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100 lines
3.2 KiB
Python
100 lines
3.2 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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"""
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This code is refer from:
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https://github.com/WenmuZhou/DBNet.pytorch/blob/master/models/losses/DB_loss.py
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"""
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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 BalanceLoss, MaskL1Loss, DiceLoss
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class DBLoss(nn.Layer):
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"""
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Differentiable Binarization (DB) Loss Function
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args:
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param (dict): the super paramter for DB Loss
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"""
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def __init__(
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self,
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balance_loss=True,
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main_loss_type="DiceLoss",
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alpha=5,
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beta=10,
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ohem_ratio=3,
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eps=1e-6,
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**kwargs,
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):
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super(DBLoss, self).__init__()
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self.alpha = alpha
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self.beta = beta
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self.dice_loss = DiceLoss(eps=eps)
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self.l1_loss = MaskL1Loss(eps=eps)
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self.bce_loss = BalanceLoss(
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balance_loss=balance_loss,
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main_loss_type=main_loss_type,
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negative_ratio=ohem_ratio,
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)
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def forward(self, predicts, labels):
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predict_maps = predicts["maps"]
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(
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label_threshold_map,
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label_threshold_mask,
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label_shrink_map,
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label_shrink_mask,
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) = labels[1:]
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shrink_maps = predict_maps[:, 0, :, :]
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threshold_maps = predict_maps[:, 1, :, :]
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binary_maps = predict_maps[:, 2, :, :]
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loss_shrink_maps = self.bce_loss(
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shrink_maps, label_shrink_map, label_shrink_mask
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)
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loss_threshold_maps = self.l1_loss(
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threshold_maps, label_threshold_map, label_threshold_mask
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)
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loss_binary_maps = self.dice_loss(
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binary_maps, label_shrink_map, label_shrink_mask
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)
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loss_shrink_maps = self.alpha * loss_shrink_maps
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loss_threshold_maps = self.beta * loss_threshold_maps
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# CBN loss
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if "distance_maps" in predicts.keys():
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distance_maps = predicts["distance_maps"]
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cbn_maps = predicts["cbn_maps"]
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cbn_loss = self.bce_loss(
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cbn_maps[:, 0, :, :], label_shrink_map, label_shrink_mask
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)
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else:
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dis_loss = paddle.to_tensor([0.0])
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cbn_loss = paddle.to_tensor([0.0])
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loss_all = loss_shrink_maps + loss_threshold_maps + loss_binary_maps
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losses = {
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"loss": loss_all + cbn_loss,
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"loss_shrink_maps": loss_shrink_maps,
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"loss_threshold_maps": loss_threshold_maps,
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"loss_binary_maps": loss_binary_maps,
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"loss_cbn": cbn_loss,
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}
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return losses
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