Merge branch 'release/2.6' of https://github.com/PaddlePaddle/PaddleOCR into 26_doc

README.md

@@ -128,7 +128,7 @@ PaddleOCR support a variety of cutting-edge algorithms related to OCR, and devel
     - [Inference and Deployment](./deploy/README.md)
         - [Python Inference](./ppstructure/docs/inference_en.md)
         - [C++ Inference](./deploy/cpp_infer/readme.md)
-        - [Serving](./deploy/pdserving/README.md)
+        - [Serving](./deploy/hubserving/readme_en.md)
 - [Academic Algorithms](./doc/doc_en/algorithm_overview_en.md)
     - [Text detection](./doc/doc_en/algorithm_overview_en.md)
     - [Text recognition](./doc/doc_en/algorithm_overview_en.md)

README_ch.md

@@ -140,7 +140,7 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力
     - [推理部署](./deploy/README_ch.md)
         - [基于Python预测引擎推理](./ppstructure/docs/inference.md)
         - [基于C++预测引擎推理](./deploy/cpp_infer/readme_ch.md)
-        - [服务化部署](./deploy/pdserving/README_CN.md)
+        - [服务化部署](./deploy/hubserving/readme.md)
 - [前沿算法与模型🚀](./doc/doc_ch/algorithm_overview.md)
     - [文本检测算法](./doc/doc_ch/algorithm_overview.md)
     - [文本识别算法](./doc/doc_ch/algorithm_overview.md)

applications/发票关键信息抽取.md

@@ -30,7 +30,7 @@ cd PaddleOCR
 # 安装PaddleOCR的依赖
 pip install -r requirements.txt
 # 安装关键信息抽取任务的依赖
-pip install -r ./ppstructure/vqa/requirements.txt
+pip install -r ./ppstructure/kie/requirements.txt
 ```
 
 ## 4. 关键信息抽取
@@ -94,7 +94,7 @@ VI-LayoutXLM的配置为[ser_vi_layoutxlm_xfund_zh_udml.yml](../configs/kie/vi_l
 
 ```yml
 Architecture:
-  model_type: &model_type "vqa"
+  model_type: &model_type "kie"
   name: DistillationModel
   algorithm: Distillation
   Models:
@@ -177,7 +177,7 @@ python3 tools/eval.py -c ./fapiao/ser_vi_layoutxlm.yml -o Architecture.Backbone.
 使用下面的命令进行预测。
 
 ```bash
-python3 tools/infer_vqa_token_ser.py -c fapiao/ser_vi_layoutxlm.yml -o Architecture.Backbone.checkpoints=fapiao/models/ser_vi_layoutxlm_fapiao_udml/best_accuracy Global.infer_img=./train_data/XFUND/zh_val/val.json Global.infer_mode=False
+python3 tools/infer_kie_token_ser.py -c fapiao/ser_vi_layoutxlm.yml -o Architecture.Backbone.checkpoints=fapiao/models/ser_vi_layoutxlm_fapiao_udml/best_accuracy Global.infer_img=./train_data/XFUND/zh_val/val.json Global.infer_mode=False
 ```
 
 预测结果会保存在配置文件中的`Global.save_res_path`目录中。
@@ -195,7 +195,7 @@ python3 tools/infer_vqa_token_ser.py -c fapiao/ser_vi_layoutxlm.yml -o Architect
 
 
 ```bash
-python3 tools/infer_vqa_token_ser.py -c fapiao/ser_vi_layoutxlm.yml -o Architecture.Backbone.checkpoints=fapiao/models/ser_vi_layoutxlm_fapiao_udml/best_accuracy Global.infer_img=./train_data/zzsfp/imgs/b25.jpg Global.infer_mode=True
+python3 tools/infer_kie_token_ser.py -c fapiao/ser_vi_layoutxlm.yml -o Architecture.Backbone.checkpoints=fapiao/models/ser_vi_layoutxlm_fapiao_udml/best_accuracy Global.infer_img=./train_data/zzsfp/imgs/b25.jpg Global.infer_mode=True
 ```
 
 结果如下所示。
@@ -211,7 +211,7 @@ python3 tools/infer_vqa_token_ser.py -c fapiao/ser_vi_layoutxlm.yml -o Architect
 如果希望构建基于你在垂类场景训练得到的OCR检测与识别模型，可以使用下面的方法传入检测与识别的inference 模型路径，即可完成OCR文本检测与识别以及SER的串联过程。
 
 ```bash
-python3 tools/infer_vqa_token_ser.py -c fapiao/ser_vi_layoutxlm.yml -o Architecture.Backbone.checkpoints=fapiao/models/ser_vi_layoutxlm_fapiao_udml/best_accuracy Global.infer_img=./train_data/zzsfp/imgs/b25.jpg Global.infer_mode=True Global.kie_rec_model_dir="your_rec_model" Global.kie_det_model_dir="your_det_model"
+python3 tools/infer_kie_token_ser.py -c fapiao/ser_vi_layoutxlm.yml -o Architecture.Backbone.checkpoints=fapiao/models/ser_vi_layoutxlm_fapiao_udml/best_accuracy Global.infer_img=./train_data/zzsfp/imgs/b25.jpg Global.infer_mode=True Global.kie_rec_model_dir="your_rec_model" Global.kie_det_model_dir="your_det_model"
 ```
 
 ### 4.4 关系抽取（Relation Extraction）
@@ -316,7 +316,7 @@ python3 tools/eval.py -c ./fapiao/re_vi_layoutxlm.yml -o Architecture.Backbone.c
 # -o 后面的字段是RE任务的配置
 # -c_ser 后面的是SER任务的配置文件
 # -c_ser 后面的字段是SER任务的配置
-python3 tools/infer_vqa_token_ser_re.py -c fapiao/re_vi_layoutxlm.yml -o Architecture.Backbone.checkpoints=fapiao/models/re_vi_layoutxlm_fapiao_udml/best_accuracy Global.infer_img=./train_data/zzsfp/val.json Global.infer_mode=False -c_ser fapiao/ser_vi_layoutxlm.yml -o_ser Architecture.Backbone.checkpoints=fapiao/models/ser_vi_layoutxlm_fapiao_udml/best_accuracy
+python3 tools/infer_kie_token_ser_re.py -c fapiao/re_vi_layoutxlm.yml -o Architecture.Backbone.checkpoints=fapiao/models/re_vi_layoutxlm_fapiao_trained/best_accuracy Global.infer_img=./train_data/zzsfp/val.json Global.infer_mode=False -c_ser fapiao/ser_vi_layoutxlm.yml -o_ser Architecture.Backbone.checkpoints=fapiao/models/ser_vi_layoutxlm_fapiao_trained/best_accuracy
 ```
 
 预测结果会保存在配置文件中的`Global.save_res_path`目录中。
@@ -333,11 +333,11 @@ python3 tools/infer_vqa_token_ser_re.py -c fapiao/re_vi_layoutxlm.yml -o Archite
 如果希望使用OCR引擎结果得到的结果进行推理，则可以使用下面的命令进行推理。
 
 ```bash
-python3 tools/infer_vqa_token_ser_re.py -c fapiao/re_vi_layoutxlm.yml -o Architecture.Backbone.checkpoints=fapiao/models/re_vi_layoutxlm_fapiao_udml/best_accuracy Global.infer_img=./train_data/zzsfp/val.json Global.infer_mode=True -c_ser fapiao/ser_vi_layoutxlm.yml -o_ser Architecture.Backbone.checkpoints=fapiao/models/ser_vi_layoutxlm_fapiao_udml/best_accuracy
+python3 tools/infer_kie_token_ser_re.py -c fapiao/re_vi_layoutxlm.yml -o Architecture.Backbone.checkpoints=fapiao/models/re_vi_layoutxlm_fapiao_udml/best_accuracy Global.infer_img=./train_data/zzsfp/val.json Global.infer_mode=True -c_ser fapiao/ser_vi_layoutxlm.yml -o_ser Architecture.Backbone.checkpoints=fapiao/models/ser_vi_layoutxlm_fapiao_udml/best_accuracy
 ```
 
 如果希望构建基于你在垂类场景训练得到的OCR检测与识别模型，可以使用下面的方法传入，即可完成SER + RE的串联过程。
 
 ```bash
-python3 tools/infer_vqa_token_ser_re.py -c fapiao/re_vi_layoutxlm.yml -o Architecture.Backbone.checkpoints=fapiao/models/re_vi_layoutxlm_fapiao_udml/best_accuracy Global.infer_img=./train_data/zzsfp/val.json Global.infer_mode=True -c_ser fapiao/ser_vi_layoutxlm.yml -o_ser Architecture.Backbone.checkpoints=fapiao/models/ser_vi_layoutxlm_fapiao_udml/best_accuracy Global.kie_rec_model_dir="your_rec_model" Global.kie_det_model_dir="your_det_model"
+python3 tools/infer_kie_token_ser_re.py -c fapiao/re_vi_layoutxlm.yml -o Architecture.Backbone.checkpoints=fapiao/models/re_vi_layoutxlm_fapiao_udml/best_accuracy Global.infer_img=./train_data/zzsfp/val.json Global.infer_mode=True -c_ser fapiao/ser_vi_layoutxlm.yml -o_ser Architecture.Backbone.checkpoints=fapiao/models/ser_vi_layoutxlm_fapiao_udml/best_accuracy Global.kie_rec_model_dir="your_rec_model" Global.kie_det_model_dir="your_det_model"
 ```

ppstructure/kie/README.md

@@ -172,16 +172,16 @@ If you want to use OCR engine to obtain end-to-end prediction results, you can u
 # just predict using SER trained model
 python3 tools/infer_kie_token_ser.py \
   -c configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh.yml \
-  -o Architecture.Backbone.checkpoints=./pretrain_models/ser_vi_layoutxlm_xfund_pretrained/best_accuracy \
+  -o Architecture.Backbone.checkpoints=./pretrained_model/ser_vi_layoutxlm_xfund_pretrained/best_accuracy \
   Global.infer_img=./ppstructure/docs/kie/input/zh_val_42.jpg
 
 # predict using SER and RE trained model at the same time
 python3 ./tools/infer_kie_token_ser_re.py \
   -c configs/kie/vi_layoutxlm/re_vi_layoutxlm_xfund_zh.yml \
-  -o Architecture.Backbone.checkpoints=./pretrain_models/re_vi_layoutxlm_xfund_pretrained/best_accuracy \
+  -o Architecture.Backbone.checkpoints=./pretrained_model/re_vi_layoutxlm_xfund_pretrained/best_accuracy \
   Global.infer_img=./train_data/XFUND/zh_val/image/zh_val_42.jpg \
   -c_ser configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh.yml \
-  -o_ser Architecture.Backbone.checkpoints=./pretrain_models/ser_vi_layoutxlm_xfund_pretrained/best_accuracy
+  -o_ser Architecture.Backbone.checkpoints=./pretrained_model/ser_vi_layoutxlm_xfund_pretrained/best_accuracy
 ```
 
 The visual result images and the predicted text file will be saved in the `Global.save_res_path` directory.
@@ -193,18 +193,18 @@ If you want to load the text detection and recognition results collected before,
 # just predict using SER trained model
 python3 tools/infer_kie_token_ser.py \
   -c configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh.yml \
-  -o Architecture.Backbone.checkpoints=./pretrain_models/ser_vi_layoutxlm_xfund_pretrained/best_accuracy \
+  -o Architecture.Backbone.checkpoints=./pretrained_model/ser_vi_layoutxlm_xfund_pretrained/best_accuracy \
   Global.infer_img=./train_data/XFUND/zh_val/val.json \
   Global.infer_mode=False
 
 # predict using SER and RE trained model at the same time
 python3 ./tools/infer_kie_token_ser_re.py \
   -c configs/kie/vi_layoutxlm/re_vi_layoutxlm_xfund_zh.yml \
-  -o Architecture.Backbone.checkpoints=./pretrain_models/re_vi_layoutxlm_xfund_pretrained/best_accuracy \
+  -o Architecture.Backbone.checkpoints=./pretrained_model/re_vi_layoutxlm_xfund_pretrained/best_accuracy \
   Global.infer_img=./train_data/XFUND/zh_val/val.json \
   Global.infer_mode=False \
   -c_ser configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh.yml \
-  -o_ser Architecture.Backbone.checkpoints=./pretrain_models/ser_vi_layoutxlm_xfund_pretrained/best_accuracy
+  -o_ser Architecture.Backbone.checkpoints=./pretrained_model/ser_vi_layoutxlm_xfund_pretrained/best_accuracy
 ```
 
 #### 4.2.3 Inference using PaddleInference

ppstructure/kie/README_ch.md

@@ -156,16 +156,16 @@ wget https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/re_vi_layou
 # 仅预测SER模型
 python3 tools/infer_kie_token_ser.py \
   -c configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh.yml \
-  -o Architecture.Backbone.checkpoints=./pretrain_models/ser_vi_layoutxlm_xfund_pretrained/best_accuracy \
+  -o Architecture.Backbone.checkpoints=./pretrained_model/ser_vi_layoutxlm_xfund_pretrained/best_accuracy \
   Global.infer_img=./ppstructure/docs/kie/input/zh_val_42.jpg
 
 # SER + RE模型串联
 python3 ./tools/infer_kie_token_ser_re.py \
   -c configs/kie/vi_layoutxlm/re_vi_layoutxlm_xfund_zh.yml \
-  -o Architecture.Backbone.checkpoints=./pretrain_models/re_vi_layoutxlm_xfund_pretrained/best_accuracy \
+  -o Architecture.Backbone.checkpoints=./pretrained_model/re_vi_layoutxlm_xfund_pretrained/best_accuracy \
   Global.infer_img=./train_data/XFUND/zh_val/image/zh_val_42.jpg \
   -c_ser configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh.yml \
-  -o_ser Architecture.Backbone.checkpoints=./pretrain_models/ser_vi_layoutxlm_xfund_pretrained/best_accuracy
+  -o_ser Architecture.Backbone.checkpoints=./pretrained_model/ser_vi_layoutxlm_xfund_pretrained/best_accuracy
 ```
 
 `Global.save_res_path`目录中会保存可视化的结果图像以及预测的文本文件。
@@ -177,18 +177,18 @@ python3 ./tools/infer_kie_token_ser_re.py \
 # 仅预测SER模型
 python3 tools/infer_kie_token_ser.py \
   -c configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh.yml \
-  -o Architecture.Backbone.checkpoints=./pretrain_models/ser_vi_layoutxlm_xfund_pretrained/best_accuracy \
+  -o Architecture.Backbone.checkpoints=./pretrained_model/ser_vi_layoutxlm_xfund_pretrained/best_accuracy \
   Global.infer_img=./train_data/XFUND/zh_val/val.json \
   Global.infer_mode=False
 
 # SER + RE模型串联
 python3 ./tools/infer_kie_token_ser_re.py \
   -c configs/kie/vi_layoutxlm/re_vi_layoutxlm_xfund_zh.yml \
-  -o Architecture.Backbone.checkpoints=./pretrain_models/re_vi_layoutxlm_xfund_pretrained/best_accuracy \
+  -o Architecture.Backbone.checkpoints=./pretrained_model/re_vi_layoutxlm_xfund_pretrained/best_accuracy \
   Global.infer_img=./train_data/XFUND/zh_val/val.json \
   Global.infer_mode=False \
   -c_ser configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh.yml \
-  -o_ser Architecture.Backbone.checkpoints=./pretrain_models/ser_vi_layoutxlm_xfund_pretrained/best_accuracy
+  -o_ser Architecture.Backbone.checkpoints=./pretrained_model/ser_vi_layoutxlm_xfund_pretrained/best_accuracy
 ```
 
 #### 4.2.3 基于PaddleInference的预测