PixelCNN代码

### PixelCNN代码实现

为了展示PixelCNN的代码实现，下面提供了一个简化版本的PyTorch实现。此实现基于推荐像素级卷积神经网络增强版（pixel-cNN++），该架构在CIFAR-10数据集上达到了2.92 bits per dimension的效果[^1]。

#### 导入必要的库

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms

#### 定义PixelCNN模型类

class MaskedConv2d(nn.Conv2d):
    def __init__(self, mask_type, *args, **kwargs):
        super(MaskedConv2d, self).__init__(*args, **kwargs)
        assert mask_type in {'A', 'B'}
        self.register_buffer('mask', self.weight.data.clone())
        _, depth, height, width = self.mask.size()
        self.mask.fill_(1)
        if mask_type == 'A':
            self.mask[:, :, height // 2, width // 2:] = 0
            self.mask[:, :, height // 2 + 1:, :] = 0
        else:
            self.mask[:, :, height // 2, width // 2 + 1:] = 0
            self.mask[:, :, height // 2 + 1:, :] = 0
    
    def forward(self, x):
        self.weight.data *= self.mask
        return super(MaskedConv2d, self).forward(x)

class PixelCNN(nn.Module):
    def __init__(self, input_channels=1, dim_hidden=64, n_layers=15):
        super(PixelCNN, self).__init__()
        
        layers = []
        layers.append(MaskedConv2d(mask_type='A', in_channels=input_channels,
                                   out_channels=dim_hidden, kernel_size=7, padding=3))
        layers.append(nn.ReLU(True))

        for _ in range(n_layers - 2):
            layers.append(MaskedConv2d(mask_type='B',
                                       in_channels=dim_hidden,
                                       out_channels=dim_hidden,
                                       kernel_size=7,
                                       padding=3))
            layers.append(nn.BatchNorm2d(dim_hidden))
            layers.append(nn.ReLU(True))

        layers.append(MaskedConv2d(mask_type='B',
                                   in_channels=dim_hidden,
                                   out_channels=input_channels,
                                   kernel_size=7,
                                   padding=3))
        self.network = nn.Sequential(*layers)

    def forward(self, x):
        return self.network(x)

#### 训练过程设置

def train(model, device, train_loader, optimizer, epoch):
    model.train()
    criterion = nn.MSELoss()

    for batch_idx, (data, _) in enumerate(train_loader):
        data = data.to(device)
        optimizer.zero_grad()
        output = model(data)
        loss = criterion(output, data)
        loss.backward()
        optimizer.step()
        if batch_idx % 100 == 0:
            print(f'Train Epoch: {epoch} [{batch_idx * len(data)}/{len(train_loader.dataset)} ({100. * batch_idx / len(train_loader):.0f}%)]\tLoss: {loss.item():.6f}')

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = PixelCNN().to(device)
optimizer = optim.Adam(model.parameters(), lr=1e-3)

transform = transforms.Compose([transforms.ToTensor()])
train_dataset = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=64, shuffle=True)

for epoch in range(1, 11):
    train(model, device, train_loader, optimizer, epoch)

上述代码实现了基本的PixelCNN训练流程，并使用了CIFAR-10数据集进行实验验证。注意这里的损失函数选择了均方误差（MSE Loss），这取决于具体的应用场景和需求调整优化器的选择以及超参数配置。