PyTorch 中使用Diffusion model代码举例

下面是一个使用 PyTorch 实现 Diffusion model 的示例代码，用于图像生成任务：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
import torchvision.datasets as datasets
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter

# 定义 Diffusion model 的参数
timesteps = 1000
noise_scale = 0.1

# 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.MNIST(root='./data', train=True, transform=transform, download=True)
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)

# 定义模型和优化器
class DiffusionModel(nn.Module):
    def __init__(self):
        super().__init__()
        self.encoder = nn.Sequential(
            nn.Conv2d(1, 16, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(16),
            nn.ReLU(),
            nn.Conv2d(16, 32, kernel_size=3, stride=2, padding=1),
            nn.BatchNorm2d(32),
            nn.ReLU(),
            nn.Conv2d(32, 64, kernel_size=3, stride=2, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(),
            nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(),
            nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1),
            nn.BatchNorm2d(256),
            nn.ReLU(),
            nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1),
            nn.BatchNorm2d(512),
            nn.ReLU(),
            nn.Conv2d(512, 1024, kernel_size=3, stride=2, padding=1),
            nn.BatchNorm2d(1024),
            nn.ReLU()
        )
        self.decoder = nn.Sequential(
            nn.ConvTranspose2d(1024, 512, kernel_size=3, stride=2, padding=1),
            nn.BatchNorm2d(512),
            nn.ReLU(),
            nn.ConvTranspose2d(512, 256, kernel_size=3, stride=2, padding=1),
            nn.BatchNorm2d(256),
            nn.ReLU(),
            nn.ConvTranspose2d(256, 128, kernel_size=3, stride=2, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(),
            nn.ConvTranspose2d(128, 64, kernel_size=3, stride=2, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(),
            nn.ConvTranspose2d(64, 32, kernel_size=3, stride=2, padding=1),
            nn.BatchNorm2d(32),
            nn.ReLU(),
            nn.ConvTranspose2d(32, 16, kernel_size=3, stride=2, padding=1),
            nn.BatchNorm2d(16),
            nn.ReLU(),
            nn.ConvTranspose2d(16, 1, kernel_size=3, stride=1, padding=1),
            nn.Tanh()
        )

    def forward(self, x):
        z = self.encoder(x)
        x = self.decoder(z)
        return x, z

model = DiffusionModel().to(device)
optimizer = optim.Adam(model.parameters(), lr=1e-3)

# 定义损失函数
def diff_loss(x, x0, t):
    noise = torch.randn_like(x)
    noise = noise * torch.sqrt(t) * noise_scale
    x1 = (1 - t) * x0 + noise
    nll = ((x1 - x) ** 2).mean() / (2 * noise_scale ** 2)
    return nll

# 训练模型
writer = SummaryWriter()
for epoch in range(10):
    for i, (x, _) in enumerate(train_loader):
        x = x.to(device)
        optimizer.zero_grad()

        x0 = x + noise_scale * torch.randn_like(x)
        x0 = x0.clamp(-1, 1)
        t = torch.rand(x.size(0), device=device) * timesteps

        loss = diff_loss(x, x0, t)
        loss.backward()
        optimizer.step()

        writer.add_scalar('loss', loss.item(), epoch * len(train_loader) + i)

        if i % 100 == 0:
            print(f'Epoch {epoch}, Iteration {i}, Loss {loss.item()}')

在上面的代码中，我们首先定义了 Diffusion model 的相关参数，包括迭代次数和噪声大小。然后加载 MNIST 数据集，并定义了模型和优化器。接着，我们定义了损失函数 diff_loss，该函数采用了噪声扩散和反演过程，并计算了负对数似然损失。最后，我们使用 PyTorch 提供的 DataLoader 进行训练，并记录了训练过程中的损失值。