博客

以下是使用PyTorch实现超分辨率的示例代码：

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

# 定义超分辨率模型
class SRModel(nn.Module):
    def __init__(self):
        super(SRModel, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(64, 64, kernel_size=3, padding=1)
        self.conv3 = nn.Conv2d(64, 32, kernel_size=3, padding=1)
        self.conv4 = nn.Conv2d(32, 3, kernel_size=3, padding=1)
        self.relu = nn.ReLU(inplace=True)
    def forward(self, x):
        out = self.relu(self.conv1(x))
        out = self.relu(self.conv2(out))
        out = self.relu(self.conv3(out))
        out = self.conv4(out)
        return out

# 定义超分辨率训练函数
def train_sr_model(model, train_loader, val_loader, num_epochs=10, lr=0.001):
    criterion = nn.MSELoss()
    optimizer = optim.Adam(model.parameters(), lr=lr)
    for epoch in range(num_epochs):
        # 训练模型
        model.train()
        train_loss = 0.0
        for i, (inputs, targets) in enumerate(train_loader):
            optimizer.zero_grad()
            outputs = model(inputs)
            loss = criterion(outputs, targets)
            loss.backward()
            optimizer.step()
            train_loss += loss.item() * inputs.size(0)
        train_loss /= len(train_loader.dataset)
        # 验证模型
        model.eval()
        val_loss = 0.0
        with torch.no_grad():
            for inputs, targets in val_loader:
                outputs = model(inputs)
                loss = criterion(outputs, targets)
                val_loss += loss.item() * inputs.size(0)
            val_loss /= len(val_loader.dataset)
        # 打印训练信息
        print(f"Epoch {epoch+1}/{num_epochs}: Train Loss={train_loss:.4f}, Val Loss={val_loss:.4f}")

# 加载数据集
train_transforms = transforms.Compose([
    transforms.Resize((256, 256)),
    transforms.RandomCrop(224),
    transforms.ToTensor(),
])
val_transforms = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
])
train_dataset = ImageFolder("path/to/train/dataset", transform=train_transforms)
val_dataset = ImageFolder("path/to/val/dataset", transform=val_transforms)
train_loader = DataLoader(train_dataset, batch_size=8, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=8)
# 创建模型并训练
model = SRModel()
train_sr_model(model, train_loader, val_loader)

这是一个简单的超分辨率模型，它使用MSE损失函数和Adam优化器进行训练。您可以根据需要更改模型架构、损失函数、优化器和超参数等。

以下是一些在GitHub上实现超分辨率的项目：

1. pytorch-super-resolution：使用PyTorch实现的多种超分辨率算法，包括SRCNN、ESPCN、SRGAN等。
2. Super-Resolution.Pytorch：包含多种超分辨率算法的PyTorch实现，同时也提供了预训练模型。
3. pytorch-cnn-visualizations：使用PyTorch实现的多种卷积神经网络可视化技术，包括超分辨率。