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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.MSE _Loss_() 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`](https://wenku.csdn.net/answer/3686e46d62d54b8a88c92afc9d55357c)
Original: https://blog.csdn.net/qq_41895747/article/details/123660935
Author: 沉迷单车的追风少年
Title: DDPM代码详细解读(1)：数据集准备、超参数设置、loss设计、关键参数计算
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