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ppocr/losses/vqa_token_layoutlm_loss.py

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+# 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
+
+from paddle import nn
+from ppocr.losses.basic_loss import DMLLoss
+
+
+class VQASerTokenLayoutLMLoss(nn.Layer):
+    def __init__(self, num_classes, key=None):
+        super().__init__()
+        self.loss_class = nn.CrossEntropyLoss()
+        self.num_classes = num_classes
+        self.ignore_index = self.loss_class.ignore_index
+        self.key = key
+
+    def forward(self, predicts, batch):
+        if isinstance(predicts, dict) and self.key is not None:
+            predicts = predicts[self.key]
+        labels = batch[5]
+        attention_mask = batch[2]
+        if attention_mask is not None:
+            active_loss = (
+                attention_mask.reshape(
+                    [
+                        -1,
+                    ]
+                )
+                == 1
+            )
+            active_output = predicts.reshape([-1, self.num_classes])[active_loss]
+            active_label = labels.reshape(
+                [
+                    -1,
+                ]
+            )[active_loss]
+            loss = self.loss_class(active_output, active_label)
+        else:
+            loss = self.loss_class(
+                predicts.reshape([-1, self.num_classes]),
+                labels.reshape(
+                    [
+                        -1,
+                    ]
+                ),
+            )
+        return {"loss": loss}