Merge branch 'PaddlePaddle:dygraph' into dygraph

configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_cml.yml

@@ -28,7 +28,7 @@ Architecture:
       algorithm: DB
       Transform:
       Backbone:
-        name: ResNet
+        name: ResNet_vd
         layers: 18
       Neck:
         name: DBFPN

configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_distill.yml

@@ -45,7 +45,7 @@ Architecture:
       algorithm: DB
       Transform:
       Backbone:
-        name: ResNet
+        name: ResNet_vd
         layers: 18
       Neck:
         name: DBFPN

configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml

@@ -61,7 +61,7 @@ Architecture:
       model_type: det
       algorithm: DB
       Backbone:
-        name: ResNet
+        name: ResNet_vd
         in_channels: 3
         layers: 50
       Neck:

configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_dml.yml

@@ -25,7 +25,7 @@ Architecture:
       model_type: det
       algorithm: DB
       Backbone:
-        name: ResNet
+        name: ResNet_vd
         in_channels: 3
         layers: 50
       Neck:
@@ -40,7 +40,7 @@ Architecture:
       model_type: det
       algorithm: DB
       Backbone:
-        name: ResNet
+        name: ResNet_vd
         in_channels: 3
         layers: 50
       Neck:

configs/det/ch_ppocr_v2.0/ch_det_res18_db_v2.0.yml

@@ -20,7 +20,7 @@ Architecture:
   algorithm: DB
   Transform:
   Backbone:
-    name: ResNet
+    name: ResNet_vd
     layers: 18
     disable_se: True
   Neck:

configs/det/det_r50_db++_ic15.yml

+Global:
+  debug: false
+  use_gpu: true
+  epoch_num: 1000
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/det_r50_icdar15/
+  save_epoch_step: 200
+  eval_batch_step:
+  - 0
+  - 2000
+  cal_metric_during_train: false
+  pretrained_model: ./pretrain_models/ResNet50_dcn_asf_synthtext_pretrained
+  checkpoints: null
+  save_inference_dir: null
+  use_visualdl: false
+  infer_img: doc/imgs_en/img_10.jpg
+  save_res_path: ./checkpoints/det_db/predicts_db.txt
+Architecture:
+  model_type: det
+  algorithm: DB++
+  Transform: null
+  Backbone:
+    name: ResNet
+    layers: 50
+    dcn_stage: [False, True, True, True]
+  Neck:
+    name: DBFPN
+    out_channels: 256
+    use_asf: True
+  Head:
+    name: DBHead
+    k: 50
+Loss:
+  name: DBLoss
+  balance_loss: true
+  main_loss_type: BCELoss
+  alpha: 5
+  beta: 10
+  ohem_ratio: 3
+Optimizer:
+  name: Momentum
+  momentum: 0.9
+  lr:
+    name: DecayLearningRate
+    learning_rate: 0.007
+    epochs: 1000
+    factor: 0.9
+    end_lr: 0
+  weight_decay: 0.0001
+PostProcess:
+  name: DBPostProcess
+  thresh: 0.3
+  box_thresh: 0.6
+  max_candidates: 1000
+  unclip_ratio: 1.5
+Metric:
+  name: DetMetric
+  main_indicator: hmean
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/icdar2015/text_localization/                                                                        
+    label_file_list:                                                                                            
+    - ./train_data/icdar2015/text_localization/train_icdar2015_label.txt                                                     
+    ratio_list:                                                                                                 
+    - 1.0 
+    transforms:
+    - DecodeImage:
+        img_mode: BGR
+        channel_first: false
+    - DetLabelEncode: null
+    - IaaAugment:
+        augmenter_args:
+        - type: Fliplr
+          args:
+            p: 0.5
+        - type: Affine
+          args:
+            rotate:
+            - -10
+            - 10
+        - type: Resize
+          args:
+            size:
+            - 0.5
+            - 3
+    - EastRandomCropData:
+        size:
+        - 640
+        - 640
+        max_tries: 10
+        keep_ratio: true
+    - MakeShrinkMap:
+        shrink_ratio: 0.4
+        min_text_size: 8
+    - MakeBorderMap:
+        shrink_ratio: 0.4
+        thresh_min: 0.3
+        thresh_max: 0.7
+    - NormalizeImage:
+        scale: 1./255.
+        mean:
+        - 0.48109378172549
+        - 0.45752457890196
+        - 0.40787054090196
+        std:
+        - 1.0
+        - 1.0
+        - 1.0
+        order: hwc
+    - ToCHWImage: null
+    - KeepKeys:
+        keep_keys:
+        - image
+        - threshold_map
+        - threshold_mask
+        - shrink_map
+        - shrink_mask
+  loader:
+    shuffle: true
+    drop_last: false
+    batch_size_per_card: 4
+    num_workers: 8
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/icdar2015/text_localization                                                                          
+    label_file_list:                                                                                            
+    - ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
+    transforms:
+    - DecodeImage:
+        img_mode: BGR
+        channel_first: false
+    - DetLabelEncode: null
+    - DetResizeForTest:
+        image_shape:
+        - 1152
+        - 2048
+    - NormalizeImage:
+        scale: 1./255.
+        mean:
+        - 0.48109378172549
+        - 0.45752457890196
+        - 0.40787054090196
+        std:
+        - 1.0
+        - 1.0
+        - 1.0
+        order: hwc
+    - ToCHWImage: null
+    - KeepKeys:
+        keep_keys:
+        - image
+        - shape
+        - polys
+        - ignore_tags
+  loader:
+    shuffle: false
+    drop_last: false
+    batch_size_per_card: 1
+    num_workers: 2
+profiler_options: null

configs/det/det_r50_db++_td_tr.yml

+Global:
+  debug: false
+  use_gpu: true
+  epoch_num: 1000
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/det_r50_td_tr/
+  save_epoch_step: 200
+  eval_batch_step:
+  - 0
+  - 2000
+  cal_metric_during_train: false
+  pretrained_model: ./pretrain_models/ResNet50_dcn_asf_synthtext_pretrained
+  checkpoints: null
+  save_inference_dir: null
+  use_visualdl: false
+  infer_img: doc/imgs_en/img_10.jpg
+  save_res_path: ./checkpoints/det_db/predicts_db.txt
+Architecture:
+  model_type: det
+  algorithm: DB++
+  Transform: null
+  Backbone:
+    name: ResNet
+    layers: 50
+    dcn_stage: [False, True, True, True]
+  Neck:
+    name: DBFPN
+    out_channels: 256
+    use_asf: True
+  Head:
+    name: DBHead
+    k: 50
+Loss:
+  name: DBLoss
+  balance_loss: true
+  main_loss_type: BCELoss
+  alpha: 5
+  beta: 10
+  ohem_ratio: 3
+Optimizer:
+  name: Momentum
+  momentum: 0.9
+  lr:
+    name: DecayLearningRate
+    learning_rate: 0.007
+    epochs: 1000
+    factor: 0.9
+    end_lr: 0
+  weight_decay: 0.0001
+PostProcess:
+  name: DBPostProcess
+  thresh: 0.3
+  box_thresh: 0.5
+  max_candidates: 1000
+  unclip_ratio: 1.5
+Metric:
+  name: DetMetric
+  main_indicator: hmean
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/                                                                                 
+    label_file_list:                                                                                            
+    - ./train_data/TD_TR/TD500/train_gt_labels.txt                                                           
+    - ./train_data/TD_TR/TR400/gt_labels.txt                                                                 
+    ratio_list:                                                                                                 
+    - 1.0                                                                                                       
+    - 1.0 
+    transforms:
+    - DecodeImage:
+        img_mode: BGR
+        channel_first: false
+    - DetLabelEncode: null
+    - IaaAugment:
+        augmenter_args:
+        - type: Fliplr
+          args:
+            p: 0.5
+        - type: Affine
+          args:
+            rotate:
+            - -10
+            - 10
+        - type: Resize
+          args:
+            size:
+            - 0.5
+            - 3
+    - EastRandomCropData:
+        size:
+        - 640
+        - 640
+        max_tries: 10
+        keep_ratio: true
+    - MakeShrinkMap:
+        shrink_ratio: 0.4
+        min_text_size: 8
+    - MakeBorderMap:
+        shrink_ratio: 0.4
+        thresh_min: 0.3
+        thresh_max: 0.7
+    - NormalizeImage:
+        scale: 1./255.
+        mean:
+        - 0.48109378172549
+        - 0.45752457890196
+        - 0.40787054090196
+        std:
+        - 1.0
+        - 1.0
+        - 1.0
+        order: hwc
+    - ToCHWImage: null
+    - KeepKeys:
+        keep_keys:
+        - image
+        - threshold_map
+        - threshold_mask
+        - shrink_map
+        - shrink_mask
+  loader:
+    shuffle: true
+    drop_last: false
+    batch_size_per_card: 4
+    num_workers: 8
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/                                                                          
+    label_file_list:                                                                                            
+    - ./train_data/TD_TR/TD500/test_gt_labels.txt
+    transforms:
+    - DecodeImage:
+        img_mode: BGR
+        channel_first: false
+    - DetLabelEncode: null
+    - DetResizeForTest:
+        image_shape:
+        - 736
+        - 736
+        keep_ratio: True
+    - NormalizeImage:
+        scale: 1./255.
+        mean:
+        - 0.48109378172549
+        - 0.45752457890196
+        - 0.40787054090196
+        std:
+        - 1.0
+        - 1.0
+        - 1.0
+        order: hwc
+    - ToCHWImage: null
+    - KeepKeys:
+        keep_keys:
+        - image
+        - shape
+        - polys
+        - ignore_tags
+  loader:
+    shuffle: false
+    drop_last: false
+    batch_size_per_card: 1
+    num_workers: 2
+profiler_options: null

configs/det/det_r50_vd_db.yml

@@ -20,7 +20,7 @@ Architecture:
   algorithm: DB
   Transform:
   Backbone:
-    name: ResNet
+    name: ResNet_vd
     layers: 50
   Neck:
     name: DBFPN

configs/det/det_r50_vd_dcn_fce_ctw.yml

@@ -21,7 +21,7 @@ Architecture:
   algorithm: FCE
   Transform:
   Backbone:
-    name: ResNet
+    name: ResNet_vd
     layers: 50
     dcn_stage: [False, True, True, True]
     out_indices: [1,2,3]

configs/det/det_r50_vd_east.yml

@@ -20,7 +20,7 @@ Architecture:
   algorithm: EAST
   Transform:
   Backbone:
-    name: ResNet
+    name: ResNet_vd
     layers: 50
   Neck:
     name: EASTFPN

configs/det/det_r50_vd_pse.yml

@@ -20,7 +20,7 @@ Architecture:
   algorithm: PSE
   Transform:
   Backbone:
-    name: ResNet
+    name: ResNet_vd
     layers: 50
   Neck:
     name: FPN

configs/det/det_res18_db_v2.0.yml

@@ -20,7 +20,7 @@ Architecture:
   algorithm: DB
   Transform:
   Backbone:
-    name: ResNet
+    name: ResNet_vd
     layers: 18
     disable_se: True
   Neck:

configs/kie/kie_unet_sdmgr.yml

@@ -17,7 +17,7 @@ Global:
   checkpoints:
   save_inference_dir:
   use_visualdl: False
-  class_path: ./train_data/wildreceipt/class_list.txt
+  class_path: &class_path ./train_data/wildreceipt/class_list.txt
   infer_img: ./train_data/wildreceipt/1.txt
   save_res_path: ./output/sdmgr_kie/predicts_kie.txt
   img_scale: [ 1024, 512 ]
@@ -72,6 +72,7 @@ Train:
           order: 'hwc'
       - KieLabelEncode: # Class handling label
           character_dict_path: ./train_data/wildreceipt/dict.txt
+          class_path: *class_path
       - KieResize:
       - ToCHWImage:
       - KeepKeys:
@@ -88,7 +89,6 @@ Eval:
     data_dir: ./train_data/wildreceipt
     label_file_list:
       - ./train_data/wildreceipt/wildreceipt_test.txt
-      # - /paddle/data/PaddleOCR/train_data/wildreceipt/1.txt
     transforms:
       - DecodeImage: # load image
           img_mode: RGB

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:
@@ -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
@@ -54,7 +54,7 @@ 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
     transforms:
       - DecodeImage: # load image
           img_mode: RGB
@@ -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
@@ -89,7 +89,7 @@ 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
@@ -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:
@@ -44,7 +44,7 @@ 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
@@ -55,7 +55,7 @@ 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
     transforms:
       - DecodeImage: # load image
           img_mode: RGB
@@ -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
@@ -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
@@ -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

@@ -11,7 +11,7 @@ Global:
   save_inference_dir:
   use_visualdl: False
   seed: 2022
-  infer_img: doc/vqa/input/zh_val_42.jpg
+  infer_img: ppstructure/docs/vqa/input/zh_val_42.jpg
   save_res_path: ./output/ser
 
 Architecture:
@@ -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
@@ -54,7 +54,7 @@ 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
@@ -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
@@ -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
@@ -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

doc/doc_ch/algorithm_det_db.md

-# DB
+# DB与DB++
 
 - [1. 算法简介](#1)
 - [2. 环境配置](#2)
@@ -21,12 +21,24 @@
 > Liao, Minghui and Wan, Zhaoyi and Yao, Cong and Chen, Kai and Bai, Xiang
 > AAAI, 2020
 
+> [Real-Time Scene Text Detection with Differentiable Binarization and Adaptive Scale Fusion](https://arxiv.org/abs/2202.10304)
+> Liao, Minghui and Zou, Zhisheng and Wan, Zhaoyi and Yao, Cong and Bai, Xiang
+> TPAMI, 2022
+
+
 在ICDAR2015文本检测公开数据集上，算法复现效果如下：
 
 |模型|骨干网络|配置文件|precision|recall|Hmean|下载链接|
 | --- | --- | --- | --- | --- | --- | --- |
 |DB|ResNet50_vd|[configs/det/det_r50_vd_db.yml](../../configs/det/det_r50_vd_db.yml)|86.41%|78.72%|82.38%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_v2.0_train.tar)|
 |DB|MobileNetV3|[configs/det/det_mv3_db.yml](../../configs/det/det_mv3_db.yml)|77.29%|73.08%|75.12%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar)|
+|DB++|ResNet50|[configs/det/det_r50_db++_ic15.yml](../../configs/det/det_r50_db++_ic15.yml)|90.89%|82.66%|86.58%|[合成数据预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/ResNet50_dcn_asf_synthtext_pretrained.pdparams)/[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_r50_db%2B%2B_icdar15_train.tar)|
+
+在TD_TR文本检测公开数据集上，算法复现效果如下：
+
+|模型|骨干网络|配置文件|precision|recall|Hmean|下载链接|
+| --- | --- | --- | --- | --- | --- | --- |
+|DB++|ResNet50|[configs/det/det_r50_db++_td_tr.yml](../../configs/det/det_r50_db++_td_tr.yml)|92.92%|86.48%|89.58%|[合成数据预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/ResNet50_dcn_asf_synthtext_pretrained.pdparams)/[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_r50_db%2B%2B_td_tr_train.tar)|
 
 
 <a name="2"></a>
@@ -54,7 +66,7 @@ python3 tools/export_model.py -c configs/det/det_r50_vd_db.yml -o Global.pretrai
 DB文本检测模型推理，可以执行如下命令：
 
 ```shell
-python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_db/"
+python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_db/" --det_algorithm="DB"
 ```
 
 可视化文本检测结果默认保存到`./inference_results`文件夹里面，结果文件的名称前缀为'det_res'。结果示例如下：
@@ -96,4 +108,12 @@ DB模型还支持以下推理部署方式：
   pages={11474--11481},
   year={2020}
 }
-```
+
\ No newline at end of file
+@article{liao2022real,
+  title={Real-Time Scene Text Detection with Differentiable Binarization and Adaptive Scale Fusion},
+  author={Liao, Minghui and Zou, Zhisheng and Wan, Zhaoyi and Yao, Cong and Bai, Xiang},
+  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
+  year={2022},
+  publisher={IEEE}
+}
+```

doc/doc_ch/algorithm_overview.md

@@ -86,8 +86,9 @@
 |SAR|Resnet31| 87.20% | rec_r31_sar | [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_r31_sar_train.tar) |
 |SEED|Aster_Resnet| 85.35% | rec_resnet_stn_bilstm_att | [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_resnet_stn_bilstm_att.tar) |
 |SVTR|SVTR-Tiny| 89.25% | rec_svtr_tiny_none_ctc_en | [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/rec_svtr_tiny_none_ctc_en_train.tar) |
-|ViTSTR|ViTSTR| 79.82% | rec_vitstr_none_ce_en | [训练模型](https://paddleocr.bj.bcebos.com/rec_vitstr_none_ce_train.tar) |
-|ABINet|Resnet45| 90.75% | rec_r45_abinet_en | [训练模型](https://paddleocr.bj.bcebos.com/rec_r45_abinet_train.tar) |
+|ViTSTR|ViTSTR| 79.82% | rec_vitstr_none_ce | [训练模型](https://paddleocr.bj.bcebos.com/rec_vitstr_none_ce_train.tar) |
+|ABINet|Resnet45| 90.75% | rec_r45_abinet | [训练模型](https://paddleocr.bj.bcebos.com/rec_r45_abinet_train.tar) |
+
 
 <a name="2"></a>
 

doc/doc_ch/dataset/ocr_datasets.md

@@ -34,6 +34,7 @@ json.dumps编码前的图像标注信息是包含多个字典的list，字典中
 | ICDAR 2015 |https://rrc.cvc.uab.es/?ch=4&com=downloads| [train](https://paddleocr.bj.bcebos.com/dataset/train_icdar2015_label.txt) / [test](https://paddleocr.bj.bcebos.com/dataset/test_icdar2015_label.txt) |
 | ctw1500 |https://paddleocr.bj.bcebos.com/dataset/ctw1500.zip| 图片下载地址中已包含 |
 | total text |https://paddleocr.bj.bcebos.com/dataset/total_text.tar| 图片下载地址中已包含 |
+| td tr |https://paddleocr.bj.bcebos.com/dataset/TD_TR.tar| 图片下载地址中已包含 |
 
 #### 1.2.1 ICDAR 2015
 ICDAR 2015 数据集包含1000张训练图像和500张测试图像。ICDAR 2015 数据集可以从上表中链接下载，首次下载需注册。

doc/doc_en/algorithm_overview_en.md

@@ -85,8 +85,9 @@ Refer to [DTRB](https://arxiv.org/abs/1904.01906), the training and evaluation r
 |SAR|Resnet31| 87.20% | rec_r31_sar | [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_r31_sar_train.tar) |
 |SEED|Aster_Resnet| 85.35% | rec_resnet_stn_bilstm_att | [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_resnet_stn_bilstm_att.tar) |
 |SVTR|SVTR-Tiny| 89.25% | rec_svtr_tiny_none_ctc_en | [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/rec_svtr_tiny_none_ctc_en_train.tar) |
-|ViTSTR|ViTSTR| 79.82% | rec_vitstr_none_ce_en | [trained model](https://paddleocr.bj.bcebos.com/rec_vitstr_none_none_train.tar) |
-|ABINet|Resnet45| 90.75% | rec_r45_abinet_en | [trained model](https://paddleocr.bj.bcebos.com/rec_r45_abinet_train.tar) |
+|ViTSTR|ViTSTR| 79.82% | rec_vitstr_none_ce | [trained model](https://paddleocr.bj.bcebos.com/rec_vitstr_none_none_train.tar) |
+|ABINet|Resnet45| 90.75% | rec_r45_abinet | [trained model](https://paddleocr.bj.bcebos.com/rec_r45_abinet_train.tar) |
+
 
 <a name="2"></a>
 

ppocr/data/imaug/__init__.py

@@ -23,9 +23,10 @@ from .random_crop_data import EastRandomCropData, RandomCropImgMask
 from .make_pse_gt import MakePseGt
 
 
-from .rec_img_aug import RecAug, RecConAug, RecResizeImg, ClsResizeImg, \
-    SRNRecResizeImg, GrayRecResizeImg, SARRecResizeImg, PRENResizeImg, \
-    ABINetRecResizeImg, SVTRRecResizeImg, ABINetRecAug
+
+from .rec_img_aug import BaseDataAugmentation, RecAug, RecConAug, RecResizeImg, ClsResizeImg, \
+                         SRNRecResizeImg, GrayRecResizeImg, SARRecResizeImg, PRENResizeImg, \
+                         ABINetRecResizeImg, SVTRRecResizeImg, ABINetRecAug
 from .ssl_img_aug import SSLRotateResize
 from .randaugment import RandAugment
 from .copy_paste import CopyPaste

ppocr/data/imaug/label_ops.py

@@ -259,15 +259,26 @@ class E2ELabelEncodeTrain(object):
 
 
 class KieLabelEncode(object):
-    def __init__(self, character_dict_path, norm=10, directed=False, **kwargs):
+    def __init__(self,
+                 character_dict_path,
+                 class_path,
+                 norm=10,
+                 directed=False,
+                 **kwargs):
         super(KieLabelEncode, self).__init__()
         self.dict = dict({'': 0})
+        self.label2classid_map = dict()
         with open(character_dict_path, 'r', encoding='utf-8') as fr:
             idx = 1
             for line in fr:
                 char = line.strip()
                 self.dict[char] = idx
                 idx += 1
+        with open(class_path, "r") as fin:
+            lines = fin.readlines()
+            for idx, line in enumerate(lines):
+                line = line.strip("\n")
+                self.label2classid_map[line] = idx
         self.norm = norm
         self.directed = directed
 
@@ -408,7 +419,7 @@ class KieLabelEncode(object):
             text_ind = [self.dict[c] for c in text if c in self.dict]
             text_inds.append(text_ind)
             if 'label' in ann.keys():
-                labels.append(ann['label'])
+                labels.append(self.label2classid_map[ann['label']])
             elif 'key_cls' in ann.keys():
                 labels.append(ann['key_cls'])
             else:
@@ -876,15 +887,16 @@ class VQATokenLabelEncode(object):
         for info in ocr_info:
             if train_re:
                 # for re
-                if len(info["text"]) == 0:
+                if len(info["transcription"]) == 0:
                     empty_entity.add(info["id"])
                     continue
                 id2label[info["id"]] = info["label"]
                 relations.extend([tuple(sorted(l)) for l in info["linking"]])
             # smooth_box
+            info["bbox"] = self.trans_poly_to_bbox(info["points"])
             bbox = self._smooth_box(info["bbox"], height, width)
 
-            text = info["text"]
+            text = info["transcription"]
             encode_res = self.tokenizer.encode(
                 text, pad_to_max_seq_len=False, return_attention_mask=True)
 
@@ -944,29 +956,29 @@ class VQATokenLabelEncode(object):
             data['entity_id_to_index_map'] = entity_id_to_index_map
         return data
 
-    def _load_ocr_info(self, data):
-        def trans_poly_to_bbox(poly):
-            x1 = np.min([p[0] for p in poly])
-            x2 = np.max([p[0] for p in poly])
-            y1 = np.min([p[1] for p in poly])
-            y2 = np.max([p[1] for p in poly])
-            return [x1, y1, x2, y2]
+    def trans_poly_to_bbox(self, poly):
+        x1 = np.min([p[0] for p in poly])
+        x2 = np.max([p[0] for p in poly])
+        y1 = np.min([p[1] for p in poly])
+        y2 = np.max([p[1] for p in poly])
+        return [x1, y1, x2, y2]
 
+    def _load_ocr_info(self, data):
         if self.infer_mode:
             ocr_result = self.ocr_engine.ocr(data['image'], cls=False)
             ocr_info = []
             for res in ocr_result:
                 ocr_info.append({
-                    "text": res[1][0],
-                    "bbox": trans_poly_to_bbox(res[0]),
-                    "poly": res[0],
+                    "transcription": res[1][0],
+                    "bbox": self.trans_poly_to_bbox(res[0]),
+                    "points": res[0],
                 })
             return ocr_info
         else:
             info = data['label']
             # read text info
             info_dict = json.loads(info)
-            return info_dict["ocr_info"]
+            return info_dict
 
     def _smooth_box(self, bbox, height, width):
         bbox[0] = int(bbox[0] * 1000.0 / width)
@@ -977,7 +989,7 @@ class VQATokenLabelEncode(object):
 
     def _parse_label(self, label, encode_res):
         gt_label = []
-        if label.lower() == "other":
+        if label.lower() in ["other", "others", "ignore"]:
             gt_label.extend([0] * len(encode_res["input_ids"]))
         else:
             gt_label.append(self.label2id_map[("b-" + label).upper()])

ppocr/data/imaug/operators.py

@@ -205,9 +205,12 @@ class DetResizeForTest(object):
     def __init__(self, **kwargs):
         super(DetResizeForTest, self).__init__()
         self.resize_type = 0
+        self.keep_ratio = False
         if 'image_shape' in kwargs:
             self.image_shape = kwargs['image_shape']
             self.resize_type = 1
+            if 'keep_ratio' in kwargs:
+                self.keep_ratio = kwargs['keep_ratio']
         elif 'limit_side_len' in kwargs:
             self.limit_side_len = kwargs['limit_side_len']
             self.limit_type = kwargs.get('limit_type', 'min')
@@ -237,6 +240,10 @@ class DetResizeForTest(object):
     def resize_image_type1(self, img):
         resize_h, resize_w = self.image_shape
         ori_h, ori_w = img.shape[:2]  # (h, w, c)
+        if self.keep_ratio is True:
+            resize_w = ori_w * resize_h / ori_h
+            N = math.ceil(resize_w / 32)
+            resize_w = N * 32
         ratio_h = float(resize_h) / ori_h
         ratio_w = float(resize_w) / ori_w
         img = cv2.resize(img, (int(resize_w), int(resize_h)))

ppocr/data/imaug/vqa/__init__.py

@@ -13,7 +13,12 @@
 # limitations under the License.
 
 from .token import VQATokenPad, VQASerTokenChunk, VQAReTokenChunk, VQAReTokenRelation
+from .augment import DistortBBox
 
 __all__ = [
-    'VQATokenPad', 'VQASerTokenChunk', 'VQAReTokenChunk', 'VQAReTokenRelation'
+    'VQATokenPad',
+    'VQASerTokenChunk',
+    'VQAReTokenChunk',
+    'VQAReTokenRelation',
+    'DistortBBox',
 ]

ppocr/data/imaug/vqa/augment.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 os
+import sys
+import numpy as np
+import random
+
+
+class DistortBBox:
+    def __init__(self, prob=0.5, max_scale=1, **kwargs):
+        """Random distort bbox
+        """
+        self.prob = prob
+        self.max_scale = max_scale
+
+    def __call__(self, data):
+        if random.random() > self.prob:
+            return data
+        bbox = np.array(data['bbox'])
+        rnd_scale = (np.random.rand(*bbox.shape) - 0.5) * 2 * self.max_scale
+        bbox = np.round(bbox + rnd_scale).astype(bbox.dtype)
+        data['bbox'] = np.clip(data['bbox'], 0, 1000)
+        data['bbox'] = bbox.tolist()
+        sys.stdout.flush()
+        return data

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/__init__.py

@@ -18,9 +18,10 @@ __all__ = ["build_backbone"]
 def build_backbone(config, model_type):
     if model_type == "det" or model_type == "table":
         from .det_mobilenet_v3 import MobileNetV3
-        from .det_resnet_vd import ResNet
+        from .det_resnet import ResNet
+        from .det_resnet_vd import ResNet_vd
         from .det_resnet_vd_sast import ResNet_SAST
-        support_dict = ["MobileNetV3", "ResNet", "ResNet_SAST"]
+        support_dict = ["MobileNetV3", "ResNet", "ResNet_vd", "ResNet_SAST"]
     elif model_type == "rec" or model_type == "cls":
         from .rec_mobilenet_v3 import MobileNetV3
         from .rec_resnet_vd import ResNet

ppocr/modeling/backbones/det_resnet.py

+# copyright (c) 2022 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+import paddle
+from paddle import ParamAttr
+import paddle.nn as nn
+import paddle.nn.functional as F
+from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
+from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
+from paddle.nn.initializer import Uniform
+
+import math
+
+from paddle.vision.ops import DeformConv2D
+from paddle.regularizer import L2Decay
+from paddle.nn.initializer import Normal, Constant, XavierUniform
+from .det_resnet_vd import DeformableConvV2, ConvBNLayer
+
+
+class BottleneckBlock(nn.Layer):
+    def __init__(self,
+                 num_channels,
+                 num_filters,
+                 stride,
+                 shortcut=True,
+                 is_dcn=False):
+        super(BottleneckBlock, self).__init__()
+
+        self.conv0 = ConvBNLayer(
+            in_channels=num_channels,
+            out_channels=num_filters,
+            kernel_size=1,
+            act="relu", )
+        self.conv1 = ConvBNLayer(
+            in_channels=num_filters,
+            out_channels=num_filters,
+            kernel_size=3,
+            stride=stride,
+            act="relu",
+            is_dcn=is_dcn,
+            dcn_groups=1, )
+        self.conv2 = ConvBNLayer(
+            in_channels=num_filters,
+            out_channels=num_filters * 4,
+            kernel_size=1,
+            act=None, )
+
+        if not shortcut:
+            self.short = ConvBNLayer(
+                in_channels=num_channels,
+                out_channels=num_filters * 4,
+                kernel_size=1,
+                stride=stride, )
+
+        self.shortcut = shortcut
+
+        self._num_channels_out = num_filters * 4
+
+    def forward(self, inputs):
+        y = self.conv0(inputs)
+        conv1 = self.conv1(y)
+        conv2 = self.conv2(conv1)
+
+        if self.shortcut:
+            short = inputs
+        else:
+            short = self.short(inputs)
+
+        y = paddle.add(x=short, y=conv2)
+        y = F.relu(y)
+        return y
+
+
+class BasicBlock(nn.Layer):
+    def __init__(self,
+                 num_channels,
+                 num_filters,
+                 stride,
+                 shortcut=True,
+                 name=None):
+        super(BasicBlock, self).__init__()
+        self.stride = stride
+        self.conv0 = ConvBNLayer(
+            in_channels=num_channels,
+            out_channels=num_filters,
+            kernel_size=3,
+            stride=stride,
+            act="relu")
+        self.conv1 = ConvBNLayer(
+            in_channels=num_filters,
+            out_channels=num_filters,
+            kernel_size=3,
+            act=None)
+
+        if not shortcut:
+            self.short = ConvBNLayer(
+                in_channels=num_channels,
+                out_channels=num_filters,
+                kernel_size=1,
+                stride=stride)
+
+        self.shortcut = shortcut
+
+    def forward(self, inputs):
+        y = self.conv0(inputs)
+        conv1 = self.conv1(y)
+
+        if self.shortcut:
+            short = inputs
+        else:
+            short = self.short(inputs)
+        y = paddle.add(x=short, y=conv1)
+        y = F.relu(y)
+        return y
+
+
+class ResNet(nn.Layer):
+    def __init__(self,
+                 in_channels=3,
+                 layers=50,
+                 out_indices=None,
+                 dcn_stage=None):
+        super(ResNet, self).__init__()
+
+        self.layers = layers
+        self.input_image_channel = in_channels
+
+        supported_layers = [18, 34, 50, 101, 152]
+        assert layers in supported_layers, \
+            "supported layers are {} but input layer is {}".format(
+                supported_layers, layers)
+
+        if layers == 18:
+            depth = [2, 2, 2, 2]
+        elif layers == 34 or layers == 50:
+            depth = [3, 4, 6, 3]
+        elif layers == 101:
+            depth = [3, 4, 23, 3]
+        elif layers == 152:
+            depth = [3, 8, 36, 3]
+        num_channels = [64, 256, 512,
+                        1024] if layers >= 50 else [64, 64, 128, 256]
+        num_filters = [64, 128, 256, 512]
+
+        self.dcn_stage = dcn_stage if dcn_stage is not None else [
+            False, False, False, False
+        ]
+        self.out_indices = out_indices if out_indices is not None else [
+            0, 1, 2, 3
+        ]
+
+        self.conv = ConvBNLayer(
+            in_channels=self.input_image_channel,
+            out_channels=64,
+            kernel_size=7,
+            stride=2,
+            act="relu", )
+        self.pool2d_max = MaxPool2D(
+            kernel_size=3,
+            stride=2,
+            padding=1, )
+
+        self.stages = []
+        self.out_channels = []
+        if layers >= 50:
+            for block in range(len(depth)):
+                shortcut = False
+                block_list = []
+                is_dcn = self.dcn_stage[block]
+                for i in range(depth[block]):
+                    if layers in [101, 152] and block == 2:
+                        if i == 0:
+                            conv_name = "res" + str(block + 2) + "a"
+                        else:
+                            conv_name = "res" + str(block + 2) + "b" + str(i)
+                    else:
+                        conv_name = "res" + str(block + 2) + chr(97 + i)
+                    bottleneck_block = self.add_sublayer(
+                        conv_name,
+                        BottleneckBlock(
+                            num_channels=num_channels[block]
+                            if i == 0 else num_filters[block] * 4,
+                            num_filters=num_filters[block],
+                            stride=2 if i == 0 and block != 0 else 1,
+                            shortcut=shortcut,
+                            is_dcn=is_dcn))
+                    block_list.append(bottleneck_block)
+                    shortcut = True
+                if block in self.out_indices:
+                    self.out_channels.append(num_filters[block] * 4)
+                self.stages.append(nn.Sequential(*block_list))
+        else:
+            for block in range(len(depth)):
+                shortcut = False
+                block_list = []
+                for i in range(depth[block]):
+                    conv_name = "res" + str(block + 2) + chr(97 + i)
+                    basic_block = self.add_sublayer(
+                        conv_name,
+                        BasicBlock(
+                            num_channels=num_channels[block]
+                            if i == 0 else num_filters[block],
+                            num_filters=num_filters[block],
+                            stride=2 if i == 0 and block != 0 else 1,
+                            shortcut=shortcut))
+                    block_list.append(basic_block)
+                    shortcut = True
+                if block in self.out_indices:
+                    self.out_channels.append(num_filters[block])
+                self.stages.append(nn.Sequential(*block_list))
+
+    def forward(self, inputs):
+        y = self.conv(inputs)
+        y = self.pool2d_max(y)
+        out = []
+        for i, block in enumerate(self.stages):
+            y = block(y)
+            if i in self.out_indices:
+                out.append(y)
+        return out

ppocr/modeling/backbones/det_resnet_vd.py

@@ -25,7 +25,7 @@ from paddle.vision.ops import DeformConv2D
 from paddle.regularizer import L2Decay
 from paddle.nn.initializer import Normal, Constant, XavierUniform
 
-__all__ = ["ResNet"]
+__all__ = ["ResNet_vd", "ConvBNLayer", "DeformableConvV2"]
 
 
 class DeformableConvV2(nn.Layer):
@@ -104,6 +104,7 @@ class ConvBNLayer(nn.Layer):
                  kernel_size,
                  stride=1,
                  groups=1,
+                 dcn_groups=1,
                  is_vd_mode=False,
                  act=None,
                  is_dcn=False):
@@ -128,7 +129,7 @@ class ConvBNLayer(nn.Layer):
                 kernel_size=kernel_size,
                 stride=stride,
                 padding=(kernel_size - 1) // 2,
-                groups=2,  #groups,
+                groups=dcn_groups,  #groups,
                 bias_attr=False)
         self._batch_norm = nn.BatchNorm(out_channels, act=act)
 
@@ -162,7 +163,8 @@ class BottleneckBlock(nn.Layer):
             kernel_size=3,
             stride=stride,
             act='relu',
-            is_dcn=is_dcn)
+            is_dcn=is_dcn,
+            dcn_groups=2)
         self.conv2 = ConvBNLayer(
             in_channels=out_channels,
             out_channels=out_channels * 4,
@@ -238,14 +240,14 @@ class BasicBlock(nn.Layer):
         return y
 
 
-class ResNet(nn.Layer):
+class ResNet_vd(nn.Layer):
     def __init__(self,
                  in_channels=3,
                  layers=50,
                  dcn_stage=None,
                  out_indices=None,
                  **kwargs):
-        super(ResNet, self).__init__()
+        super(ResNet_vd, self).__init__()
 
         self.layers = layers
         supported_layers = [18, 34, 50, 101, 152, 200]
@@ -321,7 +323,6 @@ class ResNet(nn.Layer):
             for block in range(len(depth)):
                 block_list = []
                 shortcut = False
-                # is_dcn = self.dcn_stage[block]
                 for i in range(depth[block]):
                     basic_block = self.add_sublayer(
                         'bb_%d_%d' % (block, i),

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]
 
 
@@ -120,13 +122,15 @@ 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],
+            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/modeling/necks/db_fpn.py

@@ -105,9 +105,10 @@ class DSConv(nn.Layer):
 
 
 class DBFPN(nn.Layer):
-    def __init__(self, in_channels, out_channels, **kwargs):
+    def __init__(self, in_channels, out_channels, use_asf=False, **kwargs):
         super(DBFPN, self).__init__()
         self.out_channels = out_channels
+        self.use_asf = use_asf
         weight_attr = paddle.nn.initializer.KaimingUniform()
 
         self.in2_conv = nn.Conv2D(
@@ -163,6 +164,9 @@ class DBFPN(nn.Layer):
             weight_attr=ParamAttr(initializer=weight_attr),
             bias_attr=False)
 
+        if self.use_asf is True:
+            self.asf = ASFBlock(self.out_channels, self.out_channels // 4)
+
     def forward(self, x):
         c2, c3, c4, c5 = x
 
@@ -187,6 +191,10 @@ class DBFPN(nn.Layer):
         p3 = F.upsample(p3, scale_factor=2, mode="nearest", align_mode=1)
 
         fuse = paddle.concat([p5, p4, p3, p2], axis=1)
+
+        if self.use_asf is True:
+            fuse = self.asf(fuse, [p5, p4, p3, p2])
+
         return fuse
 
 
@@ -356,3 +364,64 @@ class LKPAN(nn.Layer):
 
         fuse = paddle.concat([p5, p4, p3, p2], axis=1)
         return fuse
+
+
+class ASFBlock(nn.Layer):
+    """
+    This code is refered from:
+        https://github.com/MhLiao/DB/blob/master/decoders/feature_attention.py
+    """
+
+    def __init__(self, in_channels, inter_channels, out_features_num=4):
+        """
+        Adaptive Scale Fusion (ASF) block of DBNet++
+        Args:
+            in_channels: the number of channels in the input data
+            inter_channels: the number of middle channels
+            out_features_num: the number of fused stages
+        """
+        super(ASFBlock, self).__init__()
+        weight_attr = paddle.nn.initializer.KaimingUniform()
+        self.in_channels = in_channels
+        self.inter_channels = inter_channels
+        self.out_features_num = out_features_num
+        self.conv = nn.Conv2D(in_channels, inter_channels, 3, padding=1)
+
+        self.spatial_scale = nn.Sequential(
+            #Nx1xHxW
+            nn.Conv2D(
+                in_channels=1,
+                out_channels=1,
+                kernel_size=3,
+                bias_attr=False,
+                padding=1,
+                weight_attr=ParamAttr(initializer=weight_attr)),
+            nn.ReLU(),
+            nn.Conv2D(
+                in_channels=1,
+                out_channels=1,
+                kernel_size=1,
+                bias_attr=False,
+                weight_attr=ParamAttr(initializer=weight_attr)),
+            nn.Sigmoid())
+
+        self.channel_scale = nn.Sequential(
+            nn.Conv2D(
+                in_channels=inter_channels,
+                out_channels=out_features_num,
+                kernel_size=1,
+                bias_attr=False,
+                weight_attr=ParamAttr(initializer=weight_attr)),
+            nn.Sigmoid())
+
+    def forward(self, fuse_features, features_list):
+        fuse_features = self.conv(fuse_features)
+        spatial_x = paddle.mean(fuse_features, axis=1, keepdim=True)
+        attention_scores = self.spatial_scale(spatial_x) + fuse_features
+        attention_scores = self.channel_scale(attention_scores)
+        assert len(features_list) == self.out_features_num
+
+        out_list = []
+        for i in range(self.out_features_num):
+            out_list.append(attention_scores[:, i:i + 1] * features_list[i])
+        return paddle.concat(out_list, axis=1)

ppocr/optimizer/learning_rate.py

@@ -308,3 +308,38 @@ class Const(object):
                 end_lr=self.learning_rate,
                 last_epoch=self.last_epoch)
         return learning_rate
+
+
+class DecayLearningRate(object):
+    """
+    DecayLearningRate learning rate decay
+    new_lr = (lr - end_lr) * (1 - epoch/decay_steps)**power + end_lr
+    Args:
+        learning_rate(float): initial learning rate
+        step_each_epoch(int): steps each epoch
+        epochs(int): total training epochs
+        factor(float): Power of polynomial, should greater than 0.0 to get learning rate decay. Default: 0.9
+        end_lr(float): The minimum final learning rate. Default: 0.0.
+    """
+
+    def __init__(self,
+                 learning_rate,
+                 step_each_epoch,
+                 epochs,
+                 factor=0.9,
+                 end_lr=0,
+                 **kwargs):
+        super(DecayLearningRate, self).__init__()
+        self.learning_rate = learning_rate
+        self.epochs = epochs + 1
+        self.factor = factor
+        self.end_lr = 0
+        self.decay_steps = step_each_epoch * epochs
+
+    def __call__(self):
+        learning_rate = lr.PolynomialDecay(
+            learning_rate=self.learning_rate,
+            decay_steps=self.decay_steps,
+            power=self.factor,
+            end_lr=self.end_lr)
+        return learning_rate

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 +65,11 @@ 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]
 

ppocr/utils/utility.py

@@ -91,18 +91,19 @@ def check_and_read_gif(img_path):
 def load_vqa_bio_label_maps(label_map_path):
     with open(label_map_path, "r", encoding='utf-8') as fin:
         lines = fin.readlines()
-    lines = [line.strip() for line in lines]
-    if "O" not in lines:
-        lines.insert(0, "O")
-    labels = []
-    for line in lines:
-        if line == "O":
-            labels.append("O")
-        else:
-            labels.append("B-" + line)
-            labels.append("I-" + line)
-    label2id_map = {label: idx for idx, label in enumerate(labels)}
-    id2label_map = {idx: label for idx, label in enumerate(labels)}
+    old_lines = [line.strip() for line in lines]
+    lines = ["O"]
+    for line in old_lines:
+        # "O" has already been in lines
+        if line.upper() in ["OTHER", "OTHERS", "IGNORE"]:
+            continue
+        lines.append(line)
+    labels = ["O"]
+    for line in lines[1:]:
+        labels.append("B-" + line)
+        labels.append("I-" + line)
+    label2id_map = {label.upper(): idx for idx, label in enumerate(labels)}
+    id2label_map = {idx: label.upper() for idx, label in enumerate(labels)}
     return label2id_map, id2label_map
 
 

ppocr/utils/visual.py

@@ -19,7 +19,7 @@ from PIL import Image, ImageDraw, ImageFont
 def draw_ser_results(image,
                      ocr_results,
                      font_path="doc/fonts/simfang.ttf",
-                     font_size=18):
+                     font_size=14):
     np.random.seed(2021)
     color = (np.random.permutation(range(255)),
              np.random.permutation(range(255)),
@@ -40,9 +40,15 @@ def draw_ser_results(image,
         if ocr_info["pred_id"] not in color_map:
             continue
         color = color_map[ocr_info["pred_id"]]
-        text = "{}: {}".format(ocr_info["pred"], ocr_info["text"])
+        text = "{}: {}".format(ocr_info["pred"], ocr_info["transcription"])
 
-        draw_box_txt(ocr_info["bbox"], text, draw, font, font_size, color)
+        if "bbox" in ocr_info:
+            # draw with ocr engine
+            bbox = ocr_info["bbox"]
+        else:
+            # draw with ocr groundtruth
+            bbox = trans_poly_to_bbox(ocr_info["points"])
+        draw_box_txt(bbox, text, draw, font, font_size, color)
 
     img_new = Image.blend(image, img_new, 0.5)
     return np.array(img_new)
@@ -62,6 +68,14 @@ def draw_box_txt(bbox, text, draw, font, font_size, color):
     draw.text((bbox[0][0] + 1, start_y), text, fill=(255, 255, 255), font=font)
 
 
+def trans_poly_to_bbox(poly):
+    x1 = np.min([p[0] for p in poly])
+    x2 = np.max([p[0] for p in poly])
+    y1 = np.min([p[1] for p in poly])
+    y2 = np.max([p[1] for p in poly])
+    return [x1, y1, x2, y2]
+
+
 def draw_re_results(image,
                     result,
                     font_path="doc/fonts/simfang.ttf",
@@ -80,10 +94,10 @@ def draw_re_results(image,
     color_line = (0, 255, 0)
 
     for ocr_info_head, ocr_info_tail in result:
-        draw_box_txt(ocr_info_head["bbox"], ocr_info_head["text"], draw, font,
-                     font_size, color_head)
-        draw_box_txt(ocr_info_tail["bbox"], ocr_info_tail["text"], draw, font,
-                     font_size, color_tail)
+        draw_box_txt(ocr_info_head["bbox"], ocr_info_head["transcription"],
+                     draw, font, font_size, color_head)
+        draw_box_txt(ocr_info_tail["bbox"], ocr_info_tail["transcription"],
+                     draw, font, font_size, color_tail)
 
         center_head = (
             (ocr_info_head['bbox'][0] + ocr_info_head['bbox'][2]) // 2,

ppstructure/docs/kie.md

@@ -16,7 +16,7 @@ SDMGR是一个关键信息提取算法，将每个检测到的文本区域分类
 训练和测试的数据采用wildreceipt数据集，通过如下指令下载数据集：
 
 ```
-wget https://paddleocr.bj.bcebos.com/dygraph_v2.1/kie/wildreceipt.tar && tar xf wildreceipt.tar
+wget https://paddleocr.bj.bcebos.com/ppstructure/dataset/wildreceipt.tar && tar xf wildreceipt.tar
 ```
 
 执行预测：

ppstructure/docs/kie_en.md

@@ -15,7 +15,7 @@ This section provides a tutorial example on how to quickly use, train, and evalu
 [Wildreceipt dataset](https://paperswithcode.com/dataset/wildreceipt) is used for this tutorial. It contains 1765 photos, with 25 classes, and 50000 text boxes, which can be downloaded by wget:
 
 ```shell
-wget https://paddleocr.bj.bcebos.com/dygraph_v2.1/kie/wildreceipt.tar && tar xf wildreceipt.tar
+wget https://paddleocr.bj.bcebos.com/ppstructure/dataset/wildreceipt.tar && tar xf wildreceipt.tar
 ```
 
 Download the pretrained model and predict the result:

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)
+- [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 +42,11 @@
 
 |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)
+- [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 +42,11 @@ 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 +40,13 @@ 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",
@@ -65,7 +72,7 @@ def init_args():
         "--recovery",
         type=bool,
         default=False,
-        help='Whether to enable layout of recovery')        
+        help='Whether to enable layout of recovery')
     return parser
 
 

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)
-  - [2. Performance](#2-performance)
-  - [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)
-    - [5.3 RE](#53-re)
-  - [6. Reference](#6-Reference-Links)
+- [1 Introduction](#1-introduction)
+- [2. Performance](#2-performance)
+- [3. Effect demo](#3-effect-demo)
+  - [3.1 SER](#31-ser)
+  - [3.2 RE](#32-re)
+- [4. Install](#4-install)
+  - [4.1 Install dependencies](#41-install-dependencies)
+  - [5.3 RE](#53-re)
+- [6. Reference Links](#6-reference-links)
+- [License](#license)
 
 # Document Visual Question Answering
 
@@ -125,13 +121,13 @@ If you want to experience the prediction process directly, you can download the
 
 * Download the processed dataset
 
-The download address of the processed XFUND Chinese dataset: [https://paddleocr.bj.bcebos.com/dataset/XFUND.tar](https://paddleocr.bj.bcebos.com/dataset/XFUND.tar).
+The download address of the processed XFUND Chinese dataset: [link](https://paddleocr.bj.bcebos.com/ppstructure/dataset/XFUND.tar).
 
 
 Download and unzip the dataset, and place the dataset in the current directory after unzipping.
 
 ```shell
-wget https://paddleocr.bj.bcebos.com/dataset/XFUND.tar
+wget https://paddleocr.bj.bcebos.com/ppstructure/dataset/XFUND.tar
 ````
 
 * Convert the dataset
@@ -187,17 +183,17 @@ 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 +201,24 @@ 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 +261,19 @@ 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

 [English](README.md) | 简体中文
 
-- [文档视觉问答（DOC-VQA）](#文档视觉问答doc-vqa)
-  - [1. 简介](#1-简介)
-  - [2. 性能](#2-性能)
-  - [3. 效果演示](#3-效果演示)
-    - [3.1 SER](#31-ser)
-    - [3.2 RE](#32-re)
-  - [4. 安装](#4-安装)
-    - [4.1 安装依赖](#41-安装依赖)
-    - [4.2 安装PaddleOCR（包含 PP-OCR 和 VQA）](#42-安装paddleocr包含-pp-ocr-和-vqa)
-  - [5. 使用](#5-使用)
-    - [5.1 数据和预训练模型准备](#51-数据和预训练模型准备)
-    - [5.2 SER](#52-ser)
-    - [5.3 RE](#53-re)
-  - [6. 参考链接](#6-参考链接)
+- [1. 简介](#1-简介)
+- [2. 性能](#2-性能)
+- [3. 效果演示](#3-效果演示)
+  - [3.1 SER](#31-ser)
+  - [3.2 RE](#32-re)
+- [4. 安装](#4-安装)
+  - [4.1 安装依赖](#41-安装依赖)
+  - [4.2 安装PaddleOCR（包含 PP-OCR 和 VQA）](#42-安装paddleocr包含-pp-ocr-和-vqa)
+- [5. 使用](#5-使用)
+  - [5.1 数据和预训练模型准备](#51-数据和预训练模型准备)
+  - [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）
 
@@ -122,13 +122,13 @@ python3 -m pip install -r ppstructure/vqa/requirements.txt
 
 * 下载处理好的数据集
 
-处理好的XFUND中文数据集下载地址：[https://paddleocr.bj.bcebos.com/dataset/XFUND.tar](https://paddleocr.bj.bcebos.com/dataset/XFUND.tar)。
+处理好的XFUND中文数据集下载地址：[链接](https://paddleocr.bj.bcebos.com/ppstructure/dataset/XFUND.tar)。
 
 
 下载并解压该数据集，解压后将数据集放置在当前目录下。
 
 ```shell
-wget https://paddleocr.bj.bcebos.com/dataset/XFUND.tar
+wget https://paddleocr.bj.bcebos.com/ppstructure/dataset/XFUND.tar
 ```
 
 * 转换数据集
@@ -183,16 +183,16 @@ 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 +200,24 @@ 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 +254,24 @@ 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/labels/labels_ser.txt

-QUESTION
-ANSWER
-HEADER

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.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 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
+paddlenlp>=2.2.1
\ No newline at end of file
+pypandoc
+attrdict
+python_docx
\ No newline at end of file

ppstructure/vqa/tools/trans_xfun_data.py

@@ -21,26 +21,22 @@ def transfer_xfun_data(json_path=None, output_file=None):
 
     json_info = json.loads(lines[0])
     documents = json_info["documents"]
-    label_info = {}
     with open(output_file, "w", encoding='utf-8') as fout:
         for idx, document in enumerate(documents):
+            label_info = []
             img_info = document["img"]
             document = document["document"]
             image_path = img_info["fname"]
 
-            label_info["height"] = img_info["height"]
-            label_info["width"] = img_info["width"]
-
-            label_info["ocr_info"] = []
-
             for doc in document:
-                label_info["ocr_info"].append({
-                    "text": doc["text"],
+                x1, y1, x2, y2 = doc["box"]
+                points = [[x1, y1], [x2, y1], [x2, y2], [x1, y2]]
+                label_info.append({
+                    "transcription": doc["text"],
                     "label": doc["label"],
-                    "bbox": doc["box"],
+                    "points": points,
                     "id": doc["id"],
-                    "linking": doc["linking"],
-                    "words": doc["words"]
+                    "linking": doc["linking"]
                 })
 
             fout.write(image_path + "\t" + json.dumps(

test_tipc/configs/det_r50_db++/train_infer_python.txt

+===========================train_params===========================
+model_name:det_r50_db++
+python:python3.7
+gpu_list:0|0,1
+Global.use_gpu:True|True
+Global.auto_cast:null
+Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=300
+Global.save_model_dir:./output/
+Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_whole_infer=4
+Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/
+null:null
+##
+trainer:norm_train
+norm_train:tools/train.py -c configs/det/det_r50_db++_ic15.yml -o Global.pretrained_model=./pretrain_models/ResNet50_dcn_asf_synthtext_pretrained
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:null
+null:null
+##
+===========================infer_params===========================
+Global.save_inference_dir:./output/
+Global.checkpoints:
+norm_export:tools/export_model.py -c configs/det/det_r50_db++_ic15.yml -o 
+quant_export:null
+fpgm_export:null
+distill_export:null
+export1:null
+export2:null
+inference_dir:null
+train_model:./inference/det_r50_db++_train/best_accuracy
+infer_export:tools/export_model.py -c configs/det/det_r50_db++_ic15.yml -o
+infer_quant:False
+inference:tools/infer/predict_det.py --det_algorithm="DB++"
+--use_gpu:True|False
+--enable_mkldnn:False
+--cpu_threads:6
+--rec_batch_num:1
+--use_tensorrt:False
+--precision:fp32
+--det_model_dir:
+--image_dir:./inference/ch_det_data_50/all-sum-510/
+null:null
+--benchmark:True
+null:null
+===========================infer_benchmark_params==========================
+random_infer_input:[{float32,[3,640,640]}];[{float32,[3,960,960]}]
+===========================train_benchmark_params==========================
+batch_size:8|16
+fp_items:fp32|fp16
+epoch:2
+--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
+flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096

test_tipc/configs/det_r50_vd_east_v2_0/train_infer_python.txt

 ===========================train_params===========================
 model_name:det_r50_vd_east_v2_0
 python:python3.7
-gpu_list:0
+gpu_list:0|0,1
 Global.use_gpu:True|True
 Global.auto_cast:fp32
 Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=500
 Global.save_model_dir:./output/
 Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_whole_infer=4
-Global.pretrained_model:null
+Global.pretrained_model:./pretrain_models/det_r50_vd_east_v2.0_train/best_accuracy
 train_model_name:latest
 train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/
 null:null

test_tipc/prepare.sh

@@ -59,6 +59,9 @@ if [ ${MODE} = "lite_train_lite_infer" ];then
         wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_rec_infer.tar --no-check-certificate
         cd ./inference/ && tar xf en_ppocr_mobile_v2.0_table_det_infer.tar && tar xf en_ppocr_mobile_v2.0_table_rec_infer.tar && cd ../
     fi
+    if [[ ${model_name} =~ "det_r50_db++" ]];then
+        wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/ResNet50_dcn_asf_synthtext_pretrained.pdparams --no-check-certificate
+    fi
     cd ./pretrain_models/ && tar xf det_mv3_db_v2.0_train.tar && cd ../
     rm -rf ./train_data/icdar2015
     rm -rf ./train_data/ic15_data
@@ -120,6 +123,10 @@ if [ ${MODE} = "lite_train_lite_infer" ];then
         wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_east_v2.0_train.tar --no-check-certificate
         cd ./pretrain_models/ && tar xf det_mv3_east_v2.0_train.tar && cd ../
     fi
+    if [ ${model_name} == "det_r50_vd_east_v2_0" ]; then
+        wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_east_v2.0_train.tar --no-check-certificate
+        cd ./pretrain_models/ && tar xf det_r50_vd_east_v2.0_train.tar && cd ../
+    fi
 
 elif [ ${MODE} = "whole_train_whole_infer" ];then
     wget -nc -P  ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams --no-check-certificate

test_tipc/test_ptq_inference_python.sh

@@ -139,7 +139,7 @@ if [ ${MODE} = "whole_infer" ]; then
             save_infer_dir="${infer_model}_klquant"
             set_export_weight=$(func_set_params "${export_weight}" "${infer_model}")
             set_save_infer_key=$(func_set_params "${save_infer_key}" "${save_infer_dir}")
-            export_log_path="${LOG_PATH}/_export_${Count}.log"
+            export_log_path="${LOG_PATH}_export_${Count}.log"
             export_cmd="${python} ${infer_run_exports[Count]} ${set_export_weight} ${set_save_infer_key} > ${export_log_path} 2>&1 "
             echo ${infer_run_exports[Count]} 
             echo $export_cmd

test_tipc/test_serving_infer_cpp.sh

@@ -87,8 +87,7 @@ function func_serving(){
     set_image_dir=$(func_set_params "${image_dir_key}" "${image_dir_value}")
     python_list=(${python_list})
     cd ${serving_dir_value}
-    unset https_proxy
-    unset http_proxy
+    
     # cpp serving
     for gpu_id in ${gpu_value[*]}; do
         if [ ${gpu_id} = "null" ]; then

test_tipc/test_serving_infer_python.sh

@@ -112,8 +112,7 @@ function func_serving(){
     
     cd ${serving_dir_value}
     python=${python_list[0]}
-    unset https_proxy
-    unset http_proxy
+    
     # python serving
     for use_gpu in ${web_use_gpu_list[*]}; do
         if [ ${use_gpu} = "null" ]; then

test_tipc/test_train_inference_python.sh

@@ -193,7 +193,7 @@ if [ ${MODE} = "whole_infer" ]; then
             save_infer_dir="${infer_model}"
             set_export_weight=$(func_set_params "${export_weight}" "${infer_model}")
             set_save_infer_key=$(func_set_params "${save_infer_key}" "${save_infer_dir}")
-            export_log_path="${LOG_PATH}/_export_${Count}.log"
+            export_log_path="${LOG_PATH}_export_${Count}.log"
             export_cmd="${python} ${infer_run_exports[Count]} ${set_export_weight} ${set_save_infer_key} > ${export_log_path} 2>&1 "
             echo ${infer_run_exports[Count]} 
             echo $export_cmd
@@ -295,6 +295,7 @@ else
                 fi
                 # run train
                 eval $cmd
+                eval "cat ${save_log}/train.log >> ${save_log}.log"
                 status_check $? "${cmd}" "${status_log}" "${model_name}"
 
                 set_eval_pretrain=$(func_set_params "${pretrain_model_key}" "${save_log}/${train_model_name}")

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/predict_det.py

@@ -67,6 +67,23 @@ class TextDetector(object):
             postprocess_params["unclip_ratio"] = args.det_db_unclip_ratio
             postprocess_params["use_dilation"] = args.use_dilation
             postprocess_params["score_mode"] = args.det_db_score_mode
+        elif self.det_algorithm == "DB++":
+            postprocess_params['name'] = 'DBPostProcess'
+            postprocess_params["thresh"] = args.det_db_thresh
+            postprocess_params["box_thresh"] = args.det_db_box_thresh
+            postprocess_params["max_candidates"] = 1000
+            postprocess_params["unclip_ratio"] = args.det_db_unclip_ratio
+            postprocess_params["use_dilation"] = args.use_dilation
+            postprocess_params["score_mode"] = args.det_db_score_mode
+            pre_process_list[1] = {
+                'NormalizeImage': {
+                    'std': [1.0, 1.0, 1.0],
+                    'mean':
+                    [0.48109378172549, 0.45752457890196, 0.40787054090196],
+                    'scale': '1./255.',
+                    'order': 'hwc'
+                }
+            }
         elif self.det_algorithm == "EAST":
             postprocess_params['name'] = 'EASTPostProcess'
             postprocess_params["score_thresh"] = args.det_east_score_thresh
@@ -231,7 +248,7 @@ class TextDetector(object):
             preds['f_score'] = outputs[1]
             preds['f_tco'] = outputs[2]
             preds['f_tvo'] = outputs[3]
-        elif self.det_algorithm in ['DB', 'PSE']:
+        elif self.det_algorithm in ['DB', 'PSE', 'DB++']:
             preds['maps'] = outputs[0]
         elif self.det_algorithm == 'FCE':
             for i, output in enumerate(outputs):

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_kie.py

@@ -39,13 +39,12 @@ import time
 
 
 def read_class_list(filepath):
-    dict = {}
+    ret = {}
     with open(filepath, "r") as f:
         lines = f.readlines()
-        for line in lines:
-            key, value = line.split(" ")
-            dict[key] = value.rstrip()
-    return dict
+        for idx, line in enumerate(lines):
+            ret[idx] = line.strip("\n")
+    return ret
 
 
 def draw_kie_result(batch, node, idx_to_cls, count):
@@ -71,7 +70,7 @@ def draw_kie_result(batch, node, idx_to_cls, count):
         x_min = int(min([point[0] for point in new_box]))
         y_min = int(min([point[1] for point in new_box]))
 
-        pred_label = str(node_pred_label[i])
+        pred_label = node_pred_label[i]
         if pred_label in idx_to_cls:
             pred_label = idx_to_cls[pred_label]
         pred_score = '{:.2f}'.format(node_pred_score[i])
@@ -109,8 +108,7 @@ def main():
     save_res_path = config['Global']['save_res_path']
     class_path = config['Global']['class_path']
     idx_to_cls = read_class_list(class_path)
-    if not os.path.exists(os.path.dirname(save_res_path)):
-        os.makedirs(os.path.dirname(save_res_path))
+    os.makedirs(os.path.dirname(save_res_path), exist_ok=True)
 
     model.eval()
 

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 +72,10 @@ 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 = []
@@ -80,29 +85,30 @@ 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'
                 ]
 
             transforms.append(op)
-        global_config['infer_mode'] = True
+        if config["Global"].get("infer_mode", None) is None:
+            global_config['infer_mode'] = True
         self.ops = create_operators(config['Eval']['dataset']['transforms'],
                                     global_config)
         self.model.eval()
 
-    def __call__(self, img_path):
-        with open(img_path, 'rb') as f:
+    def __call__(self, data):
+        with open(data["img_path"], 'rb') as f:
             img = f.read()
-            data = {'image': img}
+        data["image"] = img
         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])
+            preds, segment_offset_ids=batch[6], ocr_infos=batch[7])
         return post_result, batch
 
 
@@ -112,20 +118,33 @@ if __name__ == '__main__':
 
     ser_engine = SerPredictor(config)
 
-    infer_imgs = get_image_file_list(config['Global']['infer_img'])
+    if config["Global"].get("infer_mode", None) is False:
+        data_dir = config['Eval']['dataset']['data_dir']
+        with open(config['Global']['infer_img'], "rb") as f:
+            infer_imgs = f.readlines()
+    else:
+        infer_imgs = get_image_file_list(config['Global']['infer_img'])
+
     with open(
             os.path.join(config['Global']['save_res_path'],
                          "infer_results.txt"),
             "w",
             encoding='utf-8') as fout:
-        for idx, img_path in enumerate(infer_imgs):
+        for idx, info in enumerate(infer_imgs):
+            if config["Global"].get("infer_mode", None) is False:
+                data_line = info.decode('utf-8')
+                substr = data_line.strip("\n").split("\t")
+                img_path = os.path.join(data_dir, substr[0])
+                data = {'img_path': img_path, 'label': substr[1]}
+            else:
+                img_path = info
+                data = {'img_path': img_path}
+
             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(img_path)
+            result, _ = ser_engine(data)
             result = result[0]
             fout.write(img_path + "\t" + json.dumps(
                 {
@@ -133,3 +152,6 @@ 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))

tools/program.py

@@ -576,8 +576,8 @@ def preprocess(is_train=False):
     assert alg in [
         'EAST', 'DB', 'SAST', 'Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN',
         'CLS', 'PGNet', 'Distillation', 'NRTR', 'TableAttn', 'SAR', 'PSE',
-        'SEED', 'SDMGR', 'LayoutXLM', 'LayoutLM', 'PREN', 'FCE', 'SVTR',
-        'ViTSTR', 'ABINet'
+        'SEED', 'SDMGR', 'LayoutXLM', 'LayoutLM', 'LayoutLMv2', 'PREN', 'FCE',
+        'SVTR', 'ViTSTR', 'ABINet', 'DB++'
     ]
 
     if use_xpu: