62 lines
2.4 KiB
Python
62 lines
2.4 KiB
Python
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#copyright (c) 2020 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.fluid as fluid
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class EASTLoss(object):
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"""
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EAST Loss function
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"""
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def __init__(self, params=None):
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super(EASTLoss, self).__init__()
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def __call__(self, predicts, labels):
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f_score = predicts['f_score']
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f_geo = predicts['f_geo']
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l_score = labels['score']
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l_geo = labels['geo']
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l_mask = labels['mask']
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##dice_loss
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intersection = fluid.layers.reduce_sum(f_score * l_score * l_mask)
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union = fluid.layers.reduce_sum(f_score * l_mask)\
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+ fluid.layers.reduce_sum(l_score * l_mask)
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dice_loss = 1 - 2 * intersection / (union + 1e-5)
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#smoooth_l1_loss
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channels = 8
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l_geo_split = fluid.layers.split(
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l_geo, num_or_sections=channels + 1, dim=1)
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f_geo_split = fluid.layers.split(f_geo, num_or_sections=channels, dim=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 = fluid.layers.abs(geo_diff)
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smooth_l1_sign = fluid.layers.less_than(abs_geo_diff, l_score)
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smooth_l1_sign = fluid.layers.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) * (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 = fluid.layers.reduce_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 = {'total_loss':total_loss, "dice_loss":dice_loss,\
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"smooth_l1_loss":smooth_l1_loss}
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return losses
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