知识蒸馏python代码

知识蒸馏，也称为模型压缩或迁移学习的一种形式，是指将一个大型、复杂的深度模型（通常被称为教师模型）的知识传授给一个小巧的、效率更高的模型（学生模型）。Python是常用的语言之一，来进行知识蒸馏的实现。

下面是一个简单的使用PyTorch实现知识蒸馏的例子：

import torch
from torch import nn

# 假设我们有预训练的教师网络teacher_net
teacher_net = ... # Teacher Model (Pre-trained)

# 定义学生网络student_net，结构通常简化一些
class StudentNet(nn.Module):
    def __init__(self):
        super(StudentNet, self).__init__()
        self.student_layers = ... # Your student network layers

    def forward(self, x):
        return self.student_layers(x)

student_net = StudentNet()

# 使用教师网络的输出作为学生的预测，并添加一个温度归一化层
temperature = 10
kd_loss_fn = nn.KLDivLoss(reduction='batchmean')  # Knowledge Distillation Loss Function

def kd_loss(student_outs, teacher_outs):
    soft_teacher_outs = teacher_outs.softmax(dim=1) / temperature
    soft_student_outs = student_outs.softmax(dim=1)
    return kd_loss_fn(soft_student_outs, soft_teacher_outs)

# 训练过程
optimizer = torch.optim.Adam(student_net.parameters())
for epoch in range(num_epochs):
    # 获取数据
    inputs, targets = ...
    
    # 教师网络前向传播并获取logits
    teacher_logits = teacher_net(inputs)
    
    # 学生网络前向传播
    student_logits = student_net(inputs)
    
    # 计算知识蒸馏损失
    loss = kd_loss(student_logits, teacher_logits)
    
    # 反向传播和优化
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()