Merge branch 'release/2.3' into 2.3

README.md

@@ -26,7 +26,7 @@ PaddleOCR aims to create multilingual, awesome, leading, and practical OCR tools
 **Recent updates**
 
 - PaddleOCR R&D team would like to share the key points of PP-OCRv2, at 20:15 pm on September 8th, [Live Address](https://live.bilibili.com/21689802).
-- 2021.9.7 release PaddleOCR v2.3, [PP-OCRv2](#PP-OCRv2) is proposed. The inference speed of PP-OCRv2 is 220% higher than that of PP-OCR server in CPU device. The F-score of PP-OCRv2 is 7% higher than that of PP-OCR mobile.
+- 2021.9.7 release PaddleOCR v2.3, [PP-OCRv2](#PP-OCRv2) is proposed. The inference speed of PP-OCRv2 is 220% higher than that of PP-OCR server in CPU device. The F-score of PP-OCRv2 is 7% higher than that of PP-OCR mobile. ([arxiv paper](https://arxiv.org/abs/2109.03144))
 - 2021.8.3 released PaddleOCR v2.2, add a new structured documents analysis toolkit, i.e., [PP-Structure](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/ppstructure/README.md), support layout analysis and table recognition (One-key to export chart images to Excel files).
 - 2021.4.8 release end-to-end text recognition algorithm [PGNet](https://www.aaai.org/AAAI21Papers/AAAI-2885.WangP.pdf) which is published in AAAI 2021. Find tutorial [here](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/doc/doc_en/pgnet_en.md)；release multi language recognition [models](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/doc/doc_en/multi_languages_en.md), support more than 80 languages recognition; especically, the performance of [English recognition model](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/doc/doc_en/models_list_en.md#English) is Optimized.
 
@@ -147,7 +147,7 @@ For a new language request, please refer to [Guideline for new language_requests
 
 [1] PP-OCR is a practical ultra-lightweight OCR system. It is mainly composed of three parts: DB text detection, detection frame correction and CRNN text recognition. The system adopts 19 effective strategies from 8 aspects including backbone network selection and adjustment, prediction head design, data augmentation, learning rate transformation strategy, regularization parameter selection, pre-training model use, and automatic model tailoring and quantization to optimize and slim down the models of each module (as shown in the green box above). The final results are an ultra-lightweight Chinese and English OCR model with an overall size of 3.5M and a 2.8M English digital OCR model. For more details, please refer to the PP-OCR technical article (https://arxiv.org/abs/2009.09941).
 
-[2] On the basis of PP-OCR, PP-OCRv2 is further optimized in five aspects. The detection model adopts CML(Collaborative Mutual Learning) knowledge distillation strategy and CopyPaste data expansion strategy. The recognition model adopts LCNet lightweight backbone network, U-DML knowledge distillation strategy and enhanced CTC loss function improvement (as shown in the red box above), which further improves the inference speed and prediction effect. For more details, please refer to the technical report of PP-OCRv2 (arXiv link is coming soon).
+[2] On the basis of PP-OCR, PP-OCRv2 is further optimized in five aspects. The detection model adopts CML(Collaborative Mutual Learning) knowledge distillation strategy and CopyPaste data expansion strategy. The recognition model adopts LCNet lightweight backbone network, U-DML knowledge distillation strategy and enhanced CTC loss function improvement (as shown in the red box above), which further improves the inference speed and prediction effect. For more details, please refer to the [technical report](https://arxiv.org/abs/2109.03144) of PP-OCRv2.
 
 
 

README_ch.md

@@ -25,7 +25,7 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力
 **近期更新**
 
 - PaddleOCR研发团队对最新发版内容技术深入解读，9月8日晚上20:15，[直播地址](https://live.bilibili.com/21689802)。
-- 2021.9.7 发布PaddleOCR v2.3，发布[PP-OCRv2](#PP-OCRv2)，CPU推理速度相比于PP-OCR server提升220%；效果相比于PP-OCR mobile 提升7%。
+- 2021.9.7 发布PaddleOCR v2.3，发布[PP-OCRv2](#PP-OCRv2)，CPU推理速度相比于PP-OCR server提升220%；效果相比于PP-OCR mobile 提升7%。([arxiv论文](https://arxiv.org/abs/2109.03144))
 - 2021.8.3 发布PaddleOCR v2.2，新增文档结构分析[PP-Structure](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/ppstructure/README_ch.md)工具包，支持版面分析与表格识别（含Excel导出）。
 - 2021.6.29 [FAQ](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/doc/doc_ch/FAQ.md)新增5个高频问题，总数248个，每周一都会更新，欢迎大家持续关注。
 - 2021.4.8 release 2.1版本，新增AAAI 2021论文[端到端识别算法PGNet](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/doc/doc_ch/pgnet.md)开源，[多语言模型](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/doc/doc_ch/multi_languages.md)支持种类增加到80+。
@@ -143,7 +143,7 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力
 
 [1] PP-OCR是一个实用的超轻量OCR系统。主要由DB文本检测、检测框矫正和CRNN文本识别三部分组成。该系统从骨干网络选择和调整、预测头部的设计、数据增强、学习率变换策略、正则化参数选择、预训练模型使用以及模型自动裁剪量化8个方面，采用19个有效策略，对各个模块的模型进行效果调优和瘦身(如绿框所示)，最终得到整体大小为3.5M的超轻量中英文OCR和2.8M的英文数字OCR。更多细节请参考PP-OCR技术方案 https://arxiv.org/abs/2009.09941
 
-[2] PP-OCRv2在PP-OCR的基础上，进一步在5个方面重点优化，检测模型采用CML协同互学习知识蒸馏策略和CopyPaste数据增广策略；识别模型采用LCNet轻量级骨干网络、UDML 改进知识蒸馏策略和Enhanced CTC loss损失函数改进（如上图红框所示），进一步在推理速度和预测效果上取得明显提升。更多细节请参考PP-OCR技术方案（arxiv链接生成中）。
+[2] PP-OCRv2在PP-OCR的基础上，进一步在5个方面重点优化，检测模型采用CML协同互学习知识蒸馏策略和CopyPaste数据增广策略；识别模型采用LCNet轻量级骨干网络、UDML 改进知识蒸馏策略和Enhanced CTC loss损失函数改进（如上图红框所示），进一步在推理速度和预测效果上取得明显提升。更多细节请参考PP-OCRv2[技术报告](https://arxiv.org/abs/2109.03144)。
 
 
 <a name="效果展示"></a>

deploy/cpp_infer/src/main.cpp

@@ -179,7 +179,7 @@ int main_system(std::vector<cv::String> cv_all_img_names) {
     for (int i = 0; i < cv_all_img_names.size(); ++i) {
       LOG(INFO) << "The predict img: " << cv_all_img_names[i];
 
-      cv::Mat srcimg = cv::imread(FLAGS_image_dir, cv::IMREAD_COLOR);
+      cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR);
       if (!srcimg.data) {
         std::cerr << "[ERROR] image read failed! image path: " << cv_all_img_names[i] << endl;
         exit(1);

doc/doc_ch/benchmark.md

@@ -12,40 +12,27 @@
 ## 评估指标  
 
 说明：
-- v1.0是未添加优化策略的DB+CRNN模型，v1.1是添加多种优化策略和方向分类器的PP-OCR模型。slim_v1.1是使用裁剪或量化的模型。
+
 - 检测输入图像的的长边尺寸是960。
-- 评估耗时阶段为图像输入到结果输出的完整阶段，包括了图像的预处理和后处理。  
+- 评估耗时阶段为图像预测耗时，不包括图像的预处理和后处理。  
 - `Intel至强6148`为服务器端CPU型号，测试中使用Intel MKL-DNN 加速。
 - `骁龙855`为移动端处理平台型号。  
 
-不同预测模型大小和整体识别精度对比
+预测模型大小和整体识别精度对比
 
 | 模型名称                     | 整体模型<br>大小\(M\) | 检测模型<br>大小\(M\) | 方向分类器<br>模型大小\(M\) | 识别模型<br>大小\(M\) | 整体识别<br>F\-score |
 |:-:|:-:|:-:|:-:|:-:|:-:|
-| ch\_ppocr\_mobile\_v1\.1 | 8\.1        | 2\.6        | 0\.9           | 4\.6        | 0\.5193      |
-| ch\_ppocr\_server\_v1\.1 | 155\.1      | 47\.2       | 0\.9           | 107         | 0\.5414      |
-| ch\_ppocr\_mobile\_v1\.0 | 8\.6        | 4\.1        | \-             | 4\.5        | 0\.393       |
-| ch\_ppocr\_server\_v1\.0 | 203\.8      | 98\.5       | \-             | 105\.3      | 0\.4436      |
-
-不同预测模型在T4 GPU上预测速度对比，单位ms
-
-| 模型名称                     | 整体  | 检测 | 方向分类器 | 识别  |
-|:-:|:-:|:-:|:-:|:-:|
-| ch\_ppocr\_mobile\_v1\.1 | 137 | 35 | 24    | 78  |
-| ch\_ppocr\_server\_v1\.1 | 204 | 39 | 25    | 140 |
-| ch\_ppocr\_mobile\_v1\.0 | 117 | 41 | \-    | 76  |
-| ch\_ppocr\_server\_v1\.0 | 199 | 52 | \-    | 147 |
+| PP-OCRv2 | 11\.6        | 3\.0        | 0\.9           | 8\.6        | 0\.5224      |
+| PP-OCR mobile |   8\.1  | 2\.6        | 0\.9           | 4\.6        | 0\.503       |
+| PP-OCR server | 155\.1  | 47\.2       | 0\.9           | 107         | 0\.570       |
 
-不同预测模型在CPU上预测速度对比，单位ms
 
-| 模型名称                     | 整体   | 检测  | 方向分类器 | 识别  |
-|:-:|:-:|:-:|:-:|:-:|
-| ch\_ppocr\_mobile\_v1\.1 | 421  | 164 | 51    | 206 |
-| ch\_ppocr\_mobile\_v1\.0 | 398  | 219 | \-    | 179 |
+预测模型在CPU和GPU上的速度对比，单位ms
 
-裁剪量化模型和原始模型模型大小，整体识别精度和在SD 855上预测速度对比
+| 模型名称                     | CPU   | T4 GPU  |
+|:-:|:-:|:-:|
+| PP-OCRv2 | 330  | 111 |
+| PP-OCR mobile | 356  | 11 6|
+| PP-OCR server | 1056  | 200 |
 
-| 模型名称                           | 整体模型<br>大小\(M\) | 检测模型<br>大小\(M\) | 方向分类器<br>模型大小\(M\) | 识别模型<br>大小\(M\) | 整体识别<br>F\-score | SD 855<br>\(ms\) |
-|:-:|:-:|:-:|:-:|:-:|:-:|:-:|
-| ch\_ppocr\_mobile\_v1\.1       | 8\.1        | 2\.6        | 0\.9           | 4\.6        | 0\.5193      | 306          |
-| ch\_ppocr\_mobile\_slim\_v1\.1 | 3\.5        | 1\.4        | 0\.5           | 1\.6        | 0\.521       | 268          |
+更多 PP-OCR 系列模型的预测指标可以参考[PP-OCR Benchamrk](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/doc/doc_ch/benchmark.md)

doc/doc_ch/inference_ppocr.md

@@ -18,9 +18,10 @@
 
 ```
 # 下载超轻量中文检测模型：
-wget  https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar
-tar xf ch_ppocr_mobile_v2.0_det_infer.tar
-python3 tools/infer/predict_det.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./ch_ppocr_mobile_v2.0_det_infer/"
+wget  https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar
+tar xf ch_PP-OCRv2_det_infer.tar
+python3 tools/infer/predict_det.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./ch_PP-OCRv2_det_infer.tar/"
+
 ```
 
 可视化文本检测结果默认保存到`./inference_results`文件夹里面，结果文件的名称前缀为'det_res'。结果示例如下：
@@ -38,13 +39,13 @@ python3 tools/infer/predict_det.py --image_dir="./doc/imgs/00018069.jpg" --det_m
 如果输入图片的分辨率比较大，而且想使用更大的分辨率预测，可以设置det_limit_side_len 为想要的值，比如1216：
 
 ```
-python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./inference/det_db/" --det_limit_type=max --det_limit_side_len=1216
+python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./inference/ch_PP-OCRv2_det_infer/" --det_limit_type=max --det_limit_side_len=1216
 ```
 
 如果想使用CPU进行预测，执行命令如下
 
 ```
-python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./inference/det_db/"  --use_gpu=False
+python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./inference/ch_PP-OCRv2_det_infer/"  --use_gpu=False
 ```
 
 
@@ -61,9 +62,9 @@ python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_di
 
 ```
 # 下载超轻量中文识别模型：
-wget  https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar
-tar xf ch_ppocr_mobile_v2.0_rec_infer.tar
-python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/ch/word_4.jpg" --rec_model_dir="ch_ppocr_mobile_v2.0_rec_infer"
+wget  https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar
+tar xf ch_PP-OCRv2_rec_infer.tar
+python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/ch/word_4.jpg" --rec_model_dir="./ch_PP-OCRv2_rec_infer/"
 ```
 
 ![](../imgs_words/ch/word_4.jpg)
@@ -78,10 +79,9 @@ Predicts of ./doc/imgs_words/ch/word_4.jpg:('实力活力', 0.98458153)
 
 ### 2.2 多语言模型的推理
 
-如果您需要预测的是其他语言模型，在使用inference模型预测时，需要通过`--rec_char_dict_path`指定使用的字典路径, 同时为了得到正确的可视化结果，
-需要通过 `--vis_font_path` 指定可视化的字体路径，`doc/fonts/` 路径下有默认提供的小语种字体，例如韩文识别：
-
+如果您需要预测的是其他语言模型，可以在[此链接](./models_list.md#%E5%A4%9A%E8%AF%AD%E8%A8%80%E8%AF%86%E5%88%AB%E6%A8%A1%E5%9E%8B)中找到对应语言的inference模型，在使用inference模型预测时，需要通过`--rec_char_dict_path`指定使用的字典路径, 同时为了得到正确的可视化结果，需要通过 `--vis_font_path` 指定可视化的字体路径，`doc/fonts/` 路径下有默认提供的小语种字体，例如韩文识别：
 ```
+wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_infer.tar
 python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/korean/1.jpg" --rec_model_dir="./your inference model" --rec_char_type="korean" --rec_char_dict_path="ppocr/utils/dict/korean_dict.txt" --vis_font_path="doc/fonts/korean.ttf"
 ```
 
@@ -122,14 +122,13 @@ Predicts of ./doc/imgs_words/ch/word_4.jpg:['0', 0.9999982]
 
 ```shell
 # 使用方向分类器
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/" --cls_model_dir="./inference/cls/" --rec_model_dir="./inference/rec_crnn/" --use_angle_cls=true
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/ch_PP-OCRv2_det_infer/" --cls_model_dir="./inference/cls/" --rec_model_dir="./inference/ch_PP-OCRv2_rec_infer/" --use_angle_cls=true
 # 不使用方向分类器
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/" --rec_model_dir="./inference/rec_crnn/" --use_angle_cls=false
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/ch_PP-OCRv2_det_infer/" --rec_model_dir="./inference/ch_PP-OCRv2_rec_infer/" --use_angle_cls=false
 # 使用多进程
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/" --rec_model_dir="./inference/rec_crnn/" --use_angle_cls=false --use_mp=True --total_process_num=6
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/ch_PP-OCRv2_det_infer/" --rec_model_dir="./inference/ch_PP-OCRv2_rec_infer/" --use_angle_cls=false --use_mp=True --total_process_num=6
 ```
 
 执行命令后，识别结果图像如下：
 
 ![](../imgs_results/system_res_00018069.jpg)
-

doc/doc_en/benchmark_en.md

@@ -13,7 +13,6 @@ We collected 300 images for different real application scenarios to evaluate the
 ## MEASUREMENT
 
 Explanation:
-- v1.0 indicates DB+CRNN models without the strategies. v1.1 indicates the PP-OCR models with the strategies and the direction classify. slim_v1.1 indicates the PP-OCR models with prunner or quantization.
 
 - The long size of the input for the text detector is 960.
 
@@ -27,30 +26,16 @@ Compares the model size and F-score:
 
 | Model Name                    | Model Size <br> of the <br> Whole System\(M\) | Model Size <br>of the Text <br> Detector\(M\) | Model Size <br> of the Direction <br> Classifier\(M\) | Model Size<br>of the Text <br> Recognizer \(M\) | F\-score |
 |:-:|:-:|:-:|:-:|:-:|:-:|
-| ch\_ppocr\_mobile\_v1\.1 | 8\.1        | 2\.6        | 0\.9           | 4\.6        | 0\.5193      |
-| ch\_ppocr\_server\_v1\.1 | 155\.1      | 47\.2       | 0\.9           | 107         | 0\.5414      |
-| ch\_ppocr\_mobile\_v1\.0 | 8\.6        | 4\.1        | \-             | 4\.5        | 0\.393       |
-| ch\_ppocr\_server\_v1\.0 | 203\.8      | 98\.5       | \-             | 105\.3      | 0\.4436      |
+| PP-OCRv2                 | 11\.6        | 3\.0        | 0\.9           | 8\.6        | 0\.5224      |
+| PP-OCR mobile            |   8\.1       | 2\.6        | 0\.9           | 4\.6        | 0\.503       |
+| PP-OCR server            | 155\.1       | 47\.2       | 0\.9           | 107         | 0\.570       |
 
-Compares the time-consuming on T4 GPU (ms):
+Compares the time-consuming on CPU and T4 GPU (ms):
 
-| Model Name                     | Overall  | Text Detector  | Direction Classifier  | Text Recognizer |
-|:-:|:-:|:-:|:-:|:-:|
-| ch\_ppocr\_mobile\_v1\.1 | 137 | 35 | 24    | 78  |
-| ch\_ppocr\_server\_v1\.1 | 204 | 39 | 25    | 140 |
-| ch\_ppocr\_mobile\_v1\.0 | 117 | 41 | \-    | 76  |
-| ch\_ppocr\_server\_v1\.0 | 199 | 52 | \-    | 147 |
+| Model Name    | CPU  | T4 GPU |
+|:-:|:-:|:-:|
+| PP-OCRv2      | 330  | 111 |
+| PP-OCR mobile | 356  | 116|
+| PP-OCR server | 1056 | 200 |
 
-Compares the time-consuming on CPU (ms):
-
-| Model Name                     | Overall  | Text Detector  | Direction Classifier  | Text Recognizer |
-|:-:|:-:|:-:|:-:|:-:|
-| ch\_ppocr\_mobile\_v1\.1 | 421  | 164 | 51    | 206 |
-| ch\_ppocr\_mobile\_v1\.0 | 398  | 219 | \-    | 179 |
-
-Compares the model size, F-score, the time-consuming on SD 855 of between the slim models and the original models:
-
-| Model Name                          | Model Size <br> of the <br> Whole System\(M\) | Model Size <br>of the Text <br> Detector\(M\) | Model Size <br> of the Direction <br> Classifier\(M\) | Model Size<br>of the Text <br> Recognizer \(M\) | F\-score | SD 855<br>\(ms\) |
-|:-:|:-:|:-:|:-:|:-:|:-:|:-:|
-| ch\_ppocr\_mobile\_v1\.1       | 8\.1        | 2\.6        | 0\.9           | 4\.6        | 0\.5193      | 306          |
-| ch\_ppocr\_mobile\_slim\_v1\.1 | 3\.5        | 1\.4        | 0\.5           | 1\.6        | 0\.521       | 268          |
+More indicators of PP-OCR series models can be referred to [PP-OCR Benchamrk](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/doc/doc_en/benchmark_en.md)

doc/doc_en/inference_ppocr_en.md

@@ -19,10 +19,10 @@ The default configuration is based on the inference setting of the DB text detec
 
 ```
 # download DB text detection inference model
-wget  https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar
-tar xf ch_ppocr_mobile_v2.0_det_infer.tar
-# predict
-python3 tools/infer/predict_det.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/"
+wget  https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar
+tar xf ch_PP-OCRv2_det_infer.tar
+# run inference
+python3 tools/infer/predict_det.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./ch_PP-OCRv2_det_infer.tar/"
 ```
 
 The visual 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:
@@ -39,12 +39,12 @@ Set as `limit_type='min', det_limit_side_len=960`, it means that the shortest si
 
 If the resolution of the input picture is relatively large and you want to use a larger resolution prediction, you can set det_limit_side_len to the desired value, such as 1216:
 ```
-python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./inference/det_db/" --det_limit_type=max --det_limit_side_len=1216
+python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./inference/ch_PP-OCRv2_det_infer/" --det_limit_type=max --det_limit_side_len=1216
 ```
 
 If you want to use the CPU for prediction, execute the command as follows
 ```
-python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./inference/det_db/" --use_gpu=False
+python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./inference/ch_PP-OCRv2_det_infer/"  --use_gpu=False
 ```
 
 <a name="RECOGNITION_MODEL_INFERENCE"></a>
@@ -59,9 +59,10 @@ For lightweight Chinese recognition model inference, you can execute the followi
 
 ```
 # download CRNN text recognition inference model
-wget  https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar
-tar xf ch_ppocr_mobile_v2.0_rec_infer.tar
-python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_10.png" --rec_model_dir="ch_ppocr_mobile_v2.0_rec_infer"
+wget  https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar
+tar xf ch_PP-OCRv2_rec_infer.tar
+# run inference
+python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/ch/word_4.jpg" --rec_model_dir="./ch_PP-OCRv2_rec_infer/"
 ```
 
 ![](../imgs_words_en/word_10.png)
@@ -75,10 +76,12 @@ Predicts of ./doc/imgs_words_en/word_10.png:('PAIN', 0.9897658)
 <a name="MULTILINGUAL_MODEL_INFERENCE"></a>
 
 ### 2. Multilingaul Model Inference
-If you need to predict other language models, when using inference model prediction, you need to specify the dictionary path used by `--rec_char_dict_path`. At the same time, in order to get the correct visualization results,
+If you need to predict [other language models](./models_list_en.md#Multilingual), when using inference model prediction, you need to specify the dictionary path used by `--rec_char_dict_path`. At the same time, in order to get the correct visualization results,
 You need to specify the visual font path through `--vis_font_path`. There are small language fonts provided by default under the `doc/fonts` path, such as Korean recognition:
 
 ```
+wget wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_infer.tar
+
 python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/korean/1.jpg" --rec_model_dir="./your inference model" --rec_char_type="korean" --rec_char_dict_path="ppocr/utils/dict/korean_dict.txt" --vis_font_path="doc/fonts/korean.ttf"
 ```
 ![](../imgs_words/korean/1.jpg)
@@ -117,13 +120,13 @@ When performing prediction, you need to specify the path of a single image or a
 
 ```shell
 # use direction classifier
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/" --cls_model_dir="./inference/cls/" --rec_model_dir="./inference/rec_crnn/" --use_angle_cls=true
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/ch_PP-OCRv2_det_infer/" --cls_model_dir="./inference/cls/" --rec_model_dir="./inference/ch_PP-OCRv2_rec_infer/" --use_angle_cls=true
 
 # not use use direction classifier
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/" --rec_model_dir="./inference/rec_crnn/"
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/ch_PP-OCRv2_det_infer/" --rec_model_dir="./inference/ch_PP-OCRv2_rec_infer/" --use_angle_cls=false
 
 # use multi-process
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/" --rec_model_dir="./inference/rec_crnn/" --use_angle_cls=false --use_mp=True --total_process_num=6
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/ch_PP-OCRv2_det_infer/" --rec_model_dir="./inference/ch_PP-OCRv2_rec_infer/" --use_angle_cls=false --use_mp=True --total_process_num=6
 ```
 
 

tests/compare_results.py

-import numpy as np
-import os
-import subprocess
-import json
-import argparse
-import glob
-
-
-def init_args():
-    parser = argparse.ArgumentParser()
-    # params for testing assert allclose
-    parser.add_argument("--atol", type=float, default=1e-3)
-    parser.add_argument("--rtol", type=float, default=1e-3)
-    parser.add_argument("--gt_file", type=str, default="")
-    parser.add_argument("--log_file", type=str, default="")
-    parser.add_argument("--precision", type=str, default="fp32")
-    return parser
-
-
-def parse_args():
-    parser = init_args()
-    return parser.parse_args()
-
-
-def run_shell_command(cmd):
-    p = subprocess.Popen(
-        cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True)
-    out, err = p.communicate()
-
-    if p.returncode == 0:
-        return out.decode('utf-8')
-    else:
-        return None
-
-
-def parser_results_from_log_by_name(log_path, names_list):
-    if not os.path.exists(log_path):
-        raise ValueError("The log file {} does not exists!".format(log_path))
-
-    if names_list is None or len(names_list) < 1:
-        return []
-
-    parser_results = {}
-    for name in names_list:
-        cmd = "grep {} {}".format(name, log_path)
-        outs = run_shell_command(cmd)
-        outs = outs.split("\n")[0]
-        result = outs.split("{}".format(name))[-1]
-        result = json.loads(result)
-        parser_results[name] = result
-    return parser_results
-
-
-def load_gt_from_file(gt_file):
-    if not os.path.exists(gt_file):
-        raise ValueError("The log file {} does not exists!".format(gt_file))
-    with open(gt_file, 'r') as f:
-        data = f.readlines()
-        f.close()
-    parser_gt = {}
-    for line in data:
-        image_name, result = line.strip("\n").split("\t")
-        result = json.loads(result)
-        parser_gt[image_name] = result
-    return parser_gt
-
-
-def load_gt_from_txts(gt_file):
-    gt_list = glob.glob(gt_file)
-    gt_collection = {}
-    for gt_f in gt_list:
-        gt_dict = load_gt_from_file(gt_f)
-        basename = os.path.basename(gt_f)
-        if "fp32" in basename:
-            gt_collection["fp32"] = [gt_dict, gt_f]
-        elif "fp16" in basename:
-            gt_collection["fp16"] = [gt_dict, gt_f]
-        elif "int8" in basename:
-            gt_collection["int8"] = [gt_dict, gt_f]
-        else:
-            continue
-    return gt_collection
-
-
-def collect_predict_from_logs(log_path, key_list):
-    log_list = glob.glob(log_path)
-    pred_collection = {}
-    for log_f in log_list:
-        pred_dict = parser_results_from_log_by_name(log_f, key_list)
-        key = os.path.basename(log_f)
-        pred_collection[key] = pred_dict
-
-    return pred_collection
-
-
-def testing_assert_allclose(dict_x, dict_y, atol=1e-7, rtol=1e-7):
-    for k in dict_x:
-        np.testing.assert_allclose(
-            np.array(dict_x[k]), np.array(dict_y[k]), atol=atol, rtol=rtol)
-
-
-if __name__ == "__main__":
-    # Usage:
-    # python3.7 tests/compare_results.py --gt_file=./tests/results/*.txt  --log_file=./tests/output/infer_*.log
-
-    args = parse_args()
-
-    gt_collection = load_gt_from_txts(args.gt_file)
-    key_list = gt_collection["fp32"][0].keys()
-
-    pred_collection = collect_predict_from_logs(args.log_file, key_list)
-    for filename in pred_collection.keys():
-        if "fp32" in filename:
-            gt_dict, gt_filename = gt_collection["fp32"]
-        elif "fp16" in filename:
-            gt_dict, gt_filename = gt_collection["fp16"]
-        elif "int8" in filename:
-            gt_dict, gt_filename = gt_collection["int8"]
-        else:
-            continue
-        pred_dict = pred_collection[filename]
-
-        try:
-            testing_assert_allclose(
-                gt_dict, pred_dict, atol=args.atol, rtol=args.rtol)
-            print(
-                "Assert allclose passed! The results of {} and {} are consistent!".
-                format(filename, gt_filename))
-        except Exception as E:
-            print(E)
-            raise ValueError(
-                "The results of {} and the results of {} are inconsistent!".
-                format(filename, gt_filename))

tests/ocr_det_params.txt

-===========================train_params===========================
-model_name:ocr_det
-python:python3.7
-gpu_list:0|0,1
-Global.use_gpu:True|True
-Global.auto_cast:null
-Global.epoch_num:lite_train_infer=1|whole_train_infer=300
-Global.save_model_dir:./output/
-Train.loader.batch_size_per_card:lite_train_infer=2|whole_train_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|pact_train
-norm_train:tools/train.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained
-pact_train:deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o
-fpgm_train:deploy/slim/prune/sensitivity_anal.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/det_mv3_db_v2.0_train/best_accuracy
-distill_train:null
-null:null
-null:null
-##
-===========================eval_params=========================== 
-eval:tools/eval.py -c configs/det/det_mv3_db.yml -o
-null:null
-##
-===========================infer_params===========================
-Global.save_inference_dir:./output/
-Global.pretrained_model:
-norm_export:tools/export_model.py -c configs/det/det_mv3_db.yml -o 
-quant_export:deploy/slim/quantization/export_model.py -c configs/det/det_mv3_db.yml -o 
-fpgm_export:deploy/slim/prune/export_prune_model.py -c configs/det/det_mv3_db.yml -o 
-distill_export:null
-export1:null
-export2:null
-##
-infer_model:./inference/ch_ppocr_mobile_v2.0_det_infer/
-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
-===========================cpp_infer_params===========================
-use_opencv:True
-infer_model:./inference/ch_ppocr_mobile_v2.0_det_infer/
-infer_quant:False
-inference:./deploy/cpp_infer/build/ppocr det
---use_gpu:True|False
---enable_mkldnn:True|False
---cpu_threads:1|6
---rec_batch_num:1
---use_tensorrt:False|True
---precision:fp32|fp16
---det_model_dir:
---image_dir:./inference/ch_det_data_50/all-sum-510/
---save_log_path:null
---benchmark:True
-

tests/ocr_det_server_params.txt

-===========================train_params===========================
-model_name:ocr_server_det
-python:python3.7
-gpu_list:0|0,1
-Global.use_gpu:True|True
-Global.auto_cast:null
-Global.epoch_num:lite_train_infer=2|whole_train_infer=300
-Global.save_model_dir:./output/
-Train.loader.batch_size_per_card:lite_train_infer=2|whole_train_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|pact_train
-norm_train:tools/train.py -c configs/det/det_r50_vd_db.yml -o Global.pretrained_model=""
-pact_train:null
-fpgm_train:null
-distill_train:null
-null:null
-null:null
-##
-===========================eval_params=========================== 
-eval:tools/eval.py -c configs/det/det_mv3_db.yml -o
-null:null
-##
-===========================infer_params===========================
-Global.save_inference_dir:./output/
-Global.pretrained_model:
-norm_export:tools/export_model.py -c configs/det/det_r50_vd_db.yml -o 
-quant_export:null 
-fpgm_export:null
-distill_export:null
-export1:null
-export2:null
-##
-infer_model:./inference/ch_ppocr_server_v2.0_det_infer/
-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
-

tests/ocr_rec_params.txt

-===========================train_params===========================
-model_name:ocr_rec
-python:python3.7
-gpu_list:0|2,3
-Global.use_gpu:True|True
-Global.auto_cast:null
-Global.epoch_num:lite_train_infer=2|whole_train_infer=300
-Global.save_model_dir:./output/
-Train.loader.batch_size_per_card:lite_train_infer=128|whole_train_infer=128
-Global.pretrained_model:null
-train_model_name:latest
-train_infer_img_dir:./train_data/ic15_data/train
-null:null
-##
-trainer:norm_train|pact_train
-norm_train:tools/train.py -c configs/rec/rec_icdar15_train.yml -o
-pact_train:deploy/slim/quantization/quant.py -c configs/rec/rec_icdar15_train.yml -o
-fpgm_train:null
-distill_train:null
-null:null
-null:null
-##
-===========================eval_params===========================
-eval:tools/eval.py -c configs/rec/rec_icdar15_train.yml -o
-null:null
-##
-===========================infer_params===========================
-Global.save_inference_dir:./output/
-Global.pretrained_model:
-norm_export:tools/export_model.py -c configs/rec/rec_icdar15_train.yml -o
-quant_export:deploy/slim/quantization/export_model.py -c configs/rec/rec_icdar15_train.yml -o
-fpgm_export:null
-distill_export:null
-export1:null
-export2:null
-##
-infer_model:./inference/ch_ppocr_mobile_v2.0_rec_infer/
-infer_export:null
-infer_quant:False
-inference:tools/infer/predict_rec.py
---use_gpu:True|False
---enable_mkldnn:True|False
---cpu_threads:1|6
---rec_batch_num:1
---use_tensorrt:True|False
---precision:fp32|fp16|int8
---rec_model_dir:
---image_dir:./inference/rec_inference
---save_log_path:./test/output/
---benchmark:True
-null:null

tests/prepare.sh

-#!/bin/bash
-FILENAME=$1
-# MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer', 'infer', 'cpp_infer']
-MODE=$2
-
-dataline=$(cat ${FILENAME})
-
-# parser params
-IFS=$'\n'
-lines=(${dataline})
-function func_parser_key(){
-    strs=$1
-    IFS=":"
-    array=(${strs})
-    tmp=${array[0]}
-    echo ${tmp}
-}
-function func_parser_value(){
-    strs=$1
-    IFS=":"
-    array=(${strs})
-    tmp=${array[1]}
-    echo ${tmp}
-}
-IFS=$'\n'
-# The training params
-model_name=$(func_parser_value "${lines[1]}")
-
-trainer_list=$(func_parser_value "${lines[14]}")
-
-# MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer']
-MODE=$2
-
-if [ ${MODE} = "lite_train_infer" ];then
-    # pretrain lite train data
-    wget -nc -P  ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams
-    wget -nc -P ./pretrain_models/  https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar
-    cd ./pretrain_models/ && tar xf det_mv3_db_v2.0_train.tar && cd ../
-    rm -rf ./train_data/icdar2015
-    rm -rf ./train_data/ic15_data
-    wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015_lite.tar
-    wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ic15_data.tar # todo change to bcebos
-    wget -nc -P ./deploy/slim/prune https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/sen.pickle
-    
-    cd ./train_data/ && tar xf icdar2015_lite.tar && tar xf ic15_data.tar
-    ln -s ./icdar2015_lite ./icdar2015
-    cd ../
-elif [ ${MODE} = "whole_train_infer" ];then
-    wget -nc -P  ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams
-    rm -rf ./train_data/icdar2015
-    rm -rf ./train_data/ic15_data
-    wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015.tar
-    wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ic15_data.tar
-    cd ./train_data/ && tar xf icdar2015.tar && tar xf ic15_data.tar && cd ../
-elif [ ${MODE} = "whole_infer" ];then
-    wget -nc -P  ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams
-    rm -rf ./train_data/icdar2015
-    rm -rf ./train_data/ic15_data
-    wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015_infer.tar
-    wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ic15_data.tar
-    cd ./train_data/ && tar xf icdar2015_infer.tar && tar xf ic15_data.tar
-    ln -s ./icdar2015_infer ./icdar2015
-    cd ../
-elif [ ${MODE} = "infer" ] || [ ${MODE} = "cpp_infer" ];then
-    if [ ${model_name} = "ocr_det" ]; then
-        eval_model_name="ch_ppocr_mobile_v2.0_det_infer"
-        rm -rf ./train_data/icdar2015
-        wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar
-        wget -nc  -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar
-        cd ./inference && tar xf ${eval_model_name}.tar && tar xf ch_det_data_50.tar && cd ../
-    elif [ ${model_name} = "ocr_server_det" ]; then
-        wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar
-        wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar
-        cd ./inference && tar xf ch_ppocr_server_v2.0_det_infer.tar && tar xf ch_det_data_50.tar && cd ../
-    else 
-        rm -rf ./train_data/ic15_data
-        eval_model_name="ch_ppocr_mobile_v2.0_rec_infer"
-        wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ic15_data.tar
-        wget -nc  -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar
-        cd ./inference && tar xf ${eval_model_name}.tar && tar xf ic15_data.tar && cd ../
-    fi 
-fi
-
-if [ ${MODE} = "cpp_infer" ];then
-    cd deploy/cpp_infer
-    use_opencv=$(func_parser_value "${lines[52]}")
-    if [ ${use_opencv} = "True" ]; then
-        echo "################### build opencv ###################"
-        rm -rf 3.4.7.tar.gz opencv-3.4.7/
-        wget https://github.com/opencv/opencv/archive/3.4.7.tar.gz
-        tar -xf 3.4.7.tar.gz
-
-        cd opencv-3.4.7/
-        install_path=$(pwd)/opencv-3.4.7/opencv3
-
-        rm -rf build
-        mkdir build
-        cd build
-
-        cmake .. \
-            -DCMAKE_INSTALL_PREFIX=${install_path} \
-            -DCMAKE_BUILD_TYPE=Release \
-            -DBUILD_SHARED_LIBS=OFF \
-            -DWITH_IPP=OFF \
-            -DBUILD_IPP_IW=OFF \
-            -DWITH_LAPACK=OFF \
-            -DWITH_EIGEN=OFF \
-            -DCMAKE_INSTALL_LIBDIR=lib64 \
-            -DWITH_ZLIB=ON \
-            -DBUILD_ZLIB=ON \
-            -DWITH_JPEG=ON \
-            -DBUILD_JPEG=ON \
-            -DWITH_PNG=ON \
-            -DBUILD_PNG=ON \
-            -DWITH_TIFF=ON \
-            -DBUILD_TIFF=ON
-
-        make -j
-        make install
-        cd ../
-        echo "################### build opencv finished ###################"
-    fi
-
-
-    echo "################### build PaddleOCR demo ####################"
-    if [ ${use_opencv} = "True" ]; then
-        OPENCV_DIR=$(pwd)/opencv-3.4.7/opencv3/
-    else
-        OPENCV_DIR=''
-    fi
-    LIB_DIR=$(pwd)/Paddle/build/paddle_inference_install_dir/
-    CUDA_LIB_DIR=$(dirname `find /usr -name libcudart.so`)
-    CUDNN_LIB_DIR=$(dirname `find /usr -name libcudnn.so`)
-    
-    BUILD_DIR=build
-    rm -rf ${BUILD_DIR}
-    mkdir ${BUILD_DIR}
-    cd ${BUILD_DIR}
-    cmake .. \
-        -DPADDLE_LIB=${LIB_DIR} \
-        -DWITH_MKL=ON \
-        -DWITH_GPU=OFF \
-        -DWITH_STATIC_LIB=OFF \
-        -DWITH_TENSORRT=OFF \
-        -DOPENCV_DIR=${OPENCV_DIR} \
-        -DCUDNN_LIB=${CUDNN_LIB_DIR} \
-        -DCUDA_LIB=${CUDA_LIB_DIR} \
-        -DTENSORRT_DIR=${TENSORRT_DIR} \
-
-    make -j
-    echo "################### build PaddleOCR demo finished ###################"
-fi
\ No newline at end of file

tests/readme.md

-
-# 介绍
-
-test.sh和params.txt文件配合使用，完成OCR轻量检测和识别模型从训练到预测的流程测试。
-
-# 安装依赖
-- 安装PaddlePaddle >= 2.0
-- 安装PaddleOCR依赖
-    ```
-    pip3 install  -r ../requirements.txt
-    ```
-- 安装autolog
-    ```
-    git clone https://github.com/LDOUBLEV/AutoLog
-    cd AutoLog
-    pip3 install -r requirements.txt
-    python3 setup.py bdist_wheel
-    pip3 install ./dist/auto_log-1.0.0-py3-none-any.whl
-    cd ../
-    ```
-
-# 目录介绍
-
-```bash
-tests/
-├── ocr_det_params.txt   # 测试OCR检测模型的参数配置文件
-├── ocr_rec_params.txt   # 测试OCR识别模型的参数配置文件
-└── prepare.sh           # 完成test.sh运行所需要的数据和模型下载
-└── test.sh              # 根据
-```
-
-# 使用方法
-test.sh包含四种运行模式，每种模式的运行数据不同，分别用于测试速度和精度，分别是：
-- 模式1 lite_train_infer，使用少量数据训练，用于快速验证训练到预测的走通流程，不验证精度和速度；
-```
-bash test/prepare.sh ./tests/ocr_det_params.txt 'lite_train_infer'
-bash tests/test.sh ./tests/ocr_det_params.txt 'lite_train_infer'
-```
-- 模式2 whole_infer，使用少量数据训练，一定量数据预测，用于验证训练后的模型执行预测，预测速度是否合理；
-```
-bash tests/prepare.sh ./tests/ocr_det_params.txt 'whole_infer'
-bash tests/test.sh ./tests/ocr_det_params.txt 'whole_infer'
-```
-
-- 模式3 infer 不训练，全量数据预测，走通开源模型评估、动转静，检查inference model预测时间和精度;
-```
-bash tests/prepare.sh ./tests/ocr_det_params.txt 'infer'
-用法1:
-bash tests/test.sh ./tests/ocr_det_params.txt 'infer'
-用法2: 指定GPU卡预测，第三个传入参数为GPU卡号
-bash tests/test.sh ./tests/ocr_det_params.txt 'infer' '1'
-```
-
-模式4: whole_train_infer , CE： 全量数据训练，全量数据预测，验证模型训练精度，预测精度，预测速度
-```
-bash tests/prepare.sh ./tests/ocr_det_params.txt 'whole_train_infer'
-bash tests/test.sh ./tests/ocr_det_params.txt 'whole_train_infer'
-```

tools/infer/utility.py

@@ -236,11 +236,11 @@ def create_predictor(args, mode, logger):
             max_input_shape.update(max_pact_shape)
             opt_input_shape.update(opt_pact_shape)
         elif mode == "rec":
-            min_input_shape = {"x": [args.rec_batch_num, 3, 32, 10]}
+            min_input_shape = {"x": [1, 3, 32, 10]}
             max_input_shape = {"x": [args.rec_batch_num, 3, 32, 2000]}
             opt_input_shape = {"x": [args.rec_batch_num, 3, 32, 320]}
         elif mode == "cls":
-            min_input_shape = {"x": [args.rec_batch_num, 3, 48, 10]}
+            min_input_shape = {"x": [1, 3, 48, 10]}
             max_input_shape = {"x": [args.rec_batch_num, 3, 48, 2000]}
             opt_input_shape = {"x": [args.rec_batch_num, 3, 48, 320]}
         else: