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# Copyright (c) 2021 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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import paddle
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import paddle.nn as nn
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import paddle.nn.functional as F
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class DMLLoss(nn.Layer):
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"""
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DMLLoss
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"""
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def __init__(self, act="softmax", eps=1e-12):
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super().__init__()
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if act is not None:
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assert act in ["softmax", "sigmoid"]
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if act == "softmax":
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self.act = nn.Softmax(axis=-1)
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elif act == "sigmoid":
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self.act = nn.Sigmoid()
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else:
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self.act = None
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self.eps = eps
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def _kldiv(self, x, target):
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class_num = x.shape[-1]
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cost = target * paddle.log(
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(target + self.eps) / (x + self.eps)) * class_num
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return cost
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def forward(self, x, target):
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if self.act is not None:
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x = self.act(x)
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target = self.act(target)
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loss = self._kldiv(x, target) + self._kldiv(target, x)
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loss = loss / 2
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loss = paddle.mean(loss)
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return {"DMLLoss": loss}
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