59 lines
2.2 KiB
Python
59 lines
2.2 KiB
Python
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#copyright (c) 2019 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 math
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import paddle
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import paddle.fluid as fluid
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class SRNLoss(object):
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def __init__(self, params):
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super(SRNLoss, self).__init__()
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self.char_num = params['char_num']
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def __call__(self, predicts, others):
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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 = others['label']
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lbl_weight = others['lbl_weight']
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casted_label = fluid.layers.cast(x=label, dtype='int64')
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cost_word = fluid.layers.cross_entropy(input=word_predict, label=casted_label)
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cost_gsrm = fluid.layers.cross_entropy(input=gsrm_predict, label=casted_label)
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cost_vsfd = fluid.layers.cross_entropy(input=predict, label=casted_label)
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#cost_word = cost_word * lbl_weight
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#cost_gsrm = cost_gsrm * lbl_weight
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#cost_vsfd = cost_vsfd * lbl_weight
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cost_word = fluid.layers.reshape(x=fluid.layers.reduce_sum(cost_word), shape=[1])
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cost_gsrm = fluid.layers.reshape(x=fluid.layers.reduce_sum(cost_gsrm), shape=[1])
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cost_vsfd = fluid.layers.reshape(x=fluid.layers.reduce_sum(cost_vsfd), shape=[1])
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sum_cost = fluid.layers.sum([cost_word, cost_vsfd * 2.0, cost_gsrm * 0.15])
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#sum_cost = fluid.layers.sum([cost_word * 3.0, cost_vsfd, cost_gsrm * 0.15])
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#sum_cost = cost_word
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#fluid.layers.Print(cost_word,message="word_cost")
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#fluid.layers.Print(cost_vsfd,message="img_cost")
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return [sum_cost,cost_vsfd,cost_word]
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#return [sum_cost, cost_vsfd, cost_word]
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