DDPM的训练代码

以下是一个基于PyTorch实现的DDPM模型的训练代码示例：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision.datasets import CIFAR10
from torchvision.transforms import Compose, RandomCrop, ToTensor

# 构建数据集
transform = Compose([RandomCrop(32, padding=4), ToTensor()])
train_data = CIFAR10(root='./data', train=True, transform=transform, download=True)
train_loader = DataLoader(train_data, batch_size=128, shuffle=True, num_workers=4)

# 构建模型
class DDPM(nn.Module):
    def __init__(self):
        super(DDPM, self).__init__()
        self.conv1 = nn.Conv2d(3, 128, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(128, 128, kernel_size=3, padding=1)
        self.conv3 = nn.Conv2d(128, 128, kernel_size=3, padding=1)
        self.conv4 = nn.Conv2d(128, 128, kernel_size=3, padding=1)
        self.conv5 = nn.Conv2d(128, 3, kernel_size=3, padding=1)

    def forward(self, x):
        h = nn.functional.relu(self.conv1(x))
        h = nn.functional.relu(self.conv2(h))
        h = nn.functional.relu(self.conv3(h))
        h = nn.functional.relu(self.conv4(h))
        return self.conv5(h)

# 构建优化器和损失函数
model = DDPM()
optimizer = optim.Adam(model.parameters(), lr=1e-4)
criterion = nn.MSELoss()

# 开始训练
for epoch in range(100):
    for i, (x, _) in enumerate(train_loader):
        optimizer.zero_grad()
        x = x.cuda()
        loss = criterion(model(x), x)
        loss.backward()
        optimizer.step()
        if i % 100 == 0:
            print(f"Epoch {epoch}, Iteration {i}, Loss {loss.item()}")

这段代码中，我们使用了一个简单的DDPM模型，将其应用于CIFAR10数据集的训练中。我们使用了Adam优化器和均方误差损失函数进行训练。训练过程中，我们每100个iteration输出一次当前的loss。你可以根据实际情况修改模型结构，调整超参数等。