det_db_loss.py

# 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([0.0])
            cbn_loss = paddle.to_tensor([0.0])

        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