add layoutlmv2

configs/vqa/re/layoutlmv2.yml

+Global:
+  use_gpu: True
+  epoch_num: &epoch_num 200
+  log_smooth_window: 10
+  print_batch_step: 10
+  save_model_dir: ./output/re_layoutlmv2/
+  save_epoch_step: 2000
+  # evaluation is run every 10 iterations after the 0th iteration
+  eval_batch_step: [ 0, 19 ]
+  cal_metric_during_train: False
+  save_inference_dir:
+  use_visualdl: False
+  seed: 2048
+  infer_img: doc/vqa/input/zh_val_21.jpg
+  save_res_path: ./output/re/
+
+Architecture:
+  model_type: vqa
+  algorithm: &algorithm "LayoutLMv2"
+  Transform:
+  Backbone:
+    name: LayoutLMv2ForRe
+    pretrained: True
+    checkpoints: 
+
+Loss:
+  name: LossFromOutput
+  key: loss
+  reduction: mean
+
+Optimizer:
+  name: AdamW
+  beta1: 0.9
+  beta2: 0.999
+  clip_norm: 10
+  lr:
+    name: Piecewise
+    values: [0.000005, 0.00005]
+    decay_epochs: [10]
+    warmup_epoch: 0
+  regularizer:
+    name: L2
+    factor: 0.00000
+    
+PostProcess:
+  name: VQAReTokenLayoutLMPostProcess
+
+Metric:
+  name: VQAReTokenMetric
+  main_indicator: hmean
+
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: train_data/XFUND/zh_train/image
+    label_file_list: 
+      - train_data/XFUND/zh_train/xfun_normalize_train.json
+    ratio_list: [ 1.0 ]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: RGB
+          channel_first: False
+      - VQATokenLabelEncode: # Class handling label
+          contains_re: True
+          algorithm: *algorithm
+          class_path: &class_path ppstructure/vqa/labels/labels_ser.txt
+      - VQATokenPad:
+          max_seq_len: &max_seq_len 512
+          return_attention_mask: True
+      - VQAReTokenRelation:
+      - VQAReTokenChunk:
+          max_seq_len: *max_seq_len
+      - Resize:
+          size: [224,224]
+      - NormalizeImage:
+          scale: 1./255.
+          mean: [0.485, 0.456, 0.406]
+          std: [0.229, 0.224, 0.225]
+          order: 'hwc'
+      - ToCHWImage:
+      - KeepKeys:
+          keep_keys: [ 'input_ids', 'bbox', 'image', 'attention_mask', 'token_type_ids','entities', 'relations'] # dataloader will return list in this order
+  loader:
+    shuffle: True
+    drop_last: False
+    batch_size_per_card: 8
+    num_workers: 8
+    collate_fn: ListCollator
+
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: train_data/XFUND/zh_val/image
+    label_file_list:
+      - train_data/XFUND/zh_val/xfun_normalize_val.json
+    transforms:
+      - DecodeImage: # load image
+          img_mode: RGB
+          channel_first: False
+      - VQATokenLabelEncode: # Class handling label
+          contains_re: True
+          algorithm: *algorithm
+          class_path: *class_path
+      - VQATokenPad:
+          max_seq_len: *max_seq_len
+          return_attention_mask: True
+      - VQAReTokenRelation:
+      - VQAReTokenChunk:
+          max_seq_len: *max_seq_len
+      - Resize:
+          size: [224,224]
+      - NormalizeImage:
+          scale: 1./255.
+          mean: [0.485, 0.456, 0.406]
+          std: [0.229, 0.224, 0.225]
+          order: 'hwc'
+      - ToCHWImage:
+      - KeepKeys:
+          keep_keys: [ 'input_ids', 'bbox', 'image', 'attention_mask', 'token_type_ids','entities', 'relations'] # dataloader will return list in this order
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 8
+    num_workers: 8
+    collate_fn: ListCollator

configs/vqa/re/layoutxlm.yml

@@ -21,7 +21,7 @@ Architecture:
   Backbone:
     name: LayoutXLMForRe
     pretrained: True
-    checkpoints: 
+    checkpoints:
 
 Loss:
   name: LossFromOutput
@@ -34,7 +34,10 @@ Optimizer:
   beta2: 0.999
   clip_norm: 10
   lr:
-    learning_rate: 0.00005
+    name: Piecewise
+    values: [0.000005, 0.00005]
+    decay_epochs: [10]
+    warmup_epoch: 0
   regularizer:
     name: L2
     factor: 0.00000
@@ -81,7 +84,7 @@ Train:
     shuffle: True
     drop_last: False
     batch_size_per_card: 8
-    num_workers: 4
+    num_workers: 8
     collate_fn: ListCollator
 
 Eval:
@@ -118,5 +121,5 @@ Eval:
     shuffle: False
     drop_last: False
     batch_size_per_card: 8
-    num_workers: 4
+    num_workers: 8
     collate_fn: ListCollator

configs/vqa/ser/layoutlmv2.yml

+Global:
+  use_gpu: True
+  epoch_num: &epoch_num 200
+  log_smooth_window: 10
+  print_batch_step: 10
+  save_model_dir: ./output/ser_layoutlmv2/
+  save_epoch_step: 2000
+  # evaluation is run every 10 iterations after the 0th iteration
+  eval_batch_step: [ 0, 19 ]
+  cal_metric_during_train: False
+  save_inference_dir:
+  use_visualdl: False
+  seed: 2022
+  infer_img: doc/vqa/input/zh_val_0.jpg
+  save_res_path: ./output/ser/
+
+Architecture:
+  model_type: vqa
+  algorithm: &algorithm "LayoutLMv2"
+  Transform:
+  Backbone:
+    name: LayoutLMv2ForSer
+    pretrained: True
+    checkpoints:
+    num_classes: &num_classes 7
+
+Loss:
+  name: VQASerTokenLayoutLMLoss
+  num_classes: *num_classes
+
+Optimizer:
+  name: AdamW
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: Linear
+    learning_rate: 0.00005
+    epochs: *epoch_num
+    warmup_epoch: 2
+  regularizer:
+  
+    name: L2
+    factor: 0.00000
+    
+PostProcess:
+  name: VQASerTokenLayoutLMPostProcess
+  class_path: &class_path ppstructure/vqa/labels/labels_ser.txt
+
+Metric:
+  name: VQASerTokenMetric
+  main_indicator: hmean
+
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: train_data/XFUND/zh_train/image
+    label_file_list: 
+      - train_data/XFUND/zh_train/xfun_normalize_train.json
+    transforms:
+      - DecodeImage: # load image
+          img_mode: RGB
+          channel_first: False
+      - VQATokenLabelEncode: # Class handling label
+          contains_re: False
+          algorithm: *algorithm
+          class_path: *class_path
+      - VQATokenPad:
+          max_seq_len: &max_seq_len 512
+          return_attention_mask: True
+      - VQASerTokenChunk:
+          max_seq_len: *max_seq_len
+      - Resize:
+          size: [224,224]
+      - NormalizeImage:
+          scale: 1
+          mean: [ 123.675, 116.28, 103.53 ]
+          std: [ 58.395, 57.12, 57.375 ]
+          order: 'hwc'
+      - ToCHWImage:
+      - KeepKeys:
+          keep_keys: [ 'input_ids','labels', 'bbox', 'image', 'attention_mask', 'token_type_ids'] # dataloader will return list in this order
+  loader:
+    shuffle: True
+    drop_last: False
+    batch_size_per_card: 8
+    num_workers: 4
+
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: train_data/XFUND/zh_val/image
+    label_file_list:
+      - train_data/XFUND/zh_val/xfun_normalize_val.json
+    transforms:
+      - DecodeImage: # load image
+          img_mode: RGB
+          channel_first: False
+      - VQATokenLabelEncode: # Class handling label
+          contains_re: False
+          algorithm: *algorithm
+          class_path: *class_path
+      - VQATokenPad:
+          max_seq_len: *max_seq_len
+          return_attention_mask: True
+      - VQASerTokenChunk:
+          max_seq_len: *max_seq_len
+      - Resize:
+          size: [224,224]
+      - NormalizeImage:
+          scale: 1
+          mean: [ 123.675, 116.28, 103.53 ]
+          std: [ 58.395, 57.12, 57.375 ]
+          order: 'hwc'
+      - ToCHWImage:
+      - KeepKeys:
+          keep_keys: [ 'input_ids', 'labels', 'bbox', 'image', 'attention_mask', 'token_type_ids'] # dataloader will return list in this order
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 8
+    num_workers: 4

ppocr/data/imaug/label_ops.py

@@ -799,7 +799,7 @@ class VQATokenLabelEncode(object):
                  ocr_engine=None,
                  **kwargs):
         super(VQATokenLabelEncode, self).__init__()
-        from paddlenlp.transformers import LayoutXLMTokenizer, LayoutLMTokenizer
+        from paddlenlp.transformers import LayoutXLMTokenizer, LayoutLMTokenizer, LayoutLMv2Tokenizer
         from ppocr.utils.utility import load_vqa_bio_label_maps
         tokenizer_dict = {
             'LayoutXLM': {
@@ -809,6 +809,10 @@ class VQATokenLabelEncode(object):
             'LayoutLM': {
                 'class': LayoutLMTokenizer,
                 'pretrained_model': 'layoutlm-base-uncased'
+            },
+            'LayoutLMv2': {
+                'class': LayoutLMv2Tokenizer,
+                'pretrained_model': 'layoutlmv2-base-uncased'
             }
         }
         self.contains_re = contains_re

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

@@ -12,6 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from collections import defaultdict
+
 
 class VQASerTokenChunk(object):
     def __init__(self, max_seq_len=512, infer_mode=False, **kwargs):
@@ -39,6 +41,8 @@ class VQASerTokenChunk(object):
                     encoded_inputs_example[key] = data[key]
 
             encoded_inputs_all.append(encoded_inputs_example)
+        if len(encoded_inputs_all) == 0:
+            return None
         return encoded_inputs_all[0]
 
 
@@ -101,17 +105,18 @@ class VQAReTokenChunk(object):
                 "entities": self.reformat(entities_in_this_span),
                 "relations": self.reformat(relations_in_this_span),
             })
-            item['entities']['label'] = [
-                self.entities_labels[x] for x in item['entities']['label']
-            ]
-            encoded_inputs_all.append(item)
+            if len(item['entities']) > 0:
+                item['entities']['label'] = [
+                    self.entities_labels[x] for x in item['entities']['label']
+                ]
+                encoded_inputs_all.append(item)
+        if len(encoded_inputs_all) == 0:
+            return None
         return encoded_inputs_all[0]
 
     def reformat(self, data):
-        new_data = {}
+        new_data = defaultdict(list)
         for item in data:
             for k, v in item.items():
-                if k not in new_data:
-                    new_data[k] = []
                 new_data[k].append(v)
         return new_data

ppocr/modeling/backbones/__init__.py

@@ -45,8 +45,11 @@ def build_backbone(config, model_type):
         from .table_mobilenet_v3 import MobileNetV3
         support_dict = ["ResNet", "MobileNetV3"]
     elif model_type == 'vqa':
-        from .vqa_layoutlm import LayoutLMForSer, LayoutXLMForSer, LayoutXLMForRe
-        support_dict = ["LayoutLMForSer", "LayoutXLMForSer", 'LayoutXLMForRe']
+        from .vqa_layoutlm import LayoutLMForSer, LayoutLMv2ForSer, LayoutLMv2ForRe, LayoutXLMForSer, LayoutXLMForRe
+        support_dict = [
+            "LayoutLMForSer", "LayoutLMv2ForSer", 'LayoutLMv2ForRe',
+            "LayoutXLMForSer", 'LayoutXLMForRe'
+        ]
     else:
         raise NotImplementedError
 

ppocr/modeling/backbones/vqa_layoutlm.py

@@ -21,12 +21,14 @@ from paddle import nn
 
 from paddlenlp.transformers import LayoutXLMModel, LayoutXLMForTokenClassification, LayoutXLMForRelationExtraction
 from paddlenlp.transformers import LayoutLMModel, LayoutLMForTokenClassification
+from paddlenlp.transformers import LayoutLMv2Model, LayoutLMv2ForTokenClassification, LayoutLMv2ForRelationExtraction
 
 __all__ = ["LayoutXLMForSer", 'LayoutLMForSer']
 
 pretrained_model_dict = {
     LayoutXLMModel: 'layoutxlm-base-uncased',
-    LayoutLMModel: 'layoutlm-base-uncased'
+    LayoutLMModel: 'layoutlm-base-uncased',
+    LayoutLMv2Model: 'layoutlmv2-base-uncased'
 }
 
 
@@ -58,12 +60,34 @@ class NLPBaseModel(nn.Layer):
         self.out_channels = 1
 
 
-class LayoutXLMForSer(NLPBaseModel):
+class LayoutLMForSer(NLPBaseModel):
     def __init__(self, num_classes, pretrained=True, checkpoints=None,
                  **kwargs):
-        super(LayoutXLMForSer, self).__init__(
-            LayoutXLMModel,
-            LayoutXLMForTokenClassification,
+        super(LayoutLMForSer, self).__init__(
+            LayoutLMModel,
+            LayoutLMForTokenClassification,
+            'ser',
+            pretrained,
+            checkpoints,
+            num_classes=num_classes)
+
+    def forward(self, x):
+        x = self.model(
+            input_ids=x[0],
+            bbox=x[2],
+            attention_mask=x[4],
+            token_type_ids=x[5],
+            position_ids=None,
+            output_hidden_states=False)
+        return x
+
+
+class LayoutLMv2ForSer(NLPBaseModel):
+    def __init__(self, num_classes, pretrained=True, checkpoints=None,
+                 **kwargs):
+        super(LayoutLMv2ForSer, self).__init__(
+            LayoutLMv2Model,
+            LayoutLMv2ForTokenClassification,
             'ser',
             pretrained,
             checkpoints,
@@ -82,12 +106,12 @@ class LayoutXLMForSer(NLPBaseModel):
         return x[0]
 
 
-class LayoutLMForSer(NLPBaseModel):
+class LayoutXLMForSer(NLPBaseModel):
     def __init__(self, num_classes, pretrained=True, checkpoints=None,
                  **kwargs):
-        super(LayoutLMForSer, self).__init__(
-            LayoutLMModel,
-            LayoutLMForTokenClassification,
+        super(LayoutXLMForSer, self).__init__(
+            LayoutXLMModel,
+            LayoutXLMForTokenClassification,
             'ser',
             pretrained,
             checkpoints,
@@ -97,10 +121,33 @@ class LayoutLMForSer(NLPBaseModel):
         x = self.model(
             input_ids=x[0],
             bbox=x[2],
+            image=x[3],
             attention_mask=x[4],
             token_type_ids=x[5],
             position_ids=None,
-            output_hidden_states=False)
+            head_mask=None,
+            labels=None)
+        return x[0]
+
+
+class LayoutLMv2ForRe(NLPBaseModel):
+    def __init__(self, pretrained=True, checkpoints=None, **kwargs):
+        super(LayoutLMv2ForRe, self).__init__(LayoutLMv2Model,
+                                              LayoutLMv2ForRelationExtraction,
+                                              're', pretrained, checkpoints)
+
+    def forward(self, x):
+        x = self.model(
+            input_ids=x[0],
+            bbox=x[1],
+            labels=None,
+            image=x[2],
+            attention_mask=x[3],
+            token_type_ids=x[4],
+            position_ids=None,
+            head_mask=None,
+            entities=x[5],
+            relations=x[6])
         return x
 
 

ppstructure/vqa/README.md

@@ -24,6 +24,8 @@ PP-Structure 里的 DOC-VQA算法基于PaddleNLP自然语言处理算法库进
 |:---:|:---:|:---:| :---:|
 | LayoutXLM | RE | 0.7483 | [链接](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutXLM_xfun_zh.tar) |
 | LayoutXLM | SER | 0.9038 | [链接](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutXLM_xfun_zh.tar) |
+| LayoutLMv2 | RE | 0.6777 | [链接](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutLMv2_xfun_zh.tar) |
+| LayoutLMv2 | SER | 0.8544 | [链接](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLMv2_xfun_zh.tar) |
 | LayoutLM | SER | 0.7731 | [链接](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLM_xfun_zh.tar) |