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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. | |
| 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 | |