det_east_loss.py

#copyright (c) 2020 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.fluid as fluid


class EASTLoss(object):
    """
    EAST Loss function
    """

    def __init__(self, params=None):
        super(EASTLoss, self).__init__()

    def __call__(self, predicts, labels):
        f_score = predicts['f_score']
        f_geo = predicts['f_geo']
        l_score = labels['score']
        l_geo = labels['geo']
        l_mask = labels['mask']
        ##dice_loss
        intersection = fluid.layers.reduce_sum(f_score * l_score * l_mask)
        union = fluid.layers.reduce_sum(f_score * l_mask)\
            + fluid.layers.reduce_sum(l_score * l_mask)
        dice_loss = 1 - 2 * intersection / (union + 1e-5)
        #smoooth_l1_loss
        channels = 8
        l_geo_split = fluid.layers.split(
            l_geo, num_or_sections=channels + 1, dim=1)
        f_geo_split = fluid.layers.split(f_geo, num_or_sections=channels, dim=1)
        smooth_l1 = 0
        for i in range(0, channels):
            geo_diff = l_geo_split[i] - f_geo_split[i]
            abs_geo_diff = fluid.layers.abs(geo_diff)
            smooth_l1_sign = fluid.layers.less_than(abs_geo_diff, l_score)
            smooth_l1_sign = fluid.layers.cast(smooth_l1_sign, dtype='float32')
            in_loss = abs_geo_diff * abs_geo_diff * smooth_l1_sign + \
                (abs_geo_diff - 0.5) * (1.0 - smooth_l1_sign)
            out_loss = l_geo_split[-1] / channels * in_loss * l_score
            smooth_l1 += out_loss
        smooth_l1_loss = fluid.layers.reduce_mean(smooth_l1 * l_score)
        dice_loss = dice_loss * 0.01
        total_loss = dice_loss + smooth_l1_loss
        losses = {'total_loss':total_loss, "dice_loss":dice_loss,\
            "smooth_l1_loss":smooth_l1_loss}
        return losses