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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.
"""
This code is refer from:
https://github.com/WenmuZhou/DBNet.pytorch/blob/master/models/losses/basic_loss.py
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import paddle
from paddle import nn
import paddle.nn.functional as F
class BalanceLoss(nn.Layer):
def __init__(self,
balance_loss=True,
main_loss_type='DiceLoss',
negative_ratio=3,
return_origin=False,
eps=1e-6,
**kwargs):
"""
The BalanceLoss for Differentiable Binarization text detection
args:
balance_loss (bool): whether balance loss or not, default is True
main_loss_type (str): can only be one of ['CrossEntropy','DiceLoss',
'Euclidean','BCELoss', 'MaskL1Loss'], default is 'DiceLoss'.
negative_ratio (int|float): float, default is 3.
return_origin (bool): whether return unbalanced loss or not, default is False.
eps (float): default is 1e-6.
"""
super(BalanceLoss, self).__init__()
self.balance_loss = balance_loss
self.main_loss_type = main_loss_type
self.negative_ratio = negative_ratio
self.return_origin = return_origin
self.eps = eps
if self.main_loss_type == "CrossEntropy":
self.loss = nn.CrossEntropyLoss()
elif self.main_loss_type == "Euclidean":
self.loss = nn.MSELoss()
elif self.main_loss_type == "DiceLoss":
self.loss = DiceLoss(self.eps)
elif self.main_loss_type == "BCELoss":
self.loss = BCELoss(reduction='none')
elif self.main_loss_type == "MaskL1Loss":
self.loss = MaskL1Loss(self.eps)
else:
loss_type = [
'CrossEntropy', 'DiceLoss', 'Euclidean', 'BCELoss', 'MaskL1Loss'
]
raise Exception(
"main_loss_type in BalanceLoss() can only be one of {}".format(
loss_type))
def forward(self, pred, gt, mask=None):
"""
The BalanceLoss for Differentiable Binarization text detection
args:
pred (variable): predicted feature maps.
gt (variable): ground truth feature maps.
mask (variable): masked maps.
return: (variable) balanced loss
"""
positive = gt * mask
negative = (1 - gt) * mask
positive_count = int(positive.sum())
negative_count = int(
min(negative.sum(), positive_count * self.negative_ratio))
loss = self.loss(pred, gt, mask=mask)
if not self.balance_loss:
return loss
positive_loss = positive * loss
negative_loss = negative * loss
negative_loss = paddle.reshape(negative_loss, shape=[-1])
if negative_count > 0:
sort_loss = negative_loss.sort(descending=True)
negative_loss = sort_loss[:negative_count]
# negative_loss, _ = paddle.topk(negative_loss, k=negative_count_int)
balance_loss = (positive_loss.sum() + negative_loss.sum()) / (
positive_count + negative_count + self.eps)
else:
balance_loss = positive_loss.sum() / (positive_count + self.eps)
if self.return_origin:
return balance_loss, loss
return balance_loss
class DiceLoss(nn.Layer):
def __init__(self, eps=1e-6):
super(DiceLoss, self).__init__()
self.eps = eps
def forward(self, pred, gt, mask, weights=None):
"""
DiceLoss function.
"""
assert pred.shape == gt.shape
assert pred.shape == mask.shape
if weights is not None:
assert weights.shape == mask.shape
mask = weights * mask
intersection = paddle.sum(pred * gt * mask)
union = paddle.sum(pred * mask) + paddle.sum(gt * mask) + self.eps
loss = 1 - 2.0 * intersection / union
assert loss <= 1
return loss
class MaskL1Loss(nn.Layer):
def __init__(self, eps=1e-6):
super(MaskL1Loss, self).__init__()
self.eps = eps
def forward(self, pred, gt, mask):
"""
Mask L1 Loss
"""
loss = (paddle.abs(pred - gt) * mask).sum() / (mask.sum() + self.eps)
loss = paddle.mean(loss)
return loss
class BCELoss(nn.Layer):
def __init__(self, reduction='mean'):
super(BCELoss, self).__init__()
self.reduction = reduction
def forward(self, input, label, mask=None, weight=None, name=None):
loss = F.binary_cross_entropy(input, label, reduction=self.reduction)
return loss
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