Merge pull request #7666 from WenmuZhou/re1

add re predict

configs/kie/layoutlm_series/re_layoutxlm_xfund_zh.yml

@@ -68,6 +68,7 @@ Train:
       - VQAReTokenRelation:
       - VQAReTokenChunk:
           max_seq_len: *max_seq_len
+      - TensorizeEntitiesRelations:
       - Resize:
           size: [224,224]
       - NormalizeImage:
@@ -83,7 +84,6 @@ Train:
     drop_last: False
     batch_size_per_card: 2
     num_workers: 8
-    collate_fn: ListCollator
 
 Eval:
   dataset:
@@ -105,6 +105,7 @@ Eval:
       - VQAReTokenRelation:
       - VQAReTokenChunk:
           max_seq_len: *max_seq_len
+      - TensorizeEntitiesRelations:
       - Resize:
           size: [224,224]
       - NormalizeImage:
@@ -120,4 +121,3 @@ Eval:
     drop_last: False
     batch_size_per_card: 8
     num_workers: 8
-    collate_fn: ListCollator

configs/kie/vi_layoutxlm/re_vi_layoutxlm_xfund_zh.yml

@@ -73,6 +73,7 @@ Train:
       - VQAReTokenRelation:
       - VQAReTokenChunk:
           max_seq_len: *max_seq_len
+      - TensorizeEntitiesRelations:
       - Resize:
           size: [224,224]
       - NormalizeImage:
@@ -82,13 +83,12 @@ Train:
           order: 'hwc'
       - ToCHWImage:
       - KeepKeys:
-          keep_keys: [ 'input_ids', 'bbox','attention_mask', 'token_type_ids', 'image', 'entities', 'relations'] # dataloader will return list in this order
+          keep_keys: [ 'input_ids', 'bbox','attention_mask', 'token_type_ids', 'entities', 'relations'] # dataloader will return list in this order
   loader:
     shuffle: True
     drop_last: False
     batch_size_per_card: 2
     num_workers: 4
-    collate_fn: ListCollator
 
 Eval:
   dataset:
@@ -112,6 +112,7 @@ Eval:
       - VQAReTokenRelation:
       - VQAReTokenChunk:
           max_seq_len: *max_seq_len
+      - TensorizeEntitiesRelations:
       - Resize:
           size: [224,224]
       - NormalizeImage:
@@ -121,11 +122,9 @@ Eval:
           order: 'hwc'
       - ToCHWImage:
       - KeepKeys:
-          keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'entities', 'relations'] # dataloader will return list in this order
+          keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'entities', 'relations'] # dataloader will return list in this order
   loader:
     shuffle: False
     drop_last: False
     batch_size_per_card: 8
     num_workers: 8
-    collate_fn: ListCollator
-

configs/kie/vi_layoutxlm/re_vi_layoutxlm_xfund_zh_udml.yml

@@ -57,14 +57,16 @@ Loss:
       mode: "l2"
       model_name_pairs:
         - ["Student", "Teacher"]
-      key: hidden_states_5
+      key: hidden_states
+      index: 5
       name: "loss_5"
   - DistillationVQADistanceLoss:
       weight: 0.5
       mode: "l2"
       model_name_pairs:
         - ["Student", "Teacher"]
-      key: hidden_states_8
+      key: hidden_states
+      index: 8
       name: "loss_8"
 
 
@@ -116,6 +118,7 @@ Train:
       - VQAReTokenRelation:
       - VQAReTokenChunk:
           max_seq_len: *max_seq_len
+      - TensorizeEntitiesRelations:
       - Resize:
           size: [224,224]
       - NormalizeImage:
@@ -125,13 +128,12 @@ Train:
           order: 'hwc'
       - ToCHWImage:
       - KeepKeys:
-          keep_keys: [ 'input_ids', 'bbox','attention_mask', 'token_type_ids', 'image', 'entities', 'relations'] # dataloader will return list in this order
+          keep_keys: [ 'input_ids', 'bbox','attention_mask', 'token_type_ids', 'entities', 'relations'] # dataloader will return list in this order
   loader:
     shuffle: True
     drop_last: False
     batch_size_per_card: 2
     num_workers: 4
-    collate_fn: ListCollator
 
 Eval:
   dataset:
@@ -155,6 +157,7 @@ Eval:
       - VQAReTokenRelation:
       - VQAReTokenChunk:
           max_seq_len: *max_seq_len
+      - TensorizeEntitiesRelations:
       - Resize:
           size: [224,224]
       - NormalizeImage:
@@ -164,12 +167,11 @@ Eval:
           order: 'hwc'
       - ToCHWImage:
       - KeepKeys:
-          keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'entities', 'relations'] # dataloader will return list in this order
+          keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'entities', 'relations'] # dataloader will return list in this order
   loader:
     shuffle: False
     drop_last: False
     batch_size_per_card: 8
     num_workers: 8
-    collate_fn: ListCollator
 
 

configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh_udml.yml

@@ -70,14 +70,16 @@ Loss:
       mode: "l2"
       model_name_pairs:
         - ["Student", "Teacher"]
-      key: hidden_states_5
+      key: hidden_states
+      index: 5
       name: "loss_5"
   - DistillationVQADistanceLoss:
       weight: 0.5
       mode: "l2"
       model_name_pairs:
         - ["Student", "Teacher"]
-      key: hidden_states_8
+      key: hidden_states
+      index: 8
       name: "loss_8"
   
   

doc/doc_ch/algorithm_kie_layoutxlm.md

@@ -30,7 +30,7 @@
 |模型|骨干网络|任务|配置文件|hmean|下载链接|
 | --- | --- |--|--- | --- | --- |
 |LayoutXLM|LayoutXLM-base|SER |[ser_layoutxlm_xfund_zh.yml](../../configs/kie/layoutlm_series/ser_layoutxlm_xfund_zh.yml)|90.38%|[训练模型](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutXLM_xfun_zh.tar)/[推理模型](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutXLM_xfun_zh_infer.tar)|
-|LayoutXLM|LayoutXLM-base|RE | [re_layoutxlm_xfund_zh.yml](../../configs/kie/layoutlm_series/re_layoutxlm_xfund_zh.yml)|74.83%|[训练模型](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutXLM_xfun_zh.tar)/[推理模型(coming soon)]()|
+|LayoutXLM|LayoutXLM-base|RE | [re_layoutxlm_xfund_zh.yml](../../configs/kie/layoutlm_series/re_layoutxlm_xfund_zh.yml)|74.83%|[训练模型](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutXLM_xfun_zh.tar)/[推理模型](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutXLM_xfun_zh_infer.tar)|
 
 <a name="2"></a>
 
@@ -52,14 +52,14 @@
 
 ### 4.1 Python推理
 
-**注：** 目前RE任务推理过程仍在适配中，下面以SER任务为例，介绍基于LayoutXLM模型的关键信息抽取过程。
+- SER
 
 首先将训练得到的模型转换成inference model。LayoutXLM模型在XFUND_zh数据集上训练的模型为例（[模型下载地址](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutXLM_xfun_zh.tar)），可以使用下面的命令进行转换。
 
 ``` bash
 wget https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutXLM_xfun_zh.tar
 tar -xf ser_LayoutXLM_xfun_zh.tar
-python3 tools/export_model.py -c configs/kie/layoutlm_series/ser_layoutxlm_xfund_zh.yml -o Architecture.Backbone.checkpoints=./ser_LayoutXLM_xfun_zh/best_accuracy Global.save_inference_dir=./inference/ser_layoutxlm
+python3 tools/export_model.py -c configs/kie/layoutlm_series/ser_layoutxlm_xfund_zh.yml -o Architecture.Backbone.checkpoints=./ser_LayoutXLM_xfun_zh Global.save_inference_dir=./inference/ser_layoutxlm_infer
 ```
 
 LayoutXLM模型基于SER任务进行推理，可以执行如下命令：
@@ -80,6 +80,34 @@ SER可视化结果默认保存到`./output`文件夹里面，结果示例如下
     <img src="../../ppstructure/docs/kie/result_ser/zh_val_42_ser.jpg" width="800">
 </div>
 
+- RE
+
+首先将训练得到的模型转换成inference model。LayoutXLM模型在XFUND_zh数据集上训练的模型为例（[模型下载地址](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutXLM_xfun_zh.tar)），可以使用下面的命令进行转换。
+
+``` bash
+wget https://paddleocr.bj.bcebos.com/pplayout/re_LayoutXLM_xfun_zh.tar
+tar -xf re_LayoutXLM_xfun_zh.tar
+python3 tools/export_model.py -c configs/kie/layoutlm_series/re_layoutxlm_xfund_zh.yml -o Architecture.Backbone.checkpoints=./re_LayoutXLM_xfun_zh Global.save_inference_dir=./inference/ser_layoutxlm_infer
+```
+
+LayoutXLM模型基于RE任务进行推理，可以执行如下命令：
+
+```bash
+cd ppstructure
+python3 kie/predict_kie_token_ser_re.py \
+  --kie_algorithm=LayoutXLM \
+  --re_model_dir=../inference/re_layoutxlm_infer \
+  --ser_model_dir=../inference/ser_layoutxlm_infer \
+  --image_dir=./docs/kie/input/zh_val_42.jpg \
+  --ser_dict_path=../train_data/XFUND/class_list_xfun.txt \
+  --vis_font_path=../doc/fonts/simfang.ttf
+```
+
+RE可视化结果默认保存到`./output`文件夹里面，结果示例如下：
+
+<div align="center">
+    <img src="../../ppstructure/docs/kie/result_re/zh_val_42_re.jpg" width="800">
+</div>
 
 <a name="4-2"></a>
 ### 4.2 C++推理部署

doc/doc_ch/algorithm_kie_vi_layoutxlm.md

@@ -23,7 +23,7 @@ VI-LayoutXLM基于LayoutXLM进行改进，在下游任务训练过程中，去
 |模型|骨干网络|任务|配置文件|hmean|下载链接|
 | --- | --- |---| --- | --- | --- |
 |VI-LayoutXLM |VI-LayoutXLM-base | SER |[ser_vi_layoutxlm_xfund_zh_udml.yml](../../configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh_udml.yml)|93.19%|[训练模型](https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/ser_vi_layoutxlm_xfund_pretrained.tar)/[推理模型](https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/ser_vi_layoutxlm_xfund_infer.tar)|
-|VI-LayoutXLM |VI-LayoutXLM-base |RE | [re_vi_layoutxlm_xfund_zh_udml.yml](../../configs/kie/vi_layoutxlm/re_vi_layoutxlm_xfund_zh_udml.yml)|83.92%|[训练模型](https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/re_vi_layoutxlm_xfund_pretrained.tar)/[推理模型(coming soon)]()|
+|VI-LayoutXLM |VI-LayoutXLM-base |RE | [re_vi_layoutxlm_xfund_zh_udml.yml](../../configs/kie/vi_layoutxlm/re_vi_layoutxlm_xfund_zh_udml.yml)|83.92%|[训练模型](https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/re_vi_layoutxlm_xfund_pretrained.tar)/[推理模型](https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/re_vi_layoutxlm_xfund_infer.tar)|
 
 <a name="2"></a>
 
@@ -45,7 +45,7 @@ VI-LayoutXLM基于LayoutXLM进行改进，在下游任务训练过程中，去
 
 ### 4.1 Python推理
 
-**注：** 目前RE任务推理过程仍在适配中，下面以SER任务为例，介绍基于VI-LayoutXLM模型的关键信息抽取过程。
+- SER
 
 首先将训练得到的模型转换成inference model。以VI-LayoutXLM模型在XFUND_zh数据集上训练的模型为例（[模型下载地址](https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/ser_vi_layoutxlm_xfund_pretrained.tar)），可以使用下面的命令进行转换。
 
@@ -74,6 +74,36 @@ SER可视化结果默认保存到`./output`文件夹里面，结果示例如下
     <img src="../../ppstructure/docs/kie/result_ser/zh_val_42_ser.jpg" width="800">
 </div>
 
+- RE
+
+首先将训练得到的模型转换成inference model。以VI-LayoutXLM模型在XFUND_zh数据集上训练的模型为例（[模型下载地址](https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/re_vi_layoutxlm_xfund_pretrained.tar)），可以使用下面的命令进行转换。
+
+``` bash
+wget https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/re_vi_layoutxlm_xfund_pretrained.tar
+tar -xf re_vi_layoutxlm_xfund_pretrained.tar
+python3 tools/export_model.py -c configs/kie/vi_layoutxlm/re_vi_layoutxlm_xfund_zh.yml -o Architecture.Backbone.checkpoints=./re_vi_layoutxlm_xfund_pretrained/best_accuracy Global.save_inference_dir=./inference/re_vi_layoutxlm_infer
+```
+
+VI-LayoutXLM模型基于RE任务进行推理，可以执行如下命令：
+
+```bash
+cd ppstructure
+python3 kie/predict_kie_token_ser_re.py \
+  --kie_algorithm=LayoutXLM \
+  --re_model_dir=../inference/re_vi_layoutxlm_infer \
+  --ser_model_dir=../inference/ser_vi_layoutxlm_infer \
+  --use_visual_backbone=False \
+  --image_dir=./docs/kie/input/zh_val_42.jpg \
+  --ser_dict_path=../train_data/XFUND/class_list_xfun.txt \
+  --vis_font_path=../doc/fonts/simfang.ttf \
+  --ocr_order_method="tb-yx"
+```
+
+RE可视化结果默认保存到`./output`文件夹里面，结果示例如下：
+
+<div align="center">
+    <img src="../../ppstructure/docs/kie/result_re/zh_val_42_re.jpg" width="800">
+</div>
 
 <a name="4-2"></a>
 ### 4.2 C++推理部署

doc/doc_en/algorithm_kie_layoutxlm_en.md

@@ -28,7 +28,7 @@ On XFUND_zh dataset, the algorithm reproduction Hmean is as follows.
 |Model|Backbone|Task |Cnnfig|Hmean|Download link|
 | --- | --- |--|--- | --- | --- |
 |LayoutXLM|LayoutXLM-base|SER |[ser_layoutxlm_xfund_zh.yml](../../configs/kie/layoutlm_series/ser_layoutxlm_xfund_zh.yml)|90.38%|[trained model](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutXLM_xfun_zh.tar)/[inference model](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutXLM_xfun_zh_infer.tar)|
-|LayoutXLM|LayoutXLM-base|RE | [re_layoutxlm_xfund_zh.yml](../../configs/kie/layoutlm_series/re_layoutxlm_xfund_zh.yml)|74.83%|[trained model](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutXLM_xfun_zh.tar)/[inference model(coming soon)]()|
+|LayoutXLM|LayoutXLM-base|RE | [re_layoutxlm_xfund_zh.yml](../../configs/kie/layoutlm_series/re_layoutxlm_xfund_zh.yml)|74.83%|[trained model](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutXLM_xfun_zh.tar)/[inference model](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutXLM_xfun_zh_infer.tar)|
 
 
 ## 2. Environment
@@ -46,7 +46,7 @@ Please refer to [KIE tutorial](./kie_en.md)。PaddleOCR has modularized the code
 
 ### 4.1 Python Inference
 
-**Note:** Currently, the RE model inference process is still in the process of adaptation. We take SER model as an example to introduce the KIE process based on LayoutXLM model.
+- SER
 
 First, we need to export the trained model into inference model. Take LayoutXLM model trained on XFUND_zh as an example ([trained model download link](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutXLM_xfun_zh.tar)). Use the following command to export.
 
@@ -54,7 +54,7 @@ First, we need to export the trained model into inference model. Take LayoutXLM
 ``` bash
 wget https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutXLM_xfun_zh.tar
 tar -xf ser_LayoutXLM_xfun_zh.tar
-python3 tools/export_model.py -c configs/kie/layoutlm_series/ser_layoutxlm_xfund_zh.yml -o Architecture.Backbone.checkpoints=./ser_LayoutXLM_xfun_zh/best_accuracy Global.save_inference_dir=./inference/ser_layoutxlm
+python3 tools/export_model.py -c configs/kie/layoutlm_series/ser_layoutxlm_xfund_zh.yml -o Architecture.Backbone.checkpoints=./ser_LayoutXLM_xfun_zh Global.save_inference_dir=./inference/ser_layoutxlm_infer
 ```
 
 Use the following command to infer using LayoutXLM SER model.
@@ -77,6 +77,38 @@ The SER visualization results are saved in the `./output` directory by default.
 </div>
 
 
+- RE
+
+First, we need to export the trained model into inference model. Take LayoutXLM model trained on XFUND_zh as an example ([trained model download link](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutXLM_xfun_zh.tar)). Use the following command to export.
+
+
+``` bash
+wget https://paddleocr.bj.bcebos.com/pplayout/re_LayoutXLM_xfun_zh.tar
+tar -xf re_LayoutXLM_xfun_zh.tar
+python3 tools/export_model.py -c configs/kie/layoutlm_series/re_layoutxlm_xfund_zh.yml -o Architecture.Backbone.checkpoints=./re_LayoutXLM_xfun_zh Global.save_inference_dir=./inference/re_layoutxlm_infer
+```
+
+Use the following command to infer using LayoutXLM RE model.
+
+
+```bash
+cd ppstructure
+python3 kie/predict_kie_token_ser_re.py \
+  --kie_algorithm=LayoutXLM \
+  --re_model_dir=../inference/re_layoutxlm_infer \
+  --ser_model_dir=../inference/ser_layoutxlm_infer \
+  --image_dir=./docs/kie/input/zh_val_42.jpg \
+  --ser_dict_path=../train_data/XFUND/class_list_xfun.txt \
+  --vis_font_path=../doc/fonts/simfang.ttf
+```
+The RE visualization results are saved in the `./output` directory by default. The results are as follows.
+
+
+<div align="center">
+    <img src="../../ppstructure/docs/kie/result_re/zh_val_42_re.jpg" width="800">
+</div>
+
+
 ### 4.2 C++ Inference
 
 Not supported

doc/doc_en/algorithm_kie_vi_layoutxlm_en.md

@@ -22,7 +22,7 @@ On XFUND_zh dataset, the algorithm reproduction Hmean is as follows.
 |Model|Backbone|Task |Cnnfig|Hmean|Download link|
 | --- | --- |---| --- | --- | --- |
 |VI-LayoutXLM |VI-LayoutXLM-base | SER |[ser_vi_layoutxlm_xfund_zh_udml.yml](../../configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh_udml.yml)|93.19%|[trained model](https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/ser_vi_layoutxlm_xfund_pretrained.tar)/[inference model](https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/ser_vi_layoutxlm_xfund_infer.tar)|
-|VI-LayoutXLM |VI-LayoutXLM-base |RE | [re_vi_layoutxlm_xfund_zh_udml.yml](../../configs/kie/vi_layoutxlm/re_vi_layoutxlm_xfund_zh_udml.yml)|83.92%|[trained model](https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/re_vi_layoutxlm_xfund_pretrained.tar)/[inference model(coming soon)]()|
+|VI-LayoutXLM |VI-LayoutXLM-base |RE | [re_vi_layoutxlm_xfund_zh_udml.yml](../../configs/kie/vi_layoutxlm/re_vi_layoutxlm_xfund_zh_udml.yml)|83.92%|[trained model](https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/re_vi_layoutxlm_xfund_pretrained.tar)/[inference model](https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/re_vi_layoutxlm_xfund_infer.tar)|
 
 
 Please refer to ["Environment Preparation"](./environment_en.md) to configure the PaddleOCR environment, and refer to ["Project Clone"](./clone_en.md) to clone the project code.
@@ -37,7 +37,7 @@ Please refer to [KIE tutorial](./kie_en.md)。PaddleOCR has modularized the code
 
 ### 4.1 Python Inference
 
-**Note:** Currently, the RE model inference process is still in the process of adaptation. We take SER model as an example to introduce the KIE process based on VI-LayoutXLM model.
+- SER
 
 First, we need to export the trained model into inference model. Take VI-LayoutXLM model trained on XFUND_zh as an example ([trained model download link](https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/ser_vi_layoutxlm_xfund_pretrained.tar)). Use the following command to export.
 
@@ -70,6 +70,41 @@ The SER visualization results are saved in the `./output` folder by default. The
 </div>
 
 
+- RE
+
+First, we need to export the trained model into inference model. Take VI-LayoutXLM model trained on XFUND_zh as an example ([trained model download link](https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/re_vi_layoutxlm_xfund_pretrained.tar)). Use the following command to export.
+
+
+``` bash
+wget https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/re_vi_layoutxlm_xfund_pretrained.tar
+tar -xf re_vi_layoutxlm_xfund_pretrained.tar
+python3 tools/export_model.py -c configs/kie/vi_layoutxlm/re_vi_layoutxlm_xfund_zh.yml -o Architecture.Backbone.checkpoints=./re_vi_layoutxlm_xfund_pretrained/best_accuracy Global.save_inference_dir=./inference/re_vi_layoutxlm_infer
+```
+
+Use the following command to infer using VI-LayoutXLM RE model.
+
+
+```bash
+cd ppstructure
+python3 kie/predict_kie_token_ser_re.py \
+  --kie_algorithm=LayoutXLM \
+  --re_model_dir=../inference/re_vi_layoutxlm_infer \
+  --ser_model_dir=../inference/ser_vi_layoutxlm_infer \
+  --use_visual_backbone=False \
+  --image_dir=./docs/kie/input/zh_val_42.jpg \
+  --ser_dict_path=../train_data/XFUND/class_list_xfun.txt \
+  --vis_font_path=../doc/fonts/simfang.ttf \
+  --ocr_order_method="tb-yx"
+```
+
+The RE visualization results are saved in the `./output` folder by default. The results are as follows.
+
+
+<div align="center">
+    <img src="../../ppstructure/docs/kie/result_re/zh_val_42_re.jpg" width="800">
+</div>
+
+
 ### 4.2 C++ Inference
 
 Not supported

ppocr/data/imaug/vqa/__init__.py

@@ -12,11 +12,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from .token import VQATokenPad, VQASerTokenChunk, VQAReTokenChunk, VQAReTokenRelation
+from .token import VQATokenPad, VQASerTokenChunk, VQAReTokenChunk, VQAReTokenRelation, TensorizeEntitiesRelations
 
 __all__ = [
-    'VQATokenPad',
-    'VQASerTokenChunk',
-    'VQAReTokenChunk',
-    'VQAReTokenRelation',
+    'VQATokenPad', 'VQASerTokenChunk', 'VQAReTokenChunk', 'VQAReTokenRelation',
+    'TensorizeEntitiesRelations'
 ]

ppocr/data/imaug/vqa/token/__init__.py

@@ -15,3 +15,4 @@
 from .vqa_token_chunk import VQASerTokenChunk, VQAReTokenChunk
 from .vqa_token_pad import VQATokenPad
 from .vqa_token_relation import VQAReTokenRelation
+from .vqa_re_convert import TensorizeEntitiesRelations
\ No newline at end of file

ppocr/data/imaug/vqa/token/vqa_re_convert.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.
+
+import numpy as np
+
+
+class TensorizeEntitiesRelations(object):
+    def __init__(self, max_seq_len=512, infer_mode=False, **kwargs):
+        self.max_seq_len = max_seq_len
+        self.infer_mode = infer_mode
+
+    def __call__(self, data):
+        entities = data['entities']
+        relations = data['relations']
+
+        entities_new = np.full(
+            shape=[self.max_seq_len + 1, 3], fill_value=-1, dtype='int64')
+        entities_new[0, 0] = len(entities['start'])
+        entities_new[0, 1] = len(entities['end'])
+        entities_new[0, 2] = len(entities['label'])
+        entities_new[1:len(entities['start']) + 1, 0] = np.array(entities[
+            'start'])
+        entities_new[1:len(entities['end']) + 1, 1] = np.array(entities['end'])
+        entities_new[1:len(entities['label']) + 1, 2] = np.array(entities[
+            'label'])
+
+        relations_new = np.full(
+            shape=[self.max_seq_len * self.max_seq_len + 1, 2],
+            fill_value=-1,
+            dtype='int64')
+        relations_new[0, 0] = len(relations['head'])
+        relations_new[0, 1] = len(relations['tail'])
+        relations_new[1:len(relations['head']) + 1, 0] = np.array(relations[
+            'head'])
+        relations_new[1:len(relations['tail']) + 1, 1] = np.array(relations[
+            'tail'])
+
+        data['entities'] = entities_new
+        data['relations'] = relations_new
+        return data

ppocr/losses/distillation_loss.py

@@ -417,11 +417,13 @@ class DistillationVQADistanceLoss(DistanceLoss):
                  mode="l2",
                  model_name_pairs=[],
                  key=None,
+                 index=None,
                  name="loss_distance",
                  **kargs):
         super().__init__(mode=mode, **kargs)
         assert isinstance(model_name_pairs, list)
         self.key = key
+        self.index = index
         self.model_name_pairs = model_name_pairs
         self.name = name + "_l2"
 
@@ -434,6 +436,9 @@ class DistillationVQADistanceLoss(DistanceLoss):
             if self.key is not None:
                 out1 = out1[self.key]
                 out2 = out2[self.key]
+                if self.index is not None:
+                    out1 = out1[:, self.index, :, :]
+                    out2 = out2[:, self.index, :, :]
                 if attention_mask is not None:
                     max_len = attention_mask.shape[-1]
                     out1 = out1[:, :max_len]

ppocr/metrics/vqa_token_re_metric.py

@@ -37,23 +37,25 @@ class VQAReTokenMetric(object):
         gt_relations = []
         for b in range(len(self.relations_list)):
             rel_sent = []
-            if "head" in self.relations_list[b]:
-                for head, tail in zip(self.relations_list[b]["head"],
-                                      self.relations_list[b]["tail"]):
+            relation_list = self.relations_list[b]
+            entitie_list = self.entities_list[b]
+            head_len = relation_list[0, 0]
+            if head_len > 0:
+                entitie_start_list = entitie_list[1:entitie_list[0, 0] + 1, 0]
+                entitie_end_list = entitie_list[1:entitie_list[0, 1] + 1, 1]
+                entitie_label_list = entitie_list[1:entitie_list[0, 2] + 1, 2]
+                for head, tail in zip(relation_list[1:head_len + 1, 0],
+                                      relation_list[1:head_len + 1, 1]):
                     rel = {}
                     rel["head_id"] = head
-                    rel["head"] = (
-                        self.entities_list[b]["start"][rel["head_id"]],
-                        self.entities_list[b]["end"][rel["head_id"]])
-                    rel["head_type"] = self.entities_list[b]["label"][rel[
-                        "head_id"]]
+                    rel["head"] = (entitie_start_list[head],
+                                   entitie_end_list[head])
+                    rel["head_type"] = entitie_label_list[head]
 
                     rel["tail_id"] = tail
-                    rel["tail"] = (
-                        self.entities_list[b]["start"][rel["tail_id"]],
-                        self.entities_list[b]["end"][rel["tail_id"]])
-                    rel["tail_type"] = self.entities_list[b]["label"][rel[
-                        "tail_id"]]
+                    rel["tail"] = (entitie_start_list[tail],
+                                   entitie_end_list[tail])
+                    rel["tail_type"] = entitie_label_list[tail]
 
                     rel["type"] = 1
                     rel_sent.append(rel)

ppocr/modeling/backbones/vqa_layoutlm.py

@@ -218,8 +218,12 @@ class LayoutXLMForRe(NLPBaseModel):
     def forward(self, x):
         if self.use_visual_backbone is True:
             image = x[4]
+            entities = x[5]
+            relations = x[6]
         else:
             image = None
+            entities = x[4]
+            relations = x[5]
         x = self.model(
             input_ids=x[0],
             bbox=x[1],
@@ -229,6 +233,6 @@ class LayoutXLMForRe(NLPBaseModel):
             position_ids=None,
             head_mask=None,
             labels=None,
-            entities=x[5],
-            relations=x[6])
+            entities=entities,
+            relations=relations)
         return x

ppocr/postprocess/vqa_token_re_layoutlm_postprocess.py

@@ -21,18 +21,22 @@ class VQAReTokenLayoutLMPostProcess(object):
         super(VQAReTokenLayoutLMPostProcess, self).__init__()
 
     def __call__(self, preds, label=None, *args, **kwargs):
+        pred_relations = preds['pred_relations']
+        if isinstance(preds['pred_relations'], paddle.Tensor):
+            pred_relations = pred_relations.numpy()
+        pred_relations = self.decode_pred(pred_relations)
+
         if label is not None:
-            return self._metric(preds, label)
+            return self._metric(pred_relations, label)
         else:
-            return self._infer(preds, *args, **kwargs)
+            return self._infer(pred_relations, *args, **kwargs)
 
-    def _metric(self, preds, label):
-        return preds['pred_relations'], label[6], label[5]
+    def _metric(self, pred_relations, label):
+        return pred_relations, label[-1], label[-2]
 
-    def _infer(self, preds, *args, **kwargs):
+    def _infer(self, pred_relations, *args, **kwargs):
         ser_results = kwargs['ser_results']
         entity_idx_dict_batch = kwargs['entity_idx_dict_batch']
-        pred_relations = preds['pred_relations']
 
         # merge relations and ocr info
         results = []
@@ -50,6 +54,24 @@ class VQAReTokenLayoutLMPostProcess(object):
             results.append(result)
         return results
 
+    def decode_pred(self, pred_relations):
+        pred_relations_new = []
+        for pred_relation in pred_relations:
+            pred_relation_new = []
+            pred_relation = pred_relation[1:pred_relation[0, 0, 0] + 1]
+            for relation in pred_relation:
+                relation_new = dict()
+                relation_new['head_id'] = relation[0, 0]
+                relation_new['head'] = tuple(relation[1])
+                relation_new['head_type'] = relation[2, 0]
+                relation_new['tail_id'] = relation[3, 0]
+                relation_new['tail'] = tuple(relation[4])
+                relation_new['tail_type'] = relation[5, 0]
+                relation_new['type'] = relation[6, 0]
+                pred_relation_new.append(relation_new)
+            pred_relations_new.append(pred_relation_new)
+        return pred_relations_new
+
 
 class DistillationRePostProcess(VQAReTokenLayoutLMPostProcess):
     """

ppstructure/docs/models_list.md

@@ -51,9 +51,9 @@
 |模型名称|模型简介 | 推理模型大小| 精度(hmean) | 预测耗时(ms) | 下载地址|
 | --- | --- | --- |--- |--- | --- |
 |ser_VI-LayoutXLM_xfund_zh|基于VI-LayoutXLM在xfund中文数据集上训练的SER模型|1.1G| 93.19% | 15.49 | [推理模型](https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/ser_vi_layoutxlm_xfund_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/ser_vi_layoutxlm_xfund_pretrained.tar) |
-|re_VI-LayoutXLM_xfund_zh|基于VI-LayoutXLM在xfund中文数据集上训练的RE模型|1.1G| 83.92% | 15.49 |[推理模型 coming soon]() / [训练模型](https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/re_vi_layoutxlm_xfund_pretrained.tar) |
+|re_VI-LayoutXLM_xfund_zh|基于VI-LayoutXLM在xfund中文数据集上训练的RE模型|1.1G| 83.92% | 15.49 |[推理模型](https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/re_vi_layoutxlm_xfund_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/re_vi_layoutxlm_xfund_pretrained.tar) |
 |ser_LayoutXLM_xfund_zh|基于LayoutXLM在xfund中文数据集上训练的SER模型|1.4G| 90.38% | 19.49 |[推理模型](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutXLM_xfun_zh_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutXLM_xfun_zh.tar) |
-|re_LayoutXLM_xfund_zh|基于LayoutXLM在xfund中文数据集上训练的RE模型|1.4G| 74.83% | 19.49 |[推理模型 coming soon]() / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutXLM_xfun_zh.tar) |
+|re_LayoutXLM_xfund_zh|基于LayoutXLM在xfund中文数据集上训练的RE模型|1.4G| 74.83% | 19.49 |[推理模型](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutXLM_xfun_zh_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutXLM_xfun_zh.tar) |
 |ser_LayoutLMv2_xfund_zh|基于LayoutLMv2在xfund中文数据集上训练的SER模型|778M| 85.44% | 31.46 |[推理模型](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLMv2_xfun_zh_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLMv2_xfun_zh.tar) |
 |re_LayoutLMv2_xfund_zh|基于LayoutLMv2在xfun中文数据集上训练的RE模型|765M| 67.77% | 31.46 |[推理模型 coming soon]() / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutLMv2_xfun_zh.tar) |
 |ser_LayoutLM_xfund_zh|基于LayoutLM在xfund中文数据集上训练的SER模型|430M| 77.31% | - |[推理模型](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLM_xfun_zh_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLM_xfun_zh.tar) |

ppstructure/kie/README.md

@@ -209,17 +209,18 @@ python3 ./tools/infer_kie_token_ser_re.py \
 
 #### 4.2.3 Inference using PaddleInference
 
-At present, only SER model supports inference using PaddleInference.
-
 Firstly, download the inference SER inference model.
 
-
 ```bash
 mkdir inference
 cd inference
 wget https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/ser_vi_layoutxlm_xfund_infer.tar && tar -xf ser_vi_layoutxlm_xfund_infer.tar
+wget https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/re_vi_layoutxlm_xfund_infer.tar && tar -xf re_vi_layoutxlm_xfund_infer.tar
+cd ..
 ```
 
+- SER
+
 Use the following command for inference.
 
 
@@ -236,6 +237,26 @@ python3 kie/predict_kie_token_ser.py \
 
 The visual results and text file will be saved in directory `output`.
 
+- RE
+
+Use the following command for inference.
+
+
+```bash
+cd ppstructure
+python3 kie/predict_kie_token_ser_re.py \
+  --kie_algorithm=LayoutXLM \
+  --re_model_dir=../inference/re_vi_layoutxlm_xfund_infer \
+  --ser_model_dir=../inference/ser_vi_layoutxlm_xfund_infer \
+  --use_visual_backbone=False \
+  --image_dir=./docs/kie/input/zh_val_42.jpg \
+  --ser_dict_path=../train_data/XFUND/class_list_xfun.txt \
+  --vis_font_path=../doc/fonts/simfang.ttf \
+  --ocr_order_method="tb-yx"
+```
+
+The visual results and text file will be saved in directory `output`.
+
 
 ### 4.3 More
 

ppstructure/kie/README_ch.md

@@ -193,17 +193,18 @@ python3 ./tools/infer_kie_token_ser_re.py \
 
 #### 4.2.3 基于PaddleInference的预测
 
-目前仅SER模型支持PaddleInference推理。
-
-首先下载SER的推理模型。
-
+首先下载SER和RE的推理模型。
 
 ```bash
 mkdir inference
 cd inference
 wget https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/ser_vi_layoutxlm_xfund_infer.tar && tar -xf ser_vi_layoutxlm_xfund_infer.tar
+wget https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/re_vi_layoutxlm_xfund_infer.tar && tar -xf re_vi_layoutxlm_xfund_infer.tar
+cd ..
 ```
 
+- SER
+
 执行下面的命令进行预测。
 
 ```bash
@@ -219,6 +220,26 @@ python3 kie/predict_kie_token_ser.py \
 
 可视化结果保存在`output`目录下。
 
+- RE
+
+执行下面的命令进行预测。
+
+```bash
+cd ppstructure
+python3 kie/predict_kie_token_ser_re.py \
+  --kie_algorithm=LayoutXLM \
+  --re_model_dir=../inference/re_vi_layoutxlm_xfund_infer \
+  --ser_model_dir=../inference/ser_vi_layoutxlm_xfund_infer \
+  --use_visual_backbone=False \
+  --image_dir=./docs/kie/input/zh_val_42.jpg \
+  --ser_dict_path=../train_data/XFUND/class_list_xfun.txt \
+  --vis_font_path=../doc/fonts/simfang.ttf \
+  --ocr_order_method="tb-yx"
+```
+
+可视化结果保存在`output`目录下。
+
+
 ### 4.3 更多
 
 关于KIE模型的训练评估与推理，请参考：[关键信息抽取教程](../../doc/doc_ch/kie.md)。

ppstructure/kie/predict_kie_token_ser.py

@@ -102,16 +102,18 @@ class SerPredictor(object):
         ori_im = img.copy()
         data = {'image': img}
         data = transform(data, self.preprocess_op)
-        img = data[0]
-        if img is None:
+        if data[0] is None:
             return None, 0
-        img = np.expand_dims(img, axis=0)
-        img = img.copy()
         starttime = time.time()
 
+        for idx in range(len(data)):
+            if isinstance(data[idx], np.ndarray):
+                data[idx] = np.expand_dims(data[idx], axis=0)
+            else:
+                data[idx] = [data[idx]]
+
         for idx in range(len(self.input_tensor)):
-            expand_input = np.expand_dims(data[idx], axis=0)
-            self.input_tensor[idx].copy_from_cpu(expand_input)
+            self.input_tensor[idx].copy_from_cpu(data[idx])
 
         self.predictor.run()
 
@@ -122,9 +124,9 @@ class SerPredictor(object):
         preds = outputs[0]
 
         post_result = self.postprocess_op(
-            preds, segment_offset_ids=[data[6]], ocr_infos=[data[7]])
+            preds, segment_offset_ids=data[6], ocr_infos=data[7])
         elapse = time.time() - starttime
-        return post_result, elapse
+        return post_result, data, elapse
 
 
 def main(args):
@@ -145,7 +147,7 @@ def main(args):
             if img is None:
                 logger.info("error in loading image:{}".format(image_file))
                 continue
-            ser_res, elapse = ser_predictor(img)
+            ser_res, _, elapse = ser_predictor(img)
             ser_res = ser_res[0]
 
             res_str = '{}\t{}\n'.format(

ppstructure/kie/predict_kie_token_ser_re.py

+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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.
+import os
+import sys
+
+__dir__ = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(__dir__)
+sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '../..')))
+
+os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
+
+import cv2
+import json
+import numpy as np
+import time
+
+import tools.infer.utility as utility
+from tools.infer_kie_token_ser_re import make_input
+from ppocr.postprocess import build_post_process
+from ppocr.utils.logging import get_logger
+from ppocr.utils.visual import draw_re_results
+from ppocr.utils.utility import get_image_file_list, check_and_read
+from ppstructure.utility import parse_args
+from ppstructure.kie.predict_kie_token_ser import SerPredictor
+
+from paddleocr import PaddleOCR
+
+logger = get_logger()
+
+
+class SerRePredictor(object):
+    def __init__(self, args):
+        self.use_visual_backbone = args.use_visual_backbone
+        self.ser_engine = SerPredictor(args)
+
+        postprocess_params = {'name': 'VQAReTokenLayoutLMPostProcess'}
+        self.postprocess_op = build_post_process(postprocess_params)
+        self.predictor, self.input_tensor, self.output_tensors, self.config = \
+            utility.create_predictor(args, 're', logger)
+
+    def __call__(self, img):
+        ori_im = img.copy()
+        starttime = time.time()
+        ser_results, ser_inputs, _ = self.ser_engine(img)
+        re_input, entity_idx_dict_batch = make_input(ser_inputs, ser_results)
+        if self.use_visual_backbone == False:
+            re_input.pop(4)
+        for idx in range(len(self.input_tensor)):
+            self.input_tensor[idx].copy_from_cpu(re_input[idx])
+
+        self.predictor.run()
+        outputs = []
+        for output_tensor in self.output_tensors:
+            output = output_tensor.copy_to_cpu()
+            outputs.append(output)
+        preds = dict(
+            loss=outputs[1],
+            pred_relations=outputs[2],
+            hidden_states=outputs[0], )
+
+        post_result = self.postprocess_op(
+            preds,
+            ser_results=ser_results,
+            entity_idx_dict_batch=entity_idx_dict_batch)
+
+        elapse = time.time() - starttime
+        return post_result, elapse
+
+
+def main(args):
+    image_file_list = get_image_file_list(args.image_dir)
+    ser_predictor = SerRePredictor(args)
+    count = 0
+    total_time = 0
+
+    os.makedirs(args.output, exist_ok=True)
+    with open(
+            os.path.join(args.output, 'infer.txt'), mode='w',
+            encoding='utf-8') as f_w:
+        for image_file in image_file_list:
+            img, flag, _ = check_and_read(image_file)
+            if not flag:
+                img = cv2.imread(image_file)
+                img = img[:, :, ::-1]
+            if img is None:
+                logger.info("error in loading image:{}".format(image_file))
+                continue
+            re_res, elapse = ser_predictor(img)
+            re_res = re_res[0]
+
+            res_str = '{}\t{}\n'.format(
+                image_file,
+                json.dumps(
+                    {
+                        "ocr_info": re_res,
+                    }, ensure_ascii=False))
+            f_w.write(res_str)
+
+            img_res = draw_re_results(
+                image_file, re_res, font_path=args.vis_font_path)
+
+            img_save_path = os.path.join(
+                args.output,
+                os.path.splitext(os.path.basename(image_file))[0] +
+                "_ser_re.jpg")
+
+            cv2.imwrite(img_save_path, img_res)
+            logger.info("save vis result to {}".format(img_save_path))
+            if count > 0:
+                total_time += elapse
+            count += 1
+            logger.info("Predict time of {}: {}".format(image_file, elapse))
+
+
+if __name__ == "__main__":
+    main(parse_args())

ppstructure/predict_system.py

@@ -100,7 +100,8 @@ class StructureSystem(object):
                 '180': cv2.ROTATE_180,
                 '270': cv2.ROTATE_90_CLOCKWISE
             }
-            img = cv2.rotate(img, cv_rotate_code[angle])
+            if angle in cv_rotate_code:
+                img = cv2.rotate(img, cv_rotate_code[angle])
             toc = time.time()
             time_dict['image_orientation'] = toc - tic
         if self.mode == 'structure':

ppstructure/utility.py

@@ -52,6 +52,8 @@ def init_args():
     # params for kie
     parser.add_argument("--kie_algorithm", type=str, default='LayoutXLM')
     parser.add_argument("--ser_model_dir", type=str)
+    parser.add_argument("--re_model_dir", type=str)
+    parser.add_argument("--use_visual_backbone", type=str2bool, default=True)
     parser.add_argument(
         "--ser_dict_path",
         type=str,

tools/export_model.py

@@ -115,16 +115,12 @@ def export_single_model(model,
         max_text_length = arch_config["Head"]["max_text_length"]
         other_shape = [
             paddle.static.InputSpec(
-                shape=[None, 3, 48, 160], dtype="float32"),
-
-            [
-            paddle.static.InputSpec(
-                    shape=[None, ], 
-                    dtype="float32"),
-            paddle.static.InputSpec(
-                    shape=[None, max_text_length], 
-                    dtype="int64")
-            ]
+                shape=[None, 3, 48, 160], dtype="float32"), [
+                    paddle.static.InputSpec(
+                        shape=[None, ], dtype="float32"),
+                    paddle.static.InputSpec(
+                        shape=[None, max_text_length], dtype="int64")
+                ]
         ]
         model = to_static(model, input_spec=other_shape)
     elif arch_config["algorithm"] in ["LayoutLM", "LayoutLMv2", "LayoutXLM"]:
@@ -140,6 +136,13 @@ def export_single_model(model,
             paddle.static.InputSpec(
                 shape=[None, 3, 224, 224], dtype="int64"),  # image
         ]
+        if 'Re' in arch_config['Backbone']['name']:
+            input_spec.extend([
+                paddle.static.InputSpec(
+                    shape=[None, 512, 3], dtype="int64"),  # entities
+                paddle.static.InputSpec(
+                    shape=[None, None, 2], dtype="int64"),  # relations
+            ])
         if model.backbone.use_visual_backbone is False:
             input_spec.pop(4)
         model = to_static(model, input_spec=[input_spec])

tools/infer/utility.py

@@ -162,6 +162,8 @@ def create_predictor(args, mode, logger):
         model_dir = args.table_model_dir
     elif mode == 'ser':
         model_dir = args.ser_model_dir
+    elif mode == 're':
+        model_dir = args.re_model_dir
     elif mode == "sr":
         model_dir = args.sr_model_dir
     elif mode == 'layout':
@@ -227,7 +229,8 @@ def create_predictor(args, mode, logger):
                     use_calib_mode=False)
 
                 # collect shape
-                trt_shape_f = os.path.join(model_dir, f"{mode}_trt_dynamic_shape.txt")
+                trt_shape_f = os.path.join(model_dir,
+                                           f"{mode}_trt_dynamic_shape.txt")
 
                 if not os.path.exists(trt_shape_f):
                     config.collect_shape_range_info(trt_shape_f)
@@ -262,6 +265,8 @@ def create_predictor(args, mode, logger):
         config.disable_glog_info()
         config.delete_pass("conv_transpose_eltwiseadd_bn_fuse_pass")
         config.delete_pass("matmul_transpose_reshape_fuse_pass")
+        if mode == 're':
+            config.delete_pass("simplify_with_basic_ops_pass")
         if mode == 'table':
             config.delete_pass("fc_fuse_pass")  # not supported for table
         config.switch_use_feed_fetch_ops(False)

tools/infer_kie_token_ser_re.py

@@ -63,7 +63,7 @@ class ReArgsParser(ArgsParser):
 
 def make_input(ser_inputs, ser_results):
     entities_labels = {'HEADER': 0, 'QUESTION': 1, 'ANSWER': 2}
-
+    batch_size, max_seq_len = ser_inputs[0].shape[:2]
     entities = ser_inputs[8][0]
     ser_results = ser_results[0]
     assert len(entities) == len(ser_results)
@@ -80,34 +80,44 @@ def make_input(ser_inputs, ser_results):
         start.append(entity['start'])
         end.append(entity['end'])
         label.append(entities_labels[res['pred']])
-    entities = dict(start=start, end=end, label=label)
+
+    entities = np.full([max_seq_len + 1, 3], fill_value=-1)
+    entities[0, 0] = len(start)
+    entities[1:len(start) + 1, 0] = start
+    entities[0, 1] = len(end)
+    entities[1:len(end) + 1, 1] = end
+    entities[0, 2] = len(label)
+    entities[1:len(label) + 1, 2] = label
 
     # relations
     head = []
     tail = []
-    for i in range(len(entities["label"])):
-        for j in range(len(entities["label"])):
-            if entities["label"][i] == 1 and entities["label"][j] == 2:
+    for i in range(len(label)):
+        for j in range(len(label)):
+            if label[i] == 1 and label[j] == 2:
                 head.append(i)
                 tail.append(j)
 
-    relations = dict(head=head, tail=tail)
+    relations = np.full([len(head) + 1, 2], fill_value=-1)
+    relations[0, 0] = len(head)
+    relations[1:len(head) + 1, 0] = head
+    relations[0, 1] = len(tail)
+    relations[1:len(tail) + 1, 1] = tail
+
+    entities = np.expand_dims(entities, axis=0)
+    entities = np.repeat(entities, batch_size, axis=0)
+    relations = np.expand_dims(relations, axis=0)
+    relations = np.repeat(relations, batch_size, axis=0)
+
+    # remove ocr_info segment_offset_id and label in ser input
+    if isinstance(ser_inputs[0], paddle.Tensor):
+        entities = paddle.to_tensor(entities)
+        relations = paddle.to_tensor(relations)
+    ser_inputs = ser_inputs[:5] + [entities, relations]
 
-    batch_size = ser_inputs[0].shape[0]
-    entities_batch = []
-    relations_batch = []
     entity_idx_dict_batch = []
     for b in range(batch_size):
-        entities_batch.append(entities)
-        relations_batch.append(relations)
         entity_idx_dict_batch.append(entity_idx_dict)
-
-    ser_inputs[8] = entities_batch
-    ser_inputs.append(relations_batch)
-    # remove ocr_info segment_offset_id and label in ser input
-    ser_inputs.pop(7)
-    ser_inputs.pop(6)
-    ser_inputs.pop(5)
     return ser_inputs, entity_idx_dict_batch
 
 
@@ -136,6 +146,8 @@ class SerRePredictor(object):
     def __call__(self, data):
         ser_results, ser_inputs = self.ser_engine(data)
         re_input, entity_idx_dict_batch = make_input(ser_inputs, ser_results)
+        if self.model.backbone.use_visual_backbone is False:
+            re_input.pop(4)
         preds = self.model(re_input)
         post_result = self.post_process_class(
             preds,