67 lines
2.5 KiB
Python
67 lines
2.5 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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class WSLLoss(nn.Layer):
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"""
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Weighted Soft Labels Loss
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paper: https://arxiv.org/pdf/2102.00650.pdf
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code reference: https://github.com/bellymonster/Weighted-Soft-Label-Distillation
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"""
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def __init__(self, temperature=2.0, use_target_as_gt=False):
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super().__init__()
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self.temperature = temperature
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self.use_target_as_gt = use_target_as_gt
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def forward(self, logits_student, logits_teacher, target=None):
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"""Compute weighted soft labels loss.
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Args:
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logits_student: student's logits with shape (batch_size, num_classes)
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logits_teacher: teacher's logits with shape (batch_size, num_classes)
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target: ground truth labels with shape (batch_size)
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"""
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if target is None or self.use_target_as_gt:
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target = logits_teacher.argmax(axis=-1)
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target = F.one_hot(
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target.reshape([-1]), num_classes=logits_student[0].shape[0])
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s_input_for_softmax = logits_student / self.temperature
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t_input_for_softmax = logits_teacher / self.temperature
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ce_loss_s = -paddle.sum(target *
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F.log_softmax(logits_student.detach()),
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axis=1)
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ce_loss_t = -paddle.sum(target *
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F.log_softmax(logits_teacher.detach()),
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axis=1)
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ratio = ce_loss_s / (ce_loss_t + 1e-7)
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ratio = paddle.maximum(ratio, paddle.zeros_like(ratio))
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kd_loss = -paddle.sum(F.softmax(t_input_for_softmax) *
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F.log_softmax(s_input_for_softmax),
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axis=1)
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weight = 1 - paddle.exp(-ratio)
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weighted_kd_loss = (self.temperature**2) * paddle.mean(kd_loss *
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weight)
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return weighted_kd_loss
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