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