ddpm训练自己数据集的代码

DDPM（Denoising Diffusion Probabilistic Model）是一种生成式模型，可以用于图像生成和图像去噪等任务。下面是用PyTorch框架训练自己数据集的DDPM代码示例：

import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.transforms as transforms
import torchvision.datasets as datasets
from torch.utils.data import DataLoader

# 定义DDPM模型
class DDPM(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(DDPM, self).__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.conv1 = nn.Conv2d(in_channels, 128, 3, stride=1, padding=1, bias=False)
        self.conv2 = nn.Conv2d(128, 128, 3, stride=1, padding=1, bias=False)
        self.conv3 = nn.Conv2d(128, 128, 3, stride=1, padding=1, bias=False)
        self.conv4 = nn.Conv2d(128, out_channels, 3, stride=1, padding=1, bias=False)
        self.register_buffer('eps', torch.tensor(1e-8))

    def forward(self, x):
        noise = torch.randn_like(x)
        out = self.conv1(noise)
        out = F.relu(out)
        out = self.conv2(out)
        out = F.relu(out)
        out = self.conv3(out)
        out = F.relu(out)
        out = self.conv4(out)
        out = out / torch.sqrt(torch.mean(out**2, dim=[1,2,3], keepdim=True) + self.eps)
        return x + out

# 定义训练函数
def train(model, train_loader, optimizer, criterion, device):
    model.train()
    for i, (input, _) in enumerate(train_loader):
        input = input.to(device)
        optimizer.zero_grad()
        output = model(input)
        loss = criterion(output, input)
        loss.backward()
        optimizer.step()
        if i % 10 == 0:
            print('Step [{}/{}], Loss: {:.4f}'.format(i, len(train_loader), loss.item()))

# 定义数据集和数据加载器
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5,), (0.5,))
])

train_dataset = datasets.ImageFolder(root='./train', transform=transform)
train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True)

# 定义模型、损失函数、优化器和设备
model = DDPM(1, 1).to('cuda')
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

# 训练模型
num_epochs = 10
for epoch in range(num_epochs):
    train(model, train_loader, optimizer, criterion, 'cuda')

# 保存模型
torch.save(model.state_dict(), 'ddpm.pth')

在代码中，我们定义了一个DDPM模型，包含四个卷积层和一个标准差归一化层，用于对输入数据进行处理。然后，我们使用PyTorch自带的`ImageFolder`类加载训练集数据，并使用`DataLoader`类构建数据加载器。接着，我们定义了一个训练函数`