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# Copyright (c) 2021 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.
import numpy as np
import paddle
from paddle import nn
import paddle.nn.functional as F
from paddleseg.cvlibs import manager
@manager.LOSSES.add_component
class MixedLoss(nn.Layer):
"""
Weighted computations for multiple Loss.
The advantage is that mixed loss training can be achieved without changing the networking code.
Args:
losses (list[nn.Layer]): A list consisting of multiple loss classes
coef (list[float|int]): Weighting coefficient of multiple loss
Returns:
A callable object of MixedLoss.
"""
def __init__(self, losses, coef):
super(MixedLoss, self).__init__()
if not isinstance(losses, list):
raise TypeError('`losses` must be a list!')
if not isinstance(coef, list):
raise TypeError('`coef` must be a list!')
len_losses = len(losses)
len_coef = len(coef)
if len_losses != len_coef:
raise ValueError(
'The length of `losses` should equal to `coef`, but they are {} and {}.'
.format(len_losses, len_coef))
self.losses = losses
self.coef = coef
def forward(self, logits, labels):
loss_list = []
for i, loss in enumerate(self.losses):
output = loss(logits, labels)
loss_list.append(output * self.coef[i])
return loss_list
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