det_db_loss.py 2.7 KB
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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");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
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#
#    http://www.apache.org/licenses/LICENSE-2.0
#
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# 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.
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

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from paddle import nn

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from .det_basic_loss import BalanceLoss, MaskL1Loss, DiceLoss


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class DBLoss(nn.Layer):
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    """
    Differentiable Binarization (DB) Loss Function
    args:
        param (dict): the super paramter for DB Loss
    """

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    def __init__(self,
                 balance_loss=True,
                 main_loss_type='DiceLoss',
                 alpha=5,
                 beta=10,
                 ohem_ratio=3,
                 eps=1e-6,
                 **kwargs):
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        super(DBLoss, self).__init__()
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        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)
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    def forward(self, predicts, labels):
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        predicts = predicts['maps']
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        label_threshold_map, label_threshold_mask, label_shrink_map, label_shrink_mask = labels[
            1:]
        shrink_maps = predicts[:, 0, :, :]
        threshold_maps = predicts[:, 1, :, :]
        binary_maps = predicts[:, 2, :, :]
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        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)
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        loss_shrink_maps = self.alpha * loss_shrink_maps
        loss_threshold_maps = self.beta * loss_threshold_maps

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        loss_all = loss_shrink_maps + loss_threshold_maps \
                   + loss_binary_maps
        losses = {'loss': loss_all, \
                  "loss_shrink_maps": loss_shrink_maps, \
                  "loss_threshold_maps": loss_threshold_maps, \
                  "loss_binary_maps": loss_binary_maps}
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        return losses