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