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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} |