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63 lines
2.2 KiB
Python
63 lines
2.2 KiB
Python
# 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");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import paddle
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from paddle import nn
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from .det_basic_loss import DiceLoss
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class EASTLoss(nn.Layer):
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""" """
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def __init__(self, eps=1e-6, **kwargs):
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super(EASTLoss, self).__init__()
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self.dice_loss = DiceLoss(eps=eps)
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def forward(self, predicts, labels):
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l_score, l_geo, l_mask = labels[1:]
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f_score = predicts["f_score"]
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f_geo = predicts["f_geo"]
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dice_loss = self.dice_loss(f_score, l_score, l_mask)
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# smoooth_l1_loss
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channels = 8
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l_geo_split = paddle.split(l_geo, num_or_sections=channels + 1, axis=1)
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f_geo_split = paddle.split(f_geo, num_or_sections=channels, axis=1)
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smooth_l1 = 0
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for i in range(0, channels):
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geo_diff = l_geo_split[i] - f_geo_split[i]
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abs_geo_diff = paddle.abs(geo_diff)
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smooth_l1_sign = paddle.less_than(abs_geo_diff, l_score)
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smooth_l1_sign = paddle.cast(smooth_l1_sign, dtype="float32")
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in_loss = abs_geo_diff * abs_geo_diff * smooth_l1_sign + (
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abs_geo_diff - 0.5
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) * (1.0 - smooth_l1_sign)
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out_loss = l_geo_split[-1] / channels * in_loss * l_score
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smooth_l1 += out_loss
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smooth_l1_loss = paddle.mean(smooth_l1 * l_score)
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dice_loss = dice_loss * 0.01
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total_loss = dice_loss + smooth_l1_loss
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losses = {
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"loss": total_loss,
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"dice_loss": dice_loss,
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"smooth_l1_loss": smooth_l1_loss,
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}
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return losses
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