参考连接：

Knowledge Distillation Tutorial — PyTorch Tutorials 2.2.1+cu121 documentation

方法一  ：    

知识蒸馏的损失函数只接受两个相同维度的输入，所以我们需要采取措施使他们在进入损失函数之前是相同维度的。我们将使用平均池化层对在教师模型卷积运算后的logits进行池化，使得logits维度和学生保持一样。

方法二：

原来的教师模型：

只有forward函数内有调整

class DeepNN(nn.Module):

    def forward(self, x):
        x = self.features(x)
        x = torch.flatten(x, 1)
        x = self.classifier(x)
        return x

加入了池化后的教师模型：

class ModifiedDeepNNCosine(nn.Module):

    def forward(self, x):
        x = self.features(x)
        flattened_conv_output = torch.flatten(x, 1)
        x = self.classifier(flattened_conv_output)
        flattened_conv_output_after_pooling = torch.nn.functional.avg_pool1d(flattened_conv_output, 2)
        return x, flattened_conv_output_after_pooling

原来的学生模型

class LightNN(nn.Module):

    def forward(self, x):
        x = self.features(x)
        x = torch.flatten(x, 1)
        x = self.classifier(x)
        return x

更新后的学生模型：

class ModifiedLightNNCosine(nn.Module):

    def forward(self, x):
        x = self.features(x)
        flattened_conv_output = torch.flatten(x, 1)
        x = self.classifier(flattened_conv_output)
        return x, flattened_conv_output

方法三：

 

Teacher accuracy: 75.60%
Student accuracy without teacher: 70.41%
Student accuracy with CE + KD: 70.19%
Student accuracy with CE + CosineLoss: 70.87%
Student accuracy with CE + RegressorMSE: 71.40%