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50 lines
1.5 KiB
50 lines
1.5 KiB
# 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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