -xf paddle_inference.tgz
 ```
 
 最终会在当前的文件夹中生成`paddle_inference/`的子文件夹。
 
-#### 1.2.2 预测库源码编译
-* 如果希望获取最新预测库特性，可以从Paddle github上克隆最新代码，源码编译预测库。
-* 可以参考[Paddle预测库安装编译说明](https://www.paddlepaddle.org.cn/documentation/docs/zh/2.0/guides/05_inference_deployment/inference/build_and_install_lib_cn.html#congyuanmabianyi) 的说明，从github上获取Paddle代码，然后进行编译，生成最新的预测库。使用git获取代码方法如下。
+<a name="132"></a>
+#### 1.3.2 预测库源码编译
+
+如果希望获取最新预测库特性，可以从github上克隆最新Paddle代码进行编译，生成最新的预测库。
+
+* 使用git获取代码:
 
 ```shell
 git clone https://github.com/PaddlePaddle/Paddle.git
 git checkout develop
 ```
 
-* 进入Paddle目录后，编译方法如下。
+* 进入Paddle目录，进行编译:
 
 ```shell
 rm -rf build
@@ -151,7 +145,7 @@ make -j
 make inference_lib_dist
 ```
 
-更多编译参数选项介绍可以参考[文档说明](https://www.paddlepaddle.org.cn/documentation/docs/zh/2.0/guides/05_inference_deployment/inference/build_and_install_lib_cn.html#congyuanmabianyi)。
+更多编译参数选项介绍可以参考[Paddle预测库编译文档](https://www.paddlepaddle.org.cn/documentation/docs/zh/2.0/guides/05_inference_deployment/inference/build_and_install_lib_cn.html#congyuanmabianyi)。
 
 
 * 编译完成之后，可以在`build/paddle_inference_install_dir/`文件下看到生成了以下文件及文件夹。
@@ -168,13 +162,13 @@ build/paddle_inference_install_dir/
 
 <a name="2"></a>
 
-## 2 开始运行
+## 2. 开始运行
 
 <a name="21"></a>
 
-### 2.1 将模型导出为inference model
+### 2.1 准备模型
 
-* 可以参考[模型预测章节](../../doc/doc_ch/inference.md)，导出inference model，用于模型预测。模型导出之后，假设放在`inference`目录下，则目录结构如下。
+直接下载PaddleOCR提供的推理模型，或者参考[模型预测章节](../../doc/doc_ch/inference_ppocr.md)，将训练好的模型导出为推理模型。模型导出之后，假设放在`inference`目录下，则目录结构如下。
 
 ```
 inference/
@@ -193,13 +187,13 @@ inference/
 
 ### 2.2 编译PaddleOCR C++预测demo
 
-* 编译命令如下，其中Paddle C++预测库、opencv等其他依赖库的地址需要换成自己机器上的实际地址。
+编译命令如下，其中Paddle C++预测库、opencv等其他依赖库的地址需要换成自己机器上的实际地址。
 
 ```shell
 sh tools/build.sh
 ```
 
-* 具体的，需要修改`tools/build.sh`中环境路径，相关内容如下：
+具体的，需要修改`tools/build.sh`中环境路径，相关内容如下：
 
 ```shell
 OPENCV_DIR=your_opencv_dir
@@ -211,12 +205,14 @@ CUDNN_LIB_DIR=/your_cudnn_lib_dir
 其中，`OPENCV_DIR`为opencv编译安装的地址；`LIB_DIR`为下载(`paddle_inference`文件夹)或者编译生成的Paddle预测库地址(`build/paddle_inference_install_dir`文件夹)；`CUDA_LIB_DIR`为cuda库文件地址，在docker中为`/usr/local/cuda/lib64`；`CUDNN_LIB_DIR`为cudnn库文件地址，在docker中为`/usr/lib/x86_64-linux-gnu/`。**注意：以上路径都写绝对路径，不要写相对路径。**
 
 
-* 编译完成之后，会在`build`文件夹下生成一个名为`ppocr`的可执行文件。
+编译完成之后，会在`build`文件夹下生成一个名为`ppocr`的可执行文件。
 
 <a name="23"></a>
 
 ### 2.3 运行demo
 
+本demo支持系统串联调用，也支持单个功能的调用，如，只使用检测或识别功能。
+
 运行方式：  
 ```shell
 ./build/ppocr [--param1] [--param2] [...]
@@ -354,6 +350,7 @@ predict img: ../../doc/imgs/12.jpg
 The detection visualized image saved in ./output//12.jpg
 ```
 
+<a name="3"></a>
 ## 3. FAQ
 
  1.  遇到报错 `unable to access 'https://github.com/LDOUBLEV/AutoLog.git/': gnutls_handshake() failed: The TLS connection was non-properly terminated.`， 将 `deploy/cpp_infer/external-cmake/auto-log.cmake` 中的github地址改为 https://gitee.com/Double_V/AutoLog 地址即可。

deploy/slim/quantization/quant.py

@@ -137,7 +137,7 @@ def main(config, device, logger, vdl_writer):
         config['Optimizer'],
         epochs=config['Global']['epoch_num'],
         step_each_epoch=len(train_dataloader),
-        parameters=model.parameters())
+        model=model)
 
     # resume PACT training process
     if config["Global"]["checkpoints"] is not None:

doc/doc_ch/algorithm_det_db.md

@@ -47,13 +47,13 @@
 ### 4.1 Python推理
 首先将DB文本检测训练过程中保存的模型，转换成inference model。以基于Resnet50_vd骨干网络，在ICDAR2015英文数据集训练的模型为例（ [模型下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_v2.0_train.tar) )，可以使用如下命令进行转换：
 
-```
+```shell
 python3 tools/export_model.py -c configs/det/det_r50_vd_db.yml -o Global.pretrained_model=./det_r50_vd_db_v2.0_train/best_accuracy  Global.save_inference_dir=./inference/det_db
 ```
 
 DB文本检测模型推理，可以执行如下命令：
 
-```
+```shell
 python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_db/"
 ```
 
@@ -65,15 +65,20 @@ python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img_10.jpg" --det_
 
 <a name="4-2"></a>
 ### 4.2 C++推理
-敬请期待
+
+准备好推理模型后，参考[cpp infer](../../deploy/cpp_infer/)教程进行操作即可。
 
 <a name="4-3"></a>
 ### 4.3 Serving服务化部署
-敬请期待
+
+准备好推理模型后，参考[pdserving](../../deploy/pdserving/)教程进行Serving服务化部署，包括Python Serving和C++ Serving两种模式。
 
 <a name="4-4"></a>
 ### 4.4 更多推理部署
-敬请期待
+
+DB模型还支持以下推理部署方式：
+
+- Paddle2ONNX推理：准备好推理模型后，参考[paddle2onnx](../../deploy/paddle2onnx/)教程操作。
 
 <a name="5"></a>
 ## 5. FAQ

doc/doc_en/algorithm_det_db_en.md

@@ -14,4 +14,86 @@
 - [5. FAQ](#5)
 
 <a name="1"></a>
-## 1. Introduction
\ No newline at end of file
+## 1. Introduction
+
+Paper:
+> [Real-time Scene Text Detection with Differentiable Binarization](https://arxiv.org/abs/1911.08947)
+> Liao, Minghui and Wan, Zhaoyi and Yao, Cong and Chen, Kai and Bai, Xiang
+> AAAI, 2020
+
+On the ICDAR2015 dataset, the text detection result is as follows:
+
+|Model|Backbone|Configuration|Precision|Recall|Hmean|Download|
+| --- | --- | --- | --- | --- | --- | --- |
+|DB|ResNet50_vd|configs/det/det_r50_vd_db.yml|86.41%|78.72%|82.38%|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_v2.0_train.tar)|
+|DB|MobileNetV3|configs/det/det_mv3_db.yml|77.29%|73.08%|75.12%|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar)|
+
+
+<a name="2"></a>
+## 2. Environment
+Please prepare your environment referring to [prepare the environment](./environment_en.md) and [clone the repo](./clone_en.md).
+
+
+<a name="3"></a>
+## 3. Model Training / Evaluation / Prediction
+
+Please refer to [text detection training tutorial](./detection_en.md). 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
+
+<a name="4-1"></a>
+### 4.1 Python Inference
+First, convert the model saved in the DB text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as example ([model download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_v2.0_train.tar)), you can use the following command to convert:
+
+```shell
+python3 tools/export_model.py -c configs/det/det_r50_vd_db.yml -o Global.pretrained_model=./det_r50_vd_db_v2.0_train/best_accuracy  Global.save_inference_dir=./inference/det_db
+```
+
+DB text detection model inference, you can execute the following command:
+
+```shell
+python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_db/"
+```
+
+The visualized text detection results are saved to the `./inference_results` folder by default, and the name of the result file is prefixed with 'det_res'. Examples of results are as follows:
+
+![](../imgs_results/det_res_img_10_db.jpg)
+
+**Note**: Since the ICDAR2015 dataset has only 1,000 training images, mainly for English scenes, the above model has very poor detection result on Chinese text images.
+
+
+<a name="4-2"></a>
+### 4.2 C++ Inference
+
+With the inference model prepared, refer to the [cpp infer](../../deploy/cpp_infer/) tutorial for C++ inference.
+
+<a name="4-3"></a>
+### 4.3 Serving
+
+With the inference model prepared, refer to the [pdserving](../../deploy/pdserving/) tutorial for service deployment by Paddle Serving.
+
+<a name="4-4"></a>
+### 4.4 More
+
+More deployment schemes supported for DB:
+
+- Paddle2ONNX: with the inference model prepared, please refer to the [paddle2onnx](../../deploy/paddle2onnx/) tutorial.
+
+<a name="5"></a>
+## 5. FAQ
+
+
+## Citation
+
+```bibtex
+@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},
+  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
+  volume={34},
+  number={07},
+  pages={11474--11481},
+  year={2020}
+}
+```
\ No newline at end of file

ppocr/data/imaug/__init__.py

@@ -22,8 +22,8 @@ from .make_shrink_map import MakeShrinkMap
 from .random_crop_data import EastRandomCropData, RandomCropImgMask
 from .make_pse_gt import MakePseGt
 
-from .rec_img_aug import RecAug, RecResizeImg, ClsResizeImg, \
-    SRNRecResizeImg, NRTRRecResizeImg, SARRecResizeImg, PRENResizeImg
+from .rec_img_aug import RecAug, RecConAug, RecResizeImg, ClsResizeImg, \
+    SRNRecResizeImg, NRTRRecResizeImg, SARRecResizeImg, PRENResizeImg, SVTRRecResizeImg
 from .randaugment import RandAugment
 from .copy_paste import CopyPaste
 from .ColorJitter import ColorJitter

ppocr/data/imaug/label_ops.py

@@ -22,6 +22,7 @@ import numpy as np
 import string
 from shapely.geometry import LineString, Point, Polygon
 import json
+import copy
 
 from ppocr.utils.logging import get_logger
 
@@ -112,14 +113,14 @@ class BaseRecLabelEncode(object):
             dict_character = list(self.character_str)
             self.lower = True
         else:
-            self.character_str = ""
+            self.character_str = []
             with open(character_dict_path, "rb") as fin:
                 lines = fin.readlines()
                 for line in lines:
                     line = line.decode('utf-8').strip("\n").strip("\r\n")
-                    self.character_str += line
+                    self.character_str.append(line)
             if use_space_char:
-                self.character_str += " "
+                self.character_str.append(" ")
             dict_character = list(self.character_str)
         dict_character = self.add_special_char(dict_character)
         self.dict = {}
@@ -1007,3 +1008,34 @@ class VQATokenLabelEncode(object):
             gt_label.extend([self.label2id_map[("i-" + label).upper()]] *
                             (len(encode_res["input_ids"]) - 1))
         return gt_label
+
+
+class MultiLabelEncode(BaseRecLabelEncode):
+    def __init__(self,
+                 max_text_length,
+                 character_dict_path=None,
+                 use_space_char=False,
+                 **kwargs):
+        super(MultiLabelEncode, self).__init__(
+            max_text_length, character_dict_path, use_space_char)
+
+        self.ctc_encode = CTCLabelEncode(max_text_length, character_dict_path,
+                                         use_space_char, **kwargs)
+        self.sar_encode = SARLabelEncode(max_text_length, character_dict_path,
+                                         use_space_char, **kwargs)
+
+    def __call__(self, data):
+
+        data_ctc = copy.deepcopy(data)
+        data_sar = copy.deepcopy(data)
+        data_out = dict()
+        data_out['img_path'] = data.get('img_path', None)
+        data_out['image'] = data['image']
+        ctc = self.ctc_encode.__call__(data_ctc)
+        sar = self.sar_encode.__call__(data_sar)
+        if ctc is None or sar is None:
+            return None
+        data_out['label_ctc'] = ctc['label']
+        data_out['label_sar'] = sar['label']
+        data_out['length'] = ctc['length']
+        return data_out

ppocr/data/imaug/rec_img_aug.py

@@ -16,6 +16,7 @@ import math
 import cv2
 import numpy as np
 import random
+import copy
 from PIL import Image
 from .text_image_aug import tia_perspective, tia_stretch, tia_distort
 
@@ -32,13 +33,56 @@ class RecAug(object):
         return data
 
 
+class RecConAug(object):
+    def __init__(self,
+                 prob=0.5,
+                 image_shape=(32, 320, 3),
+                 max_text_length=25,
+                 ext_data_num=1,
+                 **kwargs):
+        self.ext_data_num = ext_data_num
+        self.prob = prob
+        self.max_text_length = max_text_length
+        self.image_shape = image_shape
+        self.max_wh_ratio = self.image_shape[1] / self.image_shape[0]
+
+    def merge_ext_data(self, data, ext_data):
+        ori_w = round(data['image'].shape[1] / data['image'].shape[0] *
+                      self.image_shape[0])
+        ext_w = round(ext_data['image'].shape[1] / ext_data['image'].shape[0] *
+                      self.image_shape[0])
+        data['image'] = cv2.resize(data['image'], (ori_w, self.image_shape[0]))
+        ext_data['image'] = cv2.resize(ext_data['image'],
+                                       (ext_w, self.image_shape[0]))
+        data['image'] = np.concatenate(
+            [data['image'], ext_data['image']], axis=1)
+        data["label"] += ext_data["label"]
+        return data
+
+    def __call__(self, data):
+        rnd_num = random.random()
+        if rnd_num > self.prob:
+            return data
+        for idx, ext_data in enumerate(data["ext_data"]):
+            if len(data["label"]) + len(ext_data[
+                    "label"]) > self.max_text_length:
+                break
+            concat_ratio = data['image'].shape[1] / data['image'].shape[
+                0] + ext_data['image'].shape[1] / ext_data['image'].shape[0]
+            if concat_ratio > self.max_wh_ratio:
+                break
+            data = self.merge_ext_data(data, ext_data)
+        data.pop("ext_data")
+        return data
+
+
 class ClsResizeImg(object):
     def __init__(self, image_shape, **kwargs):
         self.image_shape = image_shape
 
     def __call__(self, data):
         img = data['image']
-        norm_img = resize_norm_img(img, self.image_shape)
+        norm_img, _ = resize_norm_img(img, self.image_shape)
         data['image'] = norm_img
         return data
 
@@ -98,10 +142,13 @@ class RecResizeImg(object):
     def __call__(self, data):
         img = data['image']
         if self.infer_mode and self.character_dict_path is not None:
-            norm_img = resize_norm_img_chinese(img, self.image_shape)
+            norm_img, valid_ratio = resize_norm_img_chinese(img,
+                                                            self.image_shape)
         else:
-            norm_img = resize_norm_img(img, self.image_shape, self.padding)
+            norm_img, valid_ratio = resize_norm_img(img, self.image_shape,
+                                                    self.padding)
         data['image'] = norm_img
+        data['valid_ratio'] = valid_ratio
         return data
 
 
@@ -160,6 +207,25 @@ class PRENResizeImg(object):
         return data
 
 
+class SVTRRecResizeImg(object):
+    def __init__(self,
+                 image_shape,
+                 infer_mode=False,
+                 character_dict_path='./ppocr/utils/ppocr_keys_v1.txt',
+                 padding=True,
+                 **kwargs):
+        self.image_shape = image_shape
+        self.infer_mode = infer_mode
+        self.character_dict_path = character_dict_path
+        self.padding = padding
+
+    def __call__(self, data):
+        img = data['image']
+        norm_img = resize_norm_img_svtr(img, self.image_shape, self.padding)
+        data['image'] = norm_img
+        return data
+
+
 def resize_norm_img_sar(img, image_shape, width_downsample_ratio=0.25):
     imgC, imgH, imgW_min, imgW_max = image_shape
     h = img.shape[0]
@@ -220,7 +286,8 @@ def resize_norm_img(img, image_shape, padding=True):
     resized_image /= 0.5
     padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32)
     padding_im[:, :, 0:resized_w] = resized_image
-    return padding_im
+    valid_ratio = min(1.0, float(resized_w / imgW))
+    return padding_im, valid_ratio
 
 
 def resize_norm_img_chinese(img, image_shape):
@@ -230,7 +297,7 @@ def resize_norm_img_chinese(img, image_shape):
     h, w = img.shape[0], img.shape[1]
     ratio = w * 1.0 / h
     max_wh_ratio = max(max_wh_ratio, ratio)
-    imgW = int(32 * max_wh_ratio)
+    imgW = int(imgH * max_wh_ratio)
     if math.ceil(imgH * ratio) > imgW:
         resized_w = imgW
     else:
@@ -246,7 +313,8 @@ def resize_norm_img_chinese(img, image_shape):
     resized_image /= 0.5
     padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32)
     padding_im[:, :, 0:resized_w] = resized_image
-    return padding_im
+    valid_ratio = min(1.0, float(resized_w / imgW))
+    return padding_im, valid_ratio
 
 
 def resize_norm_img_srn(img, image_shape):
@@ -276,6 +344,58 @@ def resize_norm_img_srn(img, image_shape):
     return np.reshape(img_black, (c, row, col)).astype(np.float32)
 
 
+def resize_norm_img_svtr(img, image_shape, padding=False):
+    imgC, imgH, imgW = image_shape
+    h = img.shape[0]
+    w = img.shape[1]
+    if not padding:
+        if h > 2.0 * w:
+            image = Image.fromarray(img)
+            image1 = image.rotate(90, expand=True)
+            image2 = image.rotate(-90, expand=True)
+            img1 = np.array(image1)
+            img2 = np.array(image2)
+        else:
+            img1 = copy.deepcopy(img)
+            img2 = copy.deepcopy(img)
+
+        resized_image = cv2.resize(
+            img, (imgW, imgH), interpolation=cv2.INTER_LINEAR)
+        resized_image1 = cv2.resize(
+            img1, (imgW, imgH), interpolation=cv2.INTER_LINEAR)
+        resized_image2 = cv2.resize(
+            img2, (imgW, imgH), interpolation=cv2.INTER_LINEAR)
+        resized_w = imgW
+    else:
+        ratio = w / float(h)
+        if math.ceil(imgH * ratio) > imgW:
+            resized_w = imgW
+        else:
+            resized_w = int(math.ceil(imgH * ratio))
+        resized_image = cv2.resize(img, (resized_w, imgH))
+    resized_image = resized_image.astype('float32')
+    resized_image1 = resized_image1.astype('float32')
+    resized_image2 = resized_image2.astype('float32')
+    if image_shape[0] == 1:
+        resized_image = resized_image / 255
+        resized_image = resized_image[np.newaxis, :]
+    else:
+        resized_image = resized_image.transpose((2, 0, 1)) / 255
+        resized_image1 = resized_image1.transpose((2, 0, 1)) / 255
+        resized_image2 = resized_image2.transpose((2, 0, 1)) / 255
+    resized_image -= 0.5
+    resized_image /= 0.5
+    resized_image1 -= 0.5
+    resized_image1 /= 0.5
+    resized_image2 -= 0.5
+    resized_image2 /= 0.5
+    padding_im = np.zeros((3, imgC, imgH, imgW), dtype=np.float32)
+    padding_im[0, :, :, 0:resized_w] = resized_image
+    padding_im[1, :, :, 0:resized_w] = resized_image1
+    padding_im[2, :, :, 0:resized_w] = resized_image2
+    return padding_im
+
+
 def srn_other_inputs(image_shape, num_heads, max_text_length):
 
     imgC, imgH, imgW = image_shape

ppocr/data/simple_dataset.py

@@ -49,7 +49,8 @@ class SimpleDataSet(Dataset):
         if self.mode == "train" and self.do_shuffle:
             self.shuffle_data_random()
         self.ops = create_operators(dataset_config['transforms'], global_config)
-
+        self.ext_op_transform_idx = dataset_config.get("ext_op_transform_idx",
+                                                       2)
         self.need_reset = True in [x < 1 for x in ratio_list]
 
     def get_image_info_list(self, file_list, ratio_list):
@@ -87,7 +88,7 @@ class SimpleDataSet(Dataset):
             if hasattr(op, 'ext_data_num'):
                 ext_data_num = getattr(op, 'ext_data_num')
                 break
-        load_data_ops = self.ops[:2]
+        load_data_ops = self.ops[:self.ext_op_transform_idx]
         ext_data = []
 
         while len(ext_data) < ext_data_num:
@@ -108,8 +109,11 @@ class SimpleDataSet(Dataset):
                 data['image'] = img
             data = transform(data, load_data_ops)
 
-            if data is None or data['polys'].shape[1] != 4:
+            if data is None:
                 continue
+            if 'polys' in data.keys():
+                if data['polys'].shape[1] != 4:
+                    continue
             ext_data.append(data)
         return ext_data
 

ppocr/losses/__init__.py

@@ -34,6 +34,7 @@ from .rec_nrtr_loss import NRTRLoss
 from .rec_sar_loss import SARLoss
 from .rec_aster_loss import AsterLoss
 from .rec_pren_loss import PRENLoss
+from .rec_multi_loss import MultiLoss
 
 # cls loss
 from .cls_loss import ClsLoss
@@ -60,7 +61,7 @@ def build_loss(config):
         'DBLoss', 'PSELoss', 'EASTLoss', 'SASTLoss', 'FCELoss', 'CTCLoss',
         'ClsLoss', 'AttentionLoss', 'SRNLoss', 'PGLoss', 'CombinedLoss',
         'NRTRLoss', 'TableAttentionLoss', 'SARLoss', 'AsterLoss', 'SDMGRLoss',
-        'VQASerTokenLayoutLMLoss', 'LossFromOutput', 'PRENLoss'
+        'VQASerTokenLayoutLMLoss', 'LossFromOutput', 'PRENLoss', 'MultiLoss'
     ]
     config = copy.deepcopy(config)
     module_name = config.pop('name')

ppocr/losses/basic_loss.py

@@ -106,8 +106,8 @@ class DMLLoss(nn.Layer):
 
     def forward(self, out1, out2):
         if self.act is not None:
-            out1 = self.act(out1)
-            out2 = self.act(out2)
+            out1 = self.act(out1) + 1e-10
+            out2 = self.act(out2) + 1e-10
         if self.use_log:
             # for recognition distillation, log is needed for feature map
             log_out1 = paddle.log(out1)

ppocr/losses/combined_loss.py

@@ -18,8 +18,10 @@ import paddle.nn as nn
 from .rec_ctc_loss import CTCLoss
 from .center_loss import CenterLoss
 from .ace_loss import ACELoss
+from .rec_sar_loss import SARLoss
 
 from .distillation_loss import DistillationCTCLoss
+from .distillation_loss import DistillationSARLoss
 from .distillation_loss import DistillationDMLLoss
 from .distillation_loss import DistillationDistanceLoss, DistillationDBLoss, DistillationDilaDBLoss
 

ppocr/losses/distillation_loss.py

@@ -18,6 +18,7 @@ import numpy as np
 import cv2
 
 from .rec_ctc_loss import CTCLoss
+from .rec_sar_loss import SARLoss
 from .basic_loss import DMLLoss
 from .basic_loss import DistanceLoss
 from .det_db_loss import DBLoss
@@ -46,11 +47,15 @@ class DistillationDMLLoss(DMLLoss):
                  act=None,
                  use_log=False,
                  key=None,
+                 multi_head=False,
+                 dis_head='ctc',
                  maps_name=None,
                  name="dml"):
         super().__init__(act=act, use_log=use_log)
         assert isinstance(model_name_pairs, list)
         self.key = key
+        self.multi_head = multi_head
+        self.dis_head = dis_head
         self.model_name_pairs = self._check_model_name_pairs(model_name_pairs)
         self.name = name
         self.maps_name = self._check_maps_name(maps_name)
@@ -97,7 +102,11 @@ class DistillationDMLLoss(DMLLoss):
                 out2 = out2[self.key]
 
             if self.maps_name is None:
-                loss = super().forward(out1, out2)
+                if self.multi_head:
+                    loss = super().forward(out1[self.dis_head],
+                                           out2[self.dis_head])
+                else:
+                    loss = super().forward(out1, out2)
                 if isinstance(loss, dict):
                     for key in loss:
                         loss_dict["{}_{}_{}_{}".format(key, pair[0], pair[1],
@@ -123,11 +132,16 @@ class DistillationDMLLoss(DMLLoss):
 
 
 class DistillationCTCLoss(CTCLoss):
-    def __init__(self, model_name_list=[], key=None, name="loss_ctc"):
+    def __init__(self,
+                 model_name_list=[],
+                 key=None,
+                 multi_head=False,
+                 name="loss_ctc"):
         super().__init__()
         self.model_name_list = model_name_list
         self.key = key
         self.name = name
+        self.multi_head = multi_head
 
     def forward(self, predicts, batch):
         loss_dict = dict()
@@ -135,7 +149,45 @@ class DistillationCTCLoss(CTCLoss):
             out = predicts[model_name]
             if self.key is not None:
                 out = out[self.key]
-            loss = super().forward(out, batch)
+            if self.multi_head:
+                assert 'ctc' in out, 'multi head has multi out'
+                loss = super().forward(out['ctc'], batch[:2] + batch[3:])
+            else:
+                loss = super().forward(out, batch)
+            if isinstance(loss, dict):
+                for key in loss:
+                    loss_dict["{}_{}_{}".format(self.name, model_name,
+                                                idx)] = loss[key]
+            else:
+                loss_dict["{}_{}".format(self.name, model_name)] = loss
+        return loss_dict
+
+
+class DistillationSARLoss(SARLoss):
+    def __init__(self,
+                 model_name_list=[],
+                 key=None,
+                 multi_head=False,
+                 name="loss_sar",
+                 **kwargs):
+        ignore_index = kwargs.get('ignore_index', 92)
+        super().__init__(ignore_index=ignore_index)
+        self.model_name_list = model_name_list
+        self.key = key
+        self.name = name
+        self.multi_head = multi_head
+
+    def forward(self, predicts, batch):
+        loss_dict = dict()
+        for idx, model_name in enumerate(self.model_name_list):
+            out = predicts[model_name]
+            if self.key is not None:
+                out = out[self.key]
+            if self.multi_head:
+                assert 'sar' in out, 'multi head has multi out'
+                loss = super().forward(out['sar'], batch[:1] + batch[2:])
+            else:
+                loss = super().forward(out, batch)
             if isinstance(loss, dict):
                 for key in loss:
                     loss_dict["{}_{}_{}".format(self.name, model_name,

ppocr/losses/rec_multi_loss.py

+# copyright (c) 2022 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import paddle
+from paddle import nn
+
+from .rec_ctc_loss import CTCLoss
+from .rec_sar_loss import SARLoss
+
+
+class MultiLoss(nn.Layer):
+    def __init__(self, **kwargs):
+        super().__init__()
+        self.loss_funcs = {}
+        self.loss_list = kwargs.pop('loss_config_list')
+        self.weight_1 = kwargs.get('weight_1', 1.0)
+        self.weight_2 = kwargs.get('weight_2', 1.0)
+        self.gtc_loss = kwargs.get('gtc_loss', 'sar')
+        for loss_info in self.loss_list:
+            for name, param in loss_info.items():
+                if param is not None:
+                    kwargs.update(param)
+                loss = eval(name)(**kwargs)
+                self.loss_funcs[name] = loss
+
+    def forward(self, predicts, batch):
+        self.total_loss = {}
+        total_loss = 0.0
+        # batch [image, label_ctc, label_sar, length, valid_ratio]
+        for name, loss_func in self.loss_funcs.items():
+            if name == 'CTCLoss':
+                loss = loss_func(predicts['ctc'],
+                                 batch[:2] + batch[3:])['loss'] * self.weight_1
+            elif name == 'SARLoss':
+                loss = loss_func(predicts['sar'],
+                                 batch[:1] + batch[2:])['loss'] * self.weight_2
+            else:
+                raise NotImplementedError(
+                    '{} is not supported in MultiLoss yet'.format(name))
+            self.total_loss[name] = loss
+            total_loss += loss
+        self.total_loss['loss'] = total_loss
+        return self.total_loss

ppocr/losses/rec_sar_loss.py

@@ -9,8 +9,9 @@ from paddle import nn
 class SARLoss(nn.Layer):
     def __init__(self, **kwargs):
         super(SARLoss, self).__init__()
+        ignore_index = kwargs.get('ignore_index', 92)  # 6626
         self.loss_func = paddle.nn.loss.CrossEntropyLoss(
-            reduction="mean", ignore_index=92)
+            reduction="mean", ignore_index=ignore_index)
 
     def forward(self, predicts, batch):
         predict = predicts[:, :