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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 paddle
- import paddle.nn as nn
- import paddle.nn.functional as F
- class DMLLoss(nn.Layer):
- """
- DMLLoss
- """
- def __init__(self, act="softmax"):
- super().__init__()
- if act is not None:
- assert act in ["softmax", "sigmoid"]
- if act == "softmax":
- self.act = nn.Softmax(axis=-1)
- elif act == "sigmoid":
- self.act = nn.Sigmoid()
- else:
- self.act = None
- def forward(self, out1, out2):
- if self.act is not None:
- out1 = self.act(out1)
- out2 = self.act(out2)
- log_out1 = paddle.log(out1)
- log_out2 = paddle.log(out2)
- loss = (F.kl_div(
- log_out1, out2, reduction='batchmean') + F.kl_div(
- log_out2, out1, reduction='batchmean')) / 2.0
- return {"DMLLoss": loss}
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