init

backend/ppocr/modeling/backbones/vqa_layoutlm.py

@@ -0,0 +1,172 @@
+# copyright (c) 2021 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 os
+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',
+    LayoutLMv2Model: 'layoutlmv2-base-uncased'
+}
+
+
+class NLPBaseModel(nn.Layer):
+    def __init__(self,
+                 base_model_class,
+                 model_class,
+                 type='ser',
+                 pretrained=True,
+                 checkpoints=None,
+                 **kwargs):
+        super(NLPBaseModel, self).__init__()
+        if checkpoints is not None:
+            self.model = model_class.from_pretrained(checkpoints)
+        else:
+            pretrained_model_name = pretrained_model_dict[base_model_class]
+            if pretrained:
+                base_model = base_model_class.from_pretrained(
+                    pretrained_model_name)
+            else:
+                base_model = base_model_class(
+                    **base_model_class.pretrained_init_configuration[
+                        pretrained_model_name])
+            if type == 'ser':
+                self.model = model_class(
+                    base_model, num_classes=kwargs['num_classes'], dropout=None)
+            else:
+                self.model = model_class(base_model, dropout=None)
+        self.out_channels = 1
+
+
+class LayoutLMForSer(NLPBaseModel):
+    def __init__(self, num_classes, pretrained=True, checkpoints=None,
+                 **kwargs):
+        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,
+            num_classes=num_classes)
+
+    def forward(self, x):
+        x = self.model(
+            input_ids=x[0],
+            bbox=x[2],
+            image=x[3],
+            attention_mask=x[4],
+            token_type_ids=x[5],
+            position_ids=None,
+            head_mask=None,
+            labels=None)
+        return x[0]
+
+
+class LayoutXLMForSer(NLPBaseModel):
+    def __init__(self, num_classes, pretrained=True, checkpoints=None,
+                 **kwargs):
+        super(LayoutXLMForSer, self).__init__(
+            LayoutXLMModel,
+            LayoutXLMForTokenClassification,
+            'ser',
+            pretrained,
+            checkpoints,
+            num_classes=num_classes)
+
+    def forward(self, x):
+        x = self.model(
+            input_ids=x[0],
+            bbox=x[2],
+            image=x[3],
+            attention_mask=x[4],
+            token_type_ids=x[5],
+            position_ids=None,
+            head_mask=None,
+            labels=None)
+        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
+
+
+class LayoutXLMForRe(NLPBaseModel):
+    def __init__(self, pretrained=True, checkpoints=None, **kwargs):
+        super(LayoutXLMForRe, self).__init__(LayoutXLMModel,
+                                             LayoutXLMForRelationExtraction,
+                                             '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