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# 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}
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