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| # 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. | |
| """ | |
| This code is refer from: | |
| https://github.com/WenmuZhou/DBNet.pytorch/blob/master/models/losses/DB_loss.py | |
| """ | |
| from __future__ import absolute_import | |
| from __future__ import division | |
| from __future__ import print_function | |
| import paddle | |
| from paddle import nn | |
| from .det_basic_loss import BalanceLoss, MaskL1Loss, DiceLoss | |
| class DBLoss(nn.Layer): | |
| """ | |
| Differentiable Binarization (DB) Loss Function | |
| args: | |
| param (dict): the super paramter for DB Loss | |
| """ | |
| def __init__(self, | |
| balance_loss=True, | |
| main_loss_type='DiceLoss', | |
| alpha=5, | |
| beta=10, | |
| ohem_ratio=3, | |
| eps=1e-6, | |
| **kwargs): | |
| super(DBLoss, self).__init__() | |
| self.alpha = alpha | |
| self.beta = beta | |
| self.dice_loss = DiceLoss(eps=eps) | |
| self.l1_loss = MaskL1Loss(eps=eps) | |
| self.bce_loss = BalanceLoss( | |
| balance_loss=balance_loss, | |
| main_loss_type=main_loss_type, | |
| negative_ratio=ohem_ratio) | |
| def forward(self, predicts, labels): | |
| predict_maps = predicts['maps'] | |
| label_threshold_map, label_threshold_mask, label_shrink_map, label_shrink_mask = labels[ | |
| 1:] | |
| shrink_maps = predict_maps[:, 0, :, :] | |
| threshold_maps = predict_maps[:, 1, :, :] | |
| binary_maps = predict_maps[:, 2, :, :] | |
| loss_shrink_maps = self.bce_loss(shrink_maps, label_shrink_map, | |
| label_shrink_mask) | |
| loss_threshold_maps = self.l1_loss(threshold_maps, label_threshold_map, | |
| label_threshold_mask) | |
| loss_binary_maps = self.dice_loss(binary_maps, label_shrink_map, | |
| label_shrink_mask) | |
| loss_shrink_maps = self.alpha * loss_shrink_maps | |
| loss_threshold_maps = self.beta * loss_threshold_maps | |
| # CBN loss | |
| if 'distance_maps' in predicts.keys(): | |
| distance_maps = predicts['distance_maps'] | |
| cbn_maps = predicts['cbn_maps'] | |
| cbn_loss = self.bce_loss(cbn_maps[:, 0, :, :], label_shrink_map, | |
| label_shrink_mask) | |
| else: | |
| dis_loss = paddle.to_tensor([0.]) | |
| cbn_loss = paddle.to_tensor([0.]) | |
| loss_all = loss_shrink_maps + loss_threshold_maps \ | |
| + loss_binary_maps | |
| losses = {'loss': loss_all+ cbn_loss, \ | |
| "loss_shrink_maps": loss_shrink_maps, \ | |
| "loss_threshold_maps": loss_threshold_maps, \ | |
| "loss_binary_maps": loss_binary_maps, \ | |
| "loss_cbn": cbn_loss} | |
| return losses | |