| 1234567891011121314151617181920212223242526272829303132333435363738394041424344454647 |
- # copyright (c) 2020 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 paddle
- from paddle import nn
- class SRNLoss(nn.Layer):
- def __init__(self, **kwargs):
- super(SRNLoss, self).__init__()
- self.loss_func = paddle.nn.loss.CrossEntropyLoss(reduction="sum")
- def forward(self, predicts, batch):
- predict = predicts['predict']
- word_predict = predicts['word_out']
- gsrm_predict = predicts['gsrm_out']
- label = batch[1]
- casted_label = paddle.cast(x=label, dtype='int64')
- casted_label = paddle.reshape(x=casted_label, shape=[-1, 1])
- cost_word = self.loss_func(word_predict, label=casted_label)
- cost_gsrm = self.loss_func(gsrm_predict, label=casted_label)
- cost_vsfd = self.loss_func(predict, label=casted_label)
- cost_word = paddle.reshape(x=paddle.sum(cost_word), shape=[1])
- cost_gsrm = paddle.reshape(x=paddle.sum(cost_gsrm), shape=[1])
- cost_vsfd = paddle.reshape(x=paddle.sum(cost_vsfd), shape=[1])
- sum_cost = cost_word * 3.0 + cost_vsfd + cost_gsrm * 0.15
- return {'loss': sum_cost, 'word_loss': cost_word, 'img_loss': cost_vsfd}
|
