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| # copyright (c) 2021 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 | |
| from paddle.nn import functional as F | |
| class TableAttentionLoss(nn.Layer): | |
| def __init__(self, structure_weight, loc_weight, **kwargs): | |
| super(TableAttentionLoss, self).__init__() | |
| self.loss_func = nn.CrossEntropyLoss(weight=None, reduction='none') | |
| self.structure_weight = structure_weight | |
| self.loc_weight = loc_weight | |
| def forward(self, predicts, batch): | |
| structure_probs = predicts['structure_probs'] | |
| structure_targets = batch[1].astype("int64") | |
| structure_targets = structure_targets[:, 1:] | |
| structure_probs = paddle.reshape(structure_probs, | |
| [-1, structure_probs.shape[-1]]) | |
| structure_targets = paddle.reshape(structure_targets, [-1]) | |
| structure_loss = self.loss_func(structure_probs, structure_targets) | |
| structure_loss = paddle.mean(structure_loss) * self.structure_weight | |
| loc_preds = predicts['loc_preds'] | |
| loc_targets = batch[2].astype("float32") | |
| loc_targets_mask = batch[3].astype("float32") | |
| loc_targets = loc_targets[:, 1:, :] | |
| loc_targets_mask = loc_targets_mask[:, 1:, :] | |
| loc_loss = F.mse_loss(loc_preds * loc_targets_mask, | |
| loc_targets) * self.loc_weight | |
| total_loss = structure_loss + loc_loss | |
| return { | |
| 'loss': total_loss, | |
| "structure_loss": structure_loss, | |
| "loc_loss": loc_loss | |
| } | |
| class SLALoss(nn.Layer): | |
| def __init__(self, structure_weight, loc_weight, loc_loss='mse', **kwargs): | |
| super(SLALoss, self).__init__() | |
| self.loss_func = nn.CrossEntropyLoss(weight=None, reduction='mean') | |
| self.structure_weight = structure_weight | |
| self.loc_weight = loc_weight | |
| self.loc_loss = loc_loss | |
| self.eps = 1e-12 | |
| def forward(self, predicts, batch): | |
| structure_probs = predicts['structure_probs'] | |
| structure_targets = batch[1].astype("int64") | |
| structure_targets = structure_targets[:, 1:] | |
| structure_loss = self.loss_func(structure_probs, structure_targets) | |
| structure_loss = paddle.mean(structure_loss) * self.structure_weight | |
| loc_preds = predicts['loc_preds'] | |
| loc_targets = batch[2].astype("float32") | |
| loc_targets_mask = batch[3].astype("float32") | |
| loc_targets = loc_targets[:, 1:, :] | |
| loc_targets_mask = loc_targets_mask[:, 1:, :] | |
| loc_loss = F.smooth_l1_loss( | |
| loc_preds * loc_targets_mask, | |
| loc_targets * loc_targets_mask, | |
| reduction='sum') * self.loc_weight | |
| loc_loss = loc_loss / (loc_targets_mask.sum() + self.eps) | |
| total_loss = structure_loss + loc_loss | |
| return { | |
| 'loss': total_loss, | |
| "structure_loss": structure_loss, | |
| "loc_loss": loc_loss | |
| } | |