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| # copyright (c) 2022 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. | |
| """ | |
| This code is refer from: | |
| https://github.com/wangyuxin87/VisionLAN | |
| """ | |
| from __future__ import absolute_import | |
| from __future__ import division | |
| from __future__ import print_function | |
| import paddle | |
| from paddle import nn | |
| class VLLoss(nn.Layer): | |
| def __init__(self, mode='LF_1', weight_res=0.5, weight_mas=0.5, **kwargs): | |
| super(VLLoss, self).__init__() | |
| self.loss_func = paddle.nn.loss.CrossEntropyLoss(reduction="mean") | |
| assert mode in ['LF_1', 'LF_2', 'LA'] | |
| self.mode = mode | |
| self.weight_res = weight_res | |
| self.weight_mas = weight_mas | |
| def flatten_label(self, target): | |
| label_flatten = [] | |
| label_length = [] | |
| for i in range(0, target.shape[0]): | |
| cur_label = target[i].tolist() | |
| label_flatten += cur_label[:cur_label.index(0) + 1] | |
| label_length.append(cur_label.index(0) + 1) | |
| label_flatten = paddle.to_tensor(label_flatten, dtype='int64') | |
| label_length = paddle.to_tensor(label_length, dtype='int32') | |
| return (label_flatten, label_length) | |
| def _flatten(self, sources, lengths): | |
| return paddle.concat([t[:l] for t, l in zip(sources, lengths)]) | |
| def forward(self, predicts, batch): | |
| text_pre = predicts[0] | |
| target = batch[1].astype('int64') | |
| label_flatten, length = self.flatten_label(target) | |
| text_pre = self._flatten(text_pre, length) | |
| if self.mode == 'LF_1': | |
| loss = self.loss_func(text_pre, label_flatten) | |
| else: | |
| text_rem = predicts[1] | |
| text_mas = predicts[2] | |
| target_res = batch[2].astype('int64') | |
| target_sub = batch[3].astype('int64') | |
| label_flatten_res, length_res = self.flatten_label(target_res) | |
| label_flatten_sub, length_sub = self.flatten_label(target_sub) | |
| text_rem = self._flatten(text_rem, length_res) | |
| text_mas = self._flatten(text_mas, length_sub) | |
| loss_ori = self.loss_func(text_pre, label_flatten) | |
| loss_res = self.loss_func(text_rem, label_flatten_res) | |
| loss_mas = self.loss_func(text_mas, label_flatten_sub) | |
| loss = loss_ori + loss_res * self.weight_res + loss_mas * self.weight_mas | |
| return {'loss': loss} | |