先容怎样利用RDDM（残差噪声双扩散模型）进行知识蒸馏 - IT评测·应用市场-qidao123.com

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, Dataset
# 假设这里已经有RDDM模型的定义
class RDDM(nn.Module):
def __init__(self):
super(RDDM, self).__init__()
# 这里简单示例，实际需要根据RDDM的具体结构实现
self.fc = nn.Linear(10, 10)
def forward(self, x):
return self.fc(x)
# 自定义数据集类
class CustomDataset(Dataset):
def __init__(self, data):
self.data = data
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
return self.data[idx]
# 知识蒸馏训练函数
def knowledge_distillation(teacher_model, student_model, dataloader, criterion, optimizer, epochs):
teacher_model.eval()
for epoch in range(epochs):
running_loss = 0.0
for data in dataloader:
optimizer.zero_grad()
with torch.no_grad():
teacher_output = teacher_model(data)
student_output = student_model(data)
loss = criterion(student_output, teacher_output)
loss.backward()
optimizer.step()
running_loss += loss.item()
print(f'Epoch {epoch + 1}, Loss: {running_loss / len(dataloader)}')
# 一步去噪函数
def one_step_denoising(student_model, noisy_data):
student_model.eval()
with torch.no_grad():
denoised_data = student_model(noisy_data)
return denoised_data
# 主函数
def main():
# 模拟数据
num_samples = 1000
input_dim = 10
data = torch.randn(num_samples, input_dim)
dataset = CustomDataset(data)
dataloader = DataLoader(dataset, batch_size=32, shuffle=True)
# 初始化教师和学生模型
teacher_model = RDDM()
student_model = RDDM()
# 定义损失函数和优化器
criterion = nn.MSELoss()
optimizer = optim.Adam(student_model.parameters(), lr=0.001)
# 知识蒸馏训练
epochs = 10
knowledge_distillation(teacher_model, student_model, dataloader, criterion, optimizer, epochs)
# 模拟噪声数据进行一步去噪
noisy_data = torch.randn(1, input_dim)
denoised_data = one_step_denoising(student_model, noisy_data)
print("Noisy data:", noisy_data)
print("Denoised data:", denoised_data)
if __name__ == "__main__":
main()

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