43 lines
1.6 KiB
Python
43 lines
1.6 KiB
Python
# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from paddle import nn
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class VQASerTokenLayoutLMLoss(nn.Layer):
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def __init__(self, num_classes):
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super().__init__()
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self.loss_class = nn.CrossEntropyLoss()
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self.num_classes = num_classes
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self.ignore_index = self.loss_class.ignore_index
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def forward(self, predicts, batch):
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labels = batch[1]
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attention_mask = batch[4]
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if attention_mask is not None:
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active_loss = attention_mask.reshape([-1, ]) == 1
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active_outputs = predicts.reshape(
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[-1, self.num_classes])[active_loss]
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active_labels = labels.reshape([-1, ])[active_loss]
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loss = self.loss_class(active_outputs, active_labels)
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else:
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loss = self.loss_class(
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predicts.reshape([-1, self.num_classes]),
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labels.reshape([-1, ]))
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return {'loss': loss}
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