ppocr/losses/det_east_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.

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 DiceLoss


class EASTLoss(nn.Layer):
    """
    """

    def __init__(self,
                 eps=1e-6,
                 **kwargs):
        super(EASTLoss, self).__init__()
        self.dice_loss = DiceLoss(eps=eps)

    def forward(self, predicts, labels):
        l_score, l_geo, l_mask = labels[1:]
        f_score = predicts['f_score']
        f_geo = predicts['f_geo']

        dice_loss = self.dice_loss(f_score, l_score, l_mask)

        #smoooth_l1_loss
        channels = 8
        l_geo_split = paddle.split(
            l_geo, num_or_sections=channels + 1, axis=1)
        f_geo_split = paddle.split(f_geo, num_or_sections=channels, axis=1)
        smooth_l1 = 0
        for i in range(0, channels):
            geo_diff = l_geo_split[i] - f_geo_split[i]
            abs_geo_diff = paddle.abs(geo_diff)
            smooth_l1_sign = paddle.less_than(abs_geo_diff, l_score)
            smooth_l1_sign = paddle.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 = paddle.mean(smooth_l1 * l_score)

        dice_loss = dice_loss * 0.01
        total_loss = dice_loss + smooth_l1_loss
        losses = {"loss":total_loss, \
                  "dice_loss":dice_loss,\
                  "smooth_l1_loss":smooth_l1_loss}
        return losses