53 lines
1.8 KiB
Python
53 lines
1.8 KiB
Python
# copyright (c) 2022 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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def cosine_similarity(a, b, eps=1e-8):
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return (a * b).sum(1) / (a.norm(axis=1) * b.norm(axis=1) + eps)
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def pearson_correlation(a, b, eps=1e-8):
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return cosine_similarity(a - a.mean(1).unsqueeze(1),
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b - b.mean(1).unsqueeze(1), eps)
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def inter_class_relation(y_s, y_t):
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return 1 - pearson_correlation(y_s, y_t).mean()
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def intra_class_relation(y_s, y_t):
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return inter_class_relation(y_s.transpose([1, 0]), y_t.transpose([1, 0]))
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class DISTLoss(nn.Layer):
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# DISTLoss
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# paper [Knowledge Distillation from A Stronger Teacher](https://arxiv.org/pdf/2205.10536v1.pdf)
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# code reference: https://github.com/hunto/image_classification_sota/blob/d4f15a0494/lib/models/losses/dist_kd.py
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def __init__(self, beta=1.0, gamma=1.0):
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super().__init__()
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self.beta = beta
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self.gamma = gamma
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def forward(self, z_s, z_t):
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y_s = F.softmax(z_s, axis=-1)
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y_t = F.softmax(z_t, axis=-1)
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inter_loss = inter_class_relation(y_s, y_t)
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intra_loss = intra_class_relation(y_s, y_t)
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kd_loss = self.beta * inter_loss + self.gamma * intra_loss
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return kd_loss
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