add dygraph2static support of layoutlm series SER model

configs/vqa/re/layoutlmv2.yml

@@ -11,7 +11,7 @@ Global:
   save_inference_dir:
   use_visualdl: False
   seed: 2048
-  infer_img: doc/vqa/input/zh_val_21.jpg
+  infer_img: ppstructure/docs/vqa/input/zh_val_21.jpg
   save_res_path: ./output/re/
 
 Architecture:
@@ -21,7 +21,7 @@ Architecture:
   Backbone:
     name: LayoutLMv2ForRe
     pretrained: True
-    checkpoints: 
+    checkpoints:
 
 Loss:
   name: LossFromOutput
@@ -52,7 +52,7 @@ Train:
     name: SimpleDataSet
     data_dir: train_data/XFUND/zh_train/image
     label_file_list: 
-      - train_data/XFUND/zh_train/xfun_normalize_train.json
+      - train_data/XFUND/zh_train/train.json
     ratio_list: [ 1.0 ]
     transforms:
       - DecodeImage: # load image
@@ -61,7 +61,7 @@ Train:
       - VQATokenLabelEncode: # Class handling label
           contains_re: True
           algorithm: *algorithm
-          class_path: &class_path ppstructure/vqa/labels/labels_ser.txt
+          class_path: &class_path train_data/XFUND/class_list_xfun.txt
       - VQATokenPad:
           max_seq_len: &max_seq_len 512
           return_attention_mask: True
@@ -77,7 +77,7 @@ Train:
           order: 'hwc'
       - ToCHWImage:
       - KeepKeys:
-          keep_keys: [ 'input_ids', 'bbox', 'image', 'attention_mask', 'token_type_ids','entities', 'relations'] # dataloader will return list in this order
+          keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids','image', 'entities', 'relations'] # dataloader will return list in this order
   loader:
     shuffle: True
     drop_last: False
@@ -90,7 +90,7 @@ Eval:
     name: SimpleDataSet
     data_dir: train_data/XFUND/zh_val/image
     label_file_list:
-      - train_data/XFUND/zh_val/xfun_normalize_val.json
+      - train_data/XFUND/zh_val/val.json
     transforms:
       - DecodeImage: # load image
           img_mode: RGB
@@ -114,7 +114,7 @@ Eval:
           order: 'hwc'
       - ToCHWImage:
       - KeepKeys:
-          keep_keys: [ 'input_ids', 'bbox', 'image', 'attention_mask', 'token_type_ids','entities', 'relations'] # dataloader will return list in this order
+          keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image','entities', 'relations'] # dataloader will return list in this order
   loader:
     shuffle: False
     drop_last: False

configs/vqa/re/layoutxlm.yml

@@ -11,7 +11,7 @@ Global:
   save_inference_dir:
   use_visualdl: False
   seed: 2022
-  infer_img: doc/vqa/input/zh_val_21.jpg
+  infer_img: ppstructure/docs/vqa/input/zh_val_21.jpg
   save_res_path: ./output/re/
 
 Architecture:
@@ -52,7 +52,7 @@ Train:
     name: SimpleDataSet
     data_dir: train_data/XFUND/zh_train/image
     label_file_list: 
-      - train_data/XFUND/zh_train/xfun_normalize_train.json
+      - train_data/XFUND/zh_train/train.json
     ratio_list: [ 1.0 ]
     transforms:
       - DecodeImage: # load image
@@ -61,7 +61,7 @@ Train:
       - VQATokenLabelEncode: # Class handling label
           contains_re: True
           algorithm: *algorithm
-          class_path: &class_path ppstructure/vqa/labels/labels_ser.txt
+          class_path: &class_path train_data/XFUND/class_list_xfun.txt
       - VQATokenPad:
           max_seq_len: &max_seq_len 512
           return_attention_mask: True
@@ -77,7 +77,7 @@ Train:
           order: 'hwc'
       - ToCHWImage:
       - KeepKeys:
-          keep_keys: [ 'input_ids', 'bbox', 'image', 'attention_mask', 'token_type_ids','entities', 'relations'] # dataloader will return list in this order
+          keep_keys: [ 'input_ids', 'bbox','attention_mask', 'token_type_ids', 'image', 'entities', 'relations'] # dataloader will return list in this order
   loader:
     shuffle: True
     drop_last: False
@@ -90,7 +90,7 @@ Eval:
     name: SimpleDataSet
     data_dir: train_data/XFUND/zh_val/image
     label_file_list:
-      - train_data/XFUND/zh_val/xfun_normalize_val.json
+      - train_data/XFUND/zh_val/val.json
     transforms:
       - DecodeImage: # load image
           img_mode: RGB
@@ -114,7 +114,7 @@ Eval:
           order: 'hwc'
       - ToCHWImage:
       - KeepKeys:
-          keep_keys: [ 'input_ids', 'bbox', 'image', 'attention_mask', 'token_type_ids','entities', 'relations'] # dataloader will return list in this order
+          keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'entities', 'relations'] # dataloader will return list in this order
   loader:
     shuffle: False
     drop_last: False

configs/vqa/ser/layoutlm.yml

@@ -11,7 +11,7 @@ Global:
   save_inference_dir:
   use_visualdl: False
   seed: 2022
-  infer_img: doc/vqa/input/zh_val_0.jpg
+  infer_img: ppstructure/docs/vqa/input/zh_val_42.jpg
   save_res_path: ./output/ser/
 
 Architecture:
@@ -77,7 +77,7 @@ Train:
           order: 'hwc'
       - ToCHWImage:
       - KeepKeys:
-          keep_keys: [ 'input_ids','labels', 'bbox', 'image', 'attention_mask', 'token_type_ids'] # dataloader will return list in this order
+          keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] # dataloader will return list in this order
   loader:
     shuffle: True
     drop_last: False
@@ -112,7 +112,7 @@ Eval:
           order: 'hwc'
       - ToCHWImage:
       - KeepKeys:
-          keep_keys: [ 'input_ids', 'labels', 'bbox', 'image', 'attention_mask', 'token_type_ids'] # dataloader will return list in this order
+          keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] # dataloader will return list in this order
   loader:
     shuffle: False
     drop_last: False

configs/vqa/ser/layoutlmv2.yml

@@ -11,7 +11,7 @@ Global:
   save_inference_dir:
   use_visualdl: False
   seed: 2022
-  infer_img: doc/vqa/input/zh_val_0.jpg
+  infer_img: ppstructure/docs/vqa/input/zh_val_42.jpg
   save_res_path: ./output/ser/
 
 Architecture:
@@ -78,7 +78,7 @@ Train:
           order: 'hwc'
       - ToCHWImage:
       - KeepKeys:
-          keep_keys: [ 'input_ids','labels', 'bbox', 'image', 'attention_mask', 'token_type_ids'] # dataloader will return list in this order
+          keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] # dataloader will return list in this order
   loader:
     shuffle: True
     drop_last: False
@@ -113,7 +113,7 @@ Eval:
           order: 'hwc'
       - ToCHWImage:
       - KeepKeys:
-          keep_keys: [ 'input_ids', 'labels', 'bbox', 'image', 'attention_mask', 'token_type_ids'] # dataloader will return list in this order
+          keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] # dataloader will return list in this order
   loader:
     shuffle: False
     drop_last: False

configs/vqa/ser/layoutxlm.yml

@@ -43,7 +43,7 @@ Optimizer:
     
 PostProcess:
   name: VQASerTokenLayoutLMPostProcess
-  class_path: &class_path ppstructure/vqa/labels/labels_ser.txt
+  class_path: &class_path train_data/XFUND/class_list_xfun.txt
 
 Metric:
   name: VQASerTokenMetric
@@ -78,7 +78,7 @@ Train:
           order: 'hwc'
       - ToCHWImage:
       - KeepKeys:
-          keep_keys: [ 'input_ids','labels', 'bbox', 'image', 'attention_mask', 'token_type_ids'] # dataloader will return list in this order
+          keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] # dataloader will return list in this order
   loader:
     shuffle: True
     drop_last: False
@@ -113,7 +113,7 @@ Eval:
           order: 'hwc'
       - ToCHWImage:
       - KeepKeys:
-          keep_keys: [ 'input_ids', 'labels', 'bbox', 'image', 'attention_mask', 'token_type_ids'] # dataloader will return list in this order
+          keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] # dataloader will return list in this order
   loader:
     shuffle: False
     drop_last: False

ppocr/data/imaug/label_ops.py

@@ -912,7 +912,7 @@ class VQATokenLabelEncode(object):
                 label = info['label']
                 gt_label = self._parse_label(label, encode_res)
 
-# construct entities for re
+            # construct entities for re
             if train_re:
                 if gt_label[0] != self.label2id_map["O"]:
                     entity_id_to_index_map[info["id"]] = len(entities)

ppocr/losses/vqa_token_layoutlm_loss.py

@@ -27,8 +27,8 @@ class VQASerTokenLayoutLMLoss(nn.Layer):
         self.ignore_index = self.loss_class.ignore_index
 
     def forward(self, predicts, batch):
-        labels = batch[1]
-        attention_mask = batch[4]
+        labels = batch[5]
+        attention_mask = batch[2]
         if attention_mask is not None:
             active_loss = attention_mask.reshape([-1, ]) == 1
             active_outputs = predicts.reshape(

ppocr/modeling/backbones/vqa_layoutlm.py

@@ -74,9 +74,9 @@ class LayoutLMForSer(NLPBaseModel):
     def forward(self, x):
         x = self.model(
             input_ids=x[0],
-            bbox=x[2],
-            attention_mask=x[4],
-            token_type_ids=x[5],
+            bbox=x[1],
+            attention_mask=x[2],
+            token_type_ids=x[3],
             position_ids=None,
             output_hidden_states=False)
         return x
@@ -96,13 +96,15 @@ class LayoutLMv2ForSer(NLPBaseModel):
     def forward(self, x):
         x = self.model(
             input_ids=x[0],
-            bbox=x[2],
-            image=x[3],
-            attention_mask=x[4],
-            token_type_ids=x[5],
+            bbox=x[1],
+            attention_mask=x[2],
+            token_type_ids=x[3],
+            image=x[4],
             position_ids=None,
             head_mask=None,
             labels=None)
+        if not self.training:
+            return x
         return x[0]
 
 
@@ -119,14 +121,16 @@ class LayoutXLMForSer(NLPBaseModel):
 
     def forward(self, x):
         x = self.model(
-            input_ids=x[0],
-            bbox=x[2],
-            image=x[3],
-            attention_mask=x[4],
-            token_type_ids=x[5],
-            position_ids=None,
-            head_mask=None,
-            labels=None)
+                        input_ids=x[0],
+                        bbox=x[1],
+                        attention_mask=x[2],
+                        token_type_ids=x[3],
+                        image=x[4],
+                        position_ids=None,
+                        head_mask=None,
+                        labels=None)
+        if not self.training:
+            return x
         return x[0]
 
 
@@ -140,12 +144,12 @@ class LayoutLMv2ForRe(NLPBaseModel):
         x = self.model(
             input_ids=x[0],
             bbox=x[1],
-            labels=None,
-            image=x[2],
-            attention_mask=x[3],
-            token_type_ids=x[4],
+            attention_mask=x[2],
+            token_type_ids=x[3],
+            image=x[4],
             position_ids=None,
             head_mask=None,
+            labels=None,
             entities=x[5],
             relations=x[6])
         return x
@@ -161,12 +165,12 @@ class LayoutXLMForRe(NLPBaseModel):
         x = self.model(
             input_ids=x[0],
             bbox=x[1],
-            labels=None,
-            image=x[2],
-            attention_mask=x[3],
-            token_type_ids=x[4],
+            attention_mask=x[2],
+            token_type_ids=x[3],
+            image=x[4],
             position_ids=None,
             head_mask=None,
+            labels=None,
             entities=x[5],
             relations=x[6])
         return x

ppocr/postprocess/vqa_token_ser_layoutlm_postprocess.py

@@ -41,11 +41,13 @@ class VQASerTokenLayoutLMPostProcess(object):
                 self.id2label_map_for_show[val] = key
 
     def __call__(self, preds, batch=None, *args, **kwargs):
+        if isinstance(preds, tuple):
+            preds = preds[0]
         if isinstance(preds, paddle.Tensor):
             preds = preds.numpy()
 
         if batch is not None:
-            return self._metric(preds, batch[1])
+            return self._metric(preds, batch[5])
         else:
             return self._infer(preds, **kwargs)
 
@@ -63,11 +65,10 @@ class VQASerTokenLayoutLMPostProcess(object):
                                                                           j]])
         return decode_out_list, label_decode_out_list
 
-    def _infer(self, preds, attention_masks, segment_offset_ids, ocr_infos):
+    def _infer(self, preds, segment_offset_ids, ocr_infos):
         results = []
 
-        for pred, attention_mask, segment_offset_id, ocr_info in zip(
-                preds, attention_masks, segment_offset_ids, ocr_infos):
+        for pred, segment_offset_id, ocr_info in zip(preds, segment_offset_ids, ocr_infos):
             pred = np.argmax(pred, axis=1)
             pred = [self.id2label_map[idx] for idx in pred]
 

ppstructure/docs/models_list.md

 # PP-Structure 系列模型列表
 
-- [1. 版面分析模型](#1)
-- [2. OCR和表格识别模型](#2)
-    - [2.1 OCR](#21)
-    - [2.2 表格识别模型](#22)
-- [3. VQA模型](#3)
-- [4. KIE模型](#4)
+- [PP-Structure 系列模型列表](#pp-structure-系列模型列表)
+  - [1. 版面分析模型](#1-版面分析模型)
+  - [2. OCR和表格识别模型](#2-ocr和表格识别模型)
+    - [2.1 OCR](#21-ocr)
+    - [2.2 表格识别模型](#22-表格识别模型)
+  - [3. VQA模型](#3-vqa模型)
+  - [4. KIE模型](#4-kie模型)
 
 
 <a name="1"></a>
@@ -42,11 +43,11 @@
 
 |模型名称|模型简介|推理模型大小|下载地址|
 | --- | --- | --- | --- |
-|ser_LayoutXLM_xfun_zh|基于LayoutXLM在xfun中文数据集上训练的SER模型|1.4G|[推理模型 coming soon]() / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutXLM_xfun_zh.tar) |
-|re_LayoutXLM_xfun_zh|基于LayoutXLM在xfun中文数据集上训练的RE模型|1.4G|[推理模型 coming soon]() / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutXLM_xfun_zh.tar) |
-|ser_LayoutLMv2_xfun_zh|基于LayoutLMv2在xfun中文数据集上训练的SER模型|778M|[推理模型 coming soon]() / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLMv2_xfun_zh.tar) |
+|ser_LayoutXLM_xfun_zh|基于LayoutXLM在xfun中文数据集上训练的SER模型|1.4G|[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_xfun_zh|基于LayoutXLM在xfun中文数据集上训练的RE模型|1.4G|[推理模型 coming soon]() / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutXLM_xfun_zh.tar) |
+|ser_LayoutLMv2_xfun_zh|基于LayoutLMv2在xfun中文数据集上训练的SER模型|778M|[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_xfun_zh|基于LayoutLMv2在xfun中文数据集上训练的RE模型|765M|[推理模型 coming soon]() / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutLMv2_xfun_zh.tar) |
-|ser_LayoutLM_xfun_zh|基于LayoutLM在xfun中文数据集上训练的SER模型|430M|[推理模型 coming soon]() / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLM_xfun_zh.tar) |
+|ser_LayoutLM_xfun_zh|基于LayoutLM在xfun中文数据集上训练的SER模型|430M|[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) |
 
 <a name="4"></a>
 ## 4. KIE模型

ppstructure/docs/models_list_en.md

 # PP-Structure Model list
 
-- [1. Layout Analysis](#1)
-- [2. OCR and Table Recognition](#2)
-    - [2.1 OCR](#21)
-    - [2.2 Table Recognition](#22)
-- [3. VQA](#3)
-- [4. KIE](#4)
+- [PP-Structure Model list](#pp-structure-model-list)
+  - [1. Layout Analysis](#1-layout-analysis)
+  - [2. OCR and Table Recognition](#2-ocr-and-table-recognition)
+    - [2.1 OCR](#21-ocr)
+    - [2.2 Table Recognition](#22-table-recognition)
+  - [3. VQA](#3-vqa)
+  - [4. KIE](#4-kie)
 
 
 <a name="1"></a>
@@ -42,11 +43,11 @@ If you need to use other OCR models, you can download the model in [PP-OCR model
 
 |model| description                                                    |inference model size|download|
 | --- |----------------------------------------------------------------| --- | --- |
-|ser_LayoutXLM_xfun_zh| SER model trained on xfun Chinese dataset based on LayoutXLM   |1.4G|[inference model coming soon]() / [trained model](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutXLM_xfun_zh.tar) |
-|re_LayoutXLM_xfun_zh| Re model trained on xfun Chinese dataset based on LayoutXLM    |1.4G|[inference model coming soon]() / [trained model](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutXLM_xfun_zh.tar) |
-|ser_LayoutLMv2_xfun_zh| SER model trained on xfun Chinese dataset based on LayoutXLMv2 |778M|[inference model coming soon]() / [trained model](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLMv2_xfun_zh.tar) |
+|ser_LayoutXLM_xfun_zh| SER model trained on xfun Chinese dataset based on LayoutXLM   |1.4G|[inference model](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutXLM_xfun_zh_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutXLM_xfun_zh.tar) |
+|re_LayoutXLM_xfun_zh| Re model trained on xfun Chinese dataset based on LayoutXLM    |1.4G|[inference model coming soon]() / [trained model](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutXLM_xfun_zh.tar) |
+|ser_LayoutLMv2_xfun_zh| SER model trained on xfun Chinese dataset based on LayoutXLMv2 |778M|[inference model](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLMv2_xfun_zh_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLMv2_xfun_zh.tar) |
 |re_LayoutLMv2_xfun_zh| Re model trained on xfun Chinese dataset based on LayoutXLMv2  |765M|[inference model coming soon]() / [trained model](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutLMv2_xfun_zh.tar) |
-|ser_LayoutLM_xfun_zh| SER model trained on xfun Chinese dataset based on LayoutLM    |430M|[inference model coming soon]() / [trained model](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLM_xfun_zh.tar) |
+|ser_LayoutLM_xfun_zh| SER model trained on xfun Chinese dataset based on LayoutLM    |430M|[inference model](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLM_xfun_zh_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLM_xfun_zh.tar) |
 
 <a name="4"></a>
 ## 4. KIE

ppstructure/utility.py

@@ -40,6 +40,14 @@ def init_args():
         type=ast.literal_eval,
         default=None,
         help='label map according to ppstructure/layout/README_ch.md')
+
+    # params for vqa
+    parser.add_argument("--vqa_algorithm", type=str, default='LayoutXLM')
+    parser.add_argument("--ser_model_dir", type=str)
+    parser.add_argument(
+        "--ser_dict_path",
+        type=str,
+        default="../train_data/XFUND/class_list_xfun.txt")
     # params for inference
     parser.add_argument(
         "--mode",

ppstructure/vqa/README.md

 English | [简体中文](README_ch.md)
 
-- [Document Visual Question Answering (Doc-VQA)](#Document-Visual-Question-Answering)
-  - [1. Introduction](#1-Introduction)
+- [Document Visual Question Answering](#document-visual-question-answering)
+  - [1 Introduction](#1-introduction)
   - [2. Performance](#2-performance)
-  - [3. Effect demo](#3-Effect-demo)
+  - [3. Effect demo](#3-effect-demo)
     - [3.1 SER](#31-ser)
     - [3.2 RE](#32-re)
-  - [4. Install](#4-Install)
-    - [4.1 Installation dependencies](#41-Install-dependencies)
-    - [4.2 Install PaddleOCR](#42-Install-PaddleOCR)
-  - [5. Usage](#5-Usage)
-    - [5.1 Data and Model Preparation](#51-Data-and-Model-Preparation)
-    - [5.2 SER](#52-ser)
+  - [4. Install](#4-install)
+    - [4.1 Install dependencies](#41-install-dependencies)
     - [5.3 RE](#53-re)
-  - [6. Reference](#6-Reference-Links)
+  - [6. Reference Links](#6-reference-links)
+  - [License](#license)
 
 # Document Visual Question Answering
 
@@ -187,17 +184,17 @@ CUDA_VISIBLE_DEVICES=0 python3 tools/eval.py -c configs/vqa/ser/layoutxlm.yml -o
 ````
 Finally, `precision`, `recall`, `hmean` and other indicators will be printed
 
-* Use `OCR engine + SER` tandem prediction
+* `OCR + SER` tandem prediction based on training engine
 
-Use the following command to complete the series prediction of `OCR engine + SER`, taking the pretrained SER model as an example:
+Use the following command to complete the series prediction of `OCR engine + SER`, taking the SER model based on LayoutXLM as an example::
 
 ```shell
-CUDA_VISIBLE_DEVICES=0 python3 tools/infer_vqa_token_ser.py -c configs/vqa/ser/layoutxlm.yml -o Architecture.Backbone.checkpoints=pretrain/ser_LayoutXLM_xfun_zh/Global.infer_img=doc/vqa/input/zh_val_42.jpg
+python3.7 tools/export_model.py -c configs/vqa/ser/layoutxlm.yml -o Architecture.Backbone.checkpoints=pretrain/ser_LayoutXLM_xfun_zh/ Global.save_inference_dir=output/ser/infer
 ````
 
 Finally, the prediction result visualization image and the prediction result text file will be saved in the directory configured by the `config.Global.save_res_path` field. The prediction result text file is named `infer_results.txt`.
 
-* End-to-end evaluation of `OCR engine + SER` prediction system
+* End-to-end evaluation of `OCR + SER` prediction system
 
 First use the `tools/infer_vqa_token_ser.py` script to complete the prediction of the dataset, then use the following command to evaluate.
 
@@ -205,6 +202,24 @@ First use the `tools/infer_vqa_token_ser.py` script to complete the prediction o
 export CUDA_VISIBLE_DEVICES=0
 python3 tools/eval_with_label_end2end.py --gt_json_path XFUND/zh_val/xfun_normalize_val.json --pred_json_path output_res/infer_results.txt
 ````
+* export model
+
+Use the following command to complete the model export of the SER model, taking the SER model based on LayoutXLM as an example:
+
+```shell
+python3.7 tools/export_model.py -c configs/vqa/ser/layoutxlm.yml -o Architecture.Backbone.checkpoints=pretrain/ser_LayoutXLM_xfun_zh/ Global.save_inference_dir=output/ser/infer
+```
+The converted model will be stored in the directory specified by the `Global.save_inference_dir` field.
+
+* `OCR + SER` tandem prediction based on prediction engine
+  
+Use the following command to complete the tandem prediction of `OCR + SER` based on the prediction engine, taking the SER model based on LayoutXLM as an example:
+
+```shell
+cd ppstructure
+CUDA_VISIBLE_DEVICES=0 python3.7 vqa/predict_vqa_token_ser.py --vqa_algorithm=LayoutXLM --ser_model_dir=../output/ser/infer --ser_dict_path=../train_data/XFUND/class_list_xfun.txt --image_dir=docs/vqa/input/zh_val_42.jpg --output=output
+```
+After the prediction is successful, the visualization images and results will be saved in the directory specified by the `output` field
 
 <a name="53"></a>
 ### 5.3 RE
@@ -247,11 +262,19 @@ Finally, `precision`, `recall`, `hmean` and other indicators will be printed
 Use the following command to complete the series prediction of `OCR engine + SER + RE`, taking the pretrained SER and RE models as an example:
 ```shell
 export CUDA_VISIBLE_DEVICES=0
-python3 tools/infer_vqa_token_ser_re.py -c configs/vqa/re/layoutxlm.yml -o Architecture.Backbone.checkpoints=pretrain/re_LayoutXLM_xfun_zh/Global.infer_img=doc/vqa/input/zh_val_21.jpg -c_ser configs/vqa/ser/layoutxlm. yml -o_ser Architecture.Backbone.checkpoints=pretrain/ser_LayoutXLM_xfun_zh/
+python3 tools/infer_vqa_token_ser_re.py -c configs/vqa/re/layoutxlm.yml -o Architecture.Backbone.checkpoints=pretrain/re_LayoutXLM_xfun_zh/Global.infer_img=ppstructure/docs/vqa/input/zh_val_21.jpg -c_ser configs/vqa/ser/layoutxlm. yml -o_ser Architecture.Backbone.checkpoints=pretrain/ser_LayoutXLM_xfun_zh/
 ````
 
 Finally, the prediction result visualization image and the prediction result text file will be saved in the directory configured by the `config.Global.save_res_path` field. The prediction result text file is named `infer_results.txt`.
 
+* export model
+  
+cooming soon
+
+* `OCR + SER + RE` tandem prediction based on prediction engine
+
+cooming soon
+
 ## 6. Reference Links
 
 - LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding, https://arxiv.org/pdf/2104.08836.pdf

ppstructure/vqa/README_ch.md

@@ -14,6 +14,7 @@
     - [5.2 SER](#52-ser)
     - [5.3 RE](#53-re)
   - [6. 参考链接](#6-参考链接)
+  - [License](#license)
 
 # 文档视觉问答（DOC-VQA）
 
@@ -183,16 +184,16 @@ CUDA_VISIBLE_DEVICES=0 python3 tools/eval.py -c configs/vqa/ser/layoutxlm.yml -o
 ```
 最终会打印出`precision`, `recall`, `hmean`等指标
 
-* 使用`OCR引擎 + SER`串联预测
+* 基于训练引擎的`OCR + SER`串联预测
 
-使用如下命令即可完成`OCR引擎 + SER`的串联预测, 以SER预训练模型为例:
+使用如下命令即可完成基于训练引擎的`OCR + SER`的串联预测, 以基于LayoutXLM的SER模型为例:
 ```shell
 CUDA_VISIBLE_DEVICES=0 python3 tools/infer_vqa_token_ser.py -c configs/vqa/ser/layoutxlm.yml  -o Architecture.Backbone.checkpoints=pretrain/ser_LayoutXLM_xfun_zh/ Global.infer_img=doc/vqa/input/zh_val_42.jpg
 ```
 
 最终会在`config.Global.save_res_path`字段所配置的目录下保存预测结果可视化图像以及预测结果文本文件，预测结果文本文件名为`infer_results.txt`。
 
-* 对`OCR引擎 + SER`预测系统进行端到端评估
+* 对`OCR + SER`预测系统进行端到端评估
 
 首先使用 `tools/infer_vqa_token_ser.py` 脚本完成数据集的预测，然后使用下面的命令进行评估。
 
@@ -200,6 +201,24 @@ CUDA_VISIBLE_DEVICES=0 python3 tools/infer_vqa_token_ser.py -c configs/vqa/ser/l
 export CUDA_VISIBLE_DEVICES=0
 python3 tools/eval_with_label_end2end.py --gt_json_path XFUND/zh_val/xfun_normalize_val.json  --pred_json_path output_res/infer_results.txt
 ```
+* 模型导出
+
+使用如下命令即可完成SER模型的模型导出, 以基于LayoutXLM的SER模型为例:
+
+```shell
+python3.7 tools/export_model.py -c configs/vqa/ser/layoutxlm.yml -o Architecture.Backbone.checkpoints=pretrain/ser_LayoutXLM_xfun_zh/ Global.save_inference_dir=output/ser/infer
+```
+转换后的模型会存放在`Global.save_inference_dir`字段指定的目录下。
+
+* 基于预测引擎的`OCR + SER`串联预测
+  
+使用如下命令即可完成基于预测引擎的`OCR + SER`的串联预测, 以基于LayoutXLM的SER模型为例:
+
+```shell
+cd ppstructure
+CUDA_VISIBLE_DEVICES=0 python3.7 vqa/predict_vqa_token_ser.py --vqa_algorithm=LayoutXLM --ser_model_dir=../output/ser/infer --ser_dict_path=../train_data/XFUND/class_list_xfun.txt --image_dir=docs/vqa/input/zh_val_42.jpg --output=output
+```
+预测成功后，可视化图片和结果会保存在`output`字段指定的目录下
 
 ### 5.3 RE
 
@@ -236,16 +255,24 @@ CUDA_VISIBLE_DEVICES=0 python3 tools/eval.py -c configs/vqa/re/layoutxlm.yml -o
 ```
 最终会打印出`precision`, `recall`, `hmean`等指标
 
-* 使用`OCR引擎 + SER + RE`串联预测
+* 基于训练引擎的`OCR + SER + RE`串联预测
 
-使用如下命令即可完成`OCR引擎 + SER + RE`的串联预测, 以预训练SER和RE模型为例：
+使用如下命令即可完成基于训练引擎的`OCR + SER + RE`串联预测, 以基于LayoutXLMSER和RE模型为例：
 ```shell
 export CUDA_VISIBLE_DEVICES=0
-python3 tools/infer_vqa_token_ser_re.py -c configs/vqa/re/layoutxlm.yml -o Architecture.Backbone.checkpoints=pretrain/re_LayoutXLM_xfun_zh/ Global.infer_img=doc/vqa/input/zh_val_21.jpg -c_ser configs/vqa/ser/layoutxlm.yml -o_ser Architecture.Backbone.checkpoints=pretrain/ser_LayoutXLM_xfun_zh/
+python3 tools/infer_vqa_token_ser_re.py -c configs/vqa/re/layoutxlm.yml -o Architecture.Backbone.checkpoints=pretrain/re_LayoutXLM_xfun_zh/ Global.infer_img=ppstructure/docs/vqa/input/zh_val_21.jpg -c_ser configs/vqa/ser/layoutxlm.yml -o_ser Architecture.Backbone.checkpoints=pretrain/ser_LayoutXLM_xfun_zh/
 ```
 
 最终会在`config.Global.save_res_path`字段所配置的目录下保存预测结果可视化图像以及预测结果文本文件，预测结果文本文件名为`infer_results.txt`。
 
+* 模型导出
+  
+cooming soon
+
+* 基于预测引擎的`OCR + SER + RE`串联预测
+
+cooming soon
+
 ## 6. 参考链接
 
 - LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding, https://arxiv.org/pdf/2104.08836.pdf

ppstructure/vqa/predict_vqa_token_ser.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.append(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 ppocr.data import create_operators, transform
+from ppocr.postprocess import build_post_process
+from ppocr.utils.logging import get_logger
+from ppocr.utils.visual import draw_ser_results
+from ppocr.utils.utility import get_image_file_list, check_and_read_gif
+from ppstructure.utility import parse_args
+
+from paddleocr import PaddleOCR
+
+logger = get_logger()
+
+
+class SerPredictor(object):
+    def __init__(self, args):
+        self.ocr_engine = PaddleOCR(use_angle_cls=False, show_log=False)
+
+        pre_process_list = [{
+            'VQATokenLabelEncode': {
+                'algorithm': args.vqa_algorithm,
+                'class_path': args.ser_dict_path,
+                'contains_re': False,
+                'ocr_engine': self.ocr_engine
+            }
+        }, {
+            'VQATokenPad':{
+                'max_seq_len':512,
+                'return_attention_mask': True
+            }
+        }, {
+            'VQASerTokenChunk':{
+                'max_seq_len':512,
+                'return_attention_mask': True
+            }
+        }, {
+            'Resize':{
+                'size' : [224, 224]
+            }
+        }, {
+            'NormalizeImage': {
+                'std': [58.395, 57.12, 57.375],
+                'mean': [123.675, 116.28, 103.53],
+                'scale': '1',
+                'order': 'hwc'
+            }
+        }, {
+            'ToCHWImage': None
+        }, {
+            'KeepKeys': {
+                'keep_keys': [
+                    'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels',
+                    'segment_offset_id', 'ocr_info',
+                    'entities'
+                ]
+            }
+        }]
+        postprocess_params = {
+            'name': 'VQASerTokenLayoutLMPostProcess',
+            "class_path": args.ser_dict_path,
+        }
+
+        self.preprocess_op = create_operators(pre_process_list, {'infer_mode':True})
+        self.postprocess_op = build_post_process(postprocess_params)
+        self.predictor, self.input_tensor, self.output_tensors, self.config = \
+            utility.create_predictor(args, 'ser', logger)
+
+    def __call__(self, img):
+        ori_im = img.copy()
+        data = {'image': img}
+        data = transform(data, self.preprocess_op)
+        img = data[0]
+        if img is None:
+            return None, 0
+        img = np.expand_dims(img, axis=0)
+        img = img.copy()
+        starttime = time.time()
+
+        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.predictor.run()
+
+        outputs = []
+        for output_tensor in self.output_tensors:
+            output = output_tensor.copy_to_cpu()
+            outputs.append(output)
+        preds = outputs[0]
+        
+        post_result = self.postprocess_op(
+            preds,
+            segment_offset_ids=[data[6]],
+            ocr_infos=[data[7]])
+        elapse = time.time() - starttime
+        return post_result, elapse
+
+
+def main(args):
+    image_file_list = get_image_file_list(args.image_dir)
+    ser_predictor = SerPredictor(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_gif(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
+            ser_res, elapse = ser_predictor(img)
+            ser_res = ser_res[0]
+
+            res_str = '{}\t{}\n'.format(image_file,json.dumps({"ocr_info": ser_res,}, ensure_ascii=False))
+            f_w.write(res_str)
+
+            img_res = draw_ser_results(image_file, ser_res,  font_path="../doc/fonts/simfang.ttf",)
+
+            img_save_path = os.path.join(args.output,
+                                         os.path.basename(image_file))
+            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/vqa/requirements.txt

 sentencepiece
 yacs
 seqeval
-paddlenlp>=2.2.1
\ No newline at end of file
+paddlenlp>=2.2.1
+pypandoc
+attrdict
+python_docx
\ No newline at end of file

tools/export_model.py

@@ -97,6 +97,22 @@ def export_single_model(model,
                 shape=[None, 1, 32, 100], dtype="float32"),
         ]
         model = to_static(model, input_spec=other_shape)
+    elif arch_config["algorithm"] in ["LayoutLM", "LayoutLMv2", "LayoutXLM"]:
+        input_spec=[
+            paddle.static.InputSpec(
+                shape=[None, 512], dtype="int64"),  # input_ids
+            paddle.static.InputSpec(
+                shape=[None, 512, 4], dtype="int64"),  # bbox
+            paddle.static.InputSpec(
+                shape=[None, 512], dtype="int64"),  # attention_mask
+            paddle.static.InputSpec(
+                shape=[None, 512], dtype="int64"),  # token_type_ids
+            paddle.static.InputSpec(
+                shape=[None, 3, 224, 224], dtype="int64"),  # image
+        ]
+        if arch_config["algorithm"] == "LayoutLM":
+            input_spec.pop(4)
+        model = to_static(model, input_spec=[input_spec])
     else:
         infer_shape = [3, -1, -1]
         if arch_config["model_type"] == "rec":
@@ -172,7 +188,7 @@ def main():
             config["Architecture"]["Head"]["out_channels"] = char_num
 
     model = build_model(config["Architecture"])
-    load_model(config, model)
+    load_model(config, model, model_type=config['Architecture']["model_type"])
     model.eval()
 
     save_path = config["Global"]["save_inference_dir"]

tools/infer/utility.py

@@ -153,6 +153,8 @@ def create_predictor(args, mode, logger):
         model_dir = args.rec_model_dir
     elif mode == 'table':
         model_dir = args.table_model_dir
+    elif mode == 'ser':
+        model_dir = args.ser_model_dir
     else:
         model_dir = args.e2e_model_dir
 
@@ -316,8 +318,13 @@ def create_predictor(args, mode, logger):
         # create predictor
         predictor = inference.create_predictor(config)
         input_names = predictor.get_input_names()
-        for name in input_names:
-            input_tensor = predictor.get_input_handle(name)
+        if mode in ['ser','re']:
+            input_tensor = []
+            for name in input_names:
+                input_tensor.append(predictor.get_input_handle(name))
+        else:
+            for name in input_names:
+                input_tensor = predictor.get_input_handle(name)
         output_tensors = get_output_tensors(args, mode, predictor)
         return predictor, input_tensor, output_tensors, config
 

tools/infer_vqa_token_ser.py

@@ -44,6 +44,7 @@ def to_tensor(data):
     from collections import defaultdict
     data_dict = defaultdict(list)
     to_tensor_idxs = []
+    
     for idx, v in enumerate(data):
         if isinstance(v, (np.ndarray, paddle.Tensor, numbers.Number)):
             if idx not in to_tensor_idxs:
@@ -57,6 +58,7 @@ def to_tensor(data):
 class SerPredictor(object):
     def __init__(self, config):
         global_config = config['Global']
+        self.algorithm = config['Architecture']["algorithm"]
 
         # build post process
         self.post_process_class = build_post_process(config['PostProcess'],
@@ -70,7 +72,7 @@ class SerPredictor(object):
 
         from paddleocr import PaddleOCR
 
-        self.ocr_engine = PaddleOCR(use_angle_cls=False, show_log=False)
+        self.ocr_engine = PaddleOCR(use_angle_cls=False, show_log=False, use_gpu=global_config['use_gpu'])
 
         # create data ops
         transforms = []
@@ -80,8 +82,8 @@ class SerPredictor(object):
                 op[op_name]['ocr_engine'] = self.ocr_engine
             elif op_name == 'KeepKeys':
                 op[op_name]['keep_keys'] = [
-                    'input_ids', 'labels', 'bbox', 'image', 'attention_mask',
-                    'token_type_ids', 'segment_offset_id', 'ocr_info',
+                    'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels',
+                    'segment_offset_id', 'ocr_info',
                     'entities'
                 ]
 
@@ -99,9 +101,11 @@ class SerPredictor(object):
         batch = transform(data, self.ops)
         batch = to_tensor(batch)
         preds = self.model(batch)
+        if self.algorithm in ['LayoutLMv2', 'LayoutXLM']:
+            preds = preds[0]
+        
         post_result = self.post_process_class(
             preds,
-            attention_masks=batch[4],
             segment_offset_ids=batch[6],
             ocr_infos=batch[7])
         return post_result, batch
@@ -138,8 +142,6 @@ if __name__ == '__main__':
             save_img_path = os.path.join(
                 config['Global']['save_res_path'],
                 os.path.splitext(os.path.basename(img_path))[0] + "_ser.jpg")
-            logger.info("process: [{}/{}], save result to {}".format(
-                idx, len(infer_imgs), save_img_path))
 
             result, _ = ser_engine(data)
             result = result[0]
@@ -149,3 +151,7 @@ if __name__ == '__main__':
                 }, ensure_ascii=False) + "\n")
             img_res = draw_ser_results(img_path, result)
             cv2.imwrite(save_img_path, img_res)
+
+            logger.info("process: [{}/{}], save result to {}".format(
+                idx, len(infer_imgs), save_img_path))
+

tools/infer_vqa_token_ser_re.py

@@ -38,7 +38,7 @@ from ppocr.utils.save_load import load_model
 from ppocr.utils.visual import draw_re_results
 from ppocr.utils.logging import get_logger
 from ppocr.utils.utility import get_image_file_list, load_vqa_bio_label_maps, print_dict
-from tools.program import ArgsParser, load_config, merge_config, check_gpu
+from tools.program import ArgsParser, load_config, merge_config
 from tools.infer_vqa_token_ser import SerPredictor
 
 
@@ -107,7 +107,7 @@ def make_input(ser_inputs, ser_results):
     # remove ocr_info segment_offset_id and label in ser input
     ser_inputs.pop(7)
     ser_inputs.pop(6)
-    ser_inputs.pop(1)
+    ser_inputs.pop(5)
     return ser_inputs, entity_idx_dict_batch
 
 
@@ -131,9 +131,7 @@ class SerRePredictor(object):
         self.model.eval()
 
     def __call__(self, img_path):
-        ser_results, ser_inputs = self.ser_engine(img_path)
-        paddle.save(ser_inputs, 'ser_inputs.npy')
-        paddle.save(ser_results, 'ser_results.npy')
+        ser_results, ser_inputs = self.ser_engine({'img_path': img_path})
         re_input, entity_idx_dict_batch = make_input(ser_inputs, ser_results)
         preds = self.model(re_input)
         post_result = self.post_process_class(
@@ -155,7 +153,6 @@ def preprocess():
 
     # check if set use_gpu=True in paddlepaddle cpu version
     use_gpu = config['Global']['use_gpu']
-    check_gpu(use_gpu)
 
     device = 'gpu:{}'.format(dist.ParallelEnv().dev_id) if use_gpu else 'cpu'
     device = paddle.set_device(device)
@@ -185,9 +182,7 @@ if __name__ == '__main__':
         for idx, img_path in enumerate(infer_imgs):
             save_img_path = os.path.join(
                 config['Global']['save_res_path'],
-                os.path.splitext(os.path.basename(img_path))[0] + "_ser.jpg")
-            logger.info("process: [{}/{}], save result to {}".format(
-                idx, len(infer_imgs), save_img_path))
+                os.path.splitext(os.path.basename(img_path))[0] + "_ser_re.jpg")
 
             result = ser_re_engine(img_path)
             result = result[0]
@@ -197,3 +192,6 @@ if __name__ == '__main__':
                 }, ensure_ascii=False) + "\n")
             img_res = draw_re_results(img_path, result)
             cv2.imwrite(save_img_path, img_res)
+            
+            logger.info("process: [{}/{}], save result to {}".format(
+                idx, len(infer_imgs), save_img_path))
\ No newline at end of file