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Python

# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
from abc import abstractmethod
import paddle
import paddle.nn as nn
#XXX use _forward?? or forward??
class BaseWeightedLoss(nn.Layer):
"""Base class for loss.
All subclass should overwrite the ``_forward()`` method which returns the
normal loss without loss weights.
Args:
loss_weight (float): Factor scalar multiplied on the loss.
Default: 1.0.
"""
def __init__(self, loss_weight=1.0):
super().__init__()
self.loss_weight = loss_weight
@abstractmethod
def _forward(self, *args, **kwargs):
pass
def forward(self, *args, **kwargs):
"""Defines the computation performed at every call.
Args:
*args: The positional arguments for the corresponding
loss.
**kwargs: The keyword arguments for the corresponding
loss.
Returns:
paddle.Tensor: The calculated loss.
"""
return self._forward(*args, **kwargs) * self.loss_weight