基于对抗神经网络图像超分辨率算法代码

对抗神经网络图像超分辨率 (GANs-based Image Super-Resolution) 是一种深度学习算法，旨在通过增加图像的分辨率来提高图像质量。以下是一个基于 PyTorch 的 GANs-based Image Super-Resolution 算法的示例代码：

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

# 定义判别器
class Discriminator(nn.Module):
    def __init__(self):
        super(Discriminator, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=4, stride=2, padding=1)
        self.conv2 = nn.Conv2d(64, 128, kernel_size=4, stride=2, padding=1)
        self.bn2 = nn.BatchNorm2d(128)
        self.conv3 = nn.Conv2d(128, 256, kernel_size=4, stride=2, padding=1)
        self.bn3 = nn.BatchNorm2d(256)
        self.conv4 = nn.Conv2d(256, 512, kernel_size=4, stride=2, padding=1)
        self.bn4 = nn.BatchNorm2d(512)
        self.conv5 = nn.Conv2d(512, 1, kernel_size=4, stride=1, padding=0)

    def forward(self, x):
        x = nn.functional.leaky_relu(self.conv1(x), 0.2, inplace=True)
        x = nn.functional.leaky_relu(self.bn2(self.conv2(x)), 0.2, inplace=True)
        x = nn.functional.leaky_relu(self.bn3(self.conv3(x)), 0.2, inplace=True)
        x = nn.functional.leaky_relu(self.bn4(self.conv4(x)), 0.2, inplace=True)
        x = torch.sigmoid(self.conv5(x))
        return x

# 定义生成器
class Generator(nn.Module):
    def __init__(self):
        super(Generator, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=9, stride=1, padding=4)
        self.bn1 = nn.BatchNorm2d(64)
        self.conv2 = nn.Conv2d(64, 32, kernel_size=3, stride=1, padding=1)
        self.bn2 = nn.BatchNorm2d(32)
        self.conv3 = nn.Conv2d(32, 3, kernel_size=5, stride=1, padding=2)

    def forward(self, x):
        x = nn.functional.relu(self.bn1(self.conv1(x)))
        x = nn.functional.relu(self.bn2(self.conv2(x)))
        x = torch.tanh(self.conv3(x))
        return x

# 参数设置
batch_size = 32
epochs = 100
lr = 0.0002
beta1 = 0.5
beta2 = 0.999

# 数据集
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])])
train_data = datasets.ImageFolder(root='./data', transform=transform)
train_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size, shuffle=True)

# 模型初始化
G = Generator()
D = Discriminator()
G.cuda()
D.cuda()

# 损失函数和优化器
criterion = nn.BCELoss()
optimizerG = optim.Adam(G.parameters(), lr=lr, betas=(beta1, beta2))
optimizerD = optim.Adam(D.parameters(), lr=lr, betas=(beta1, beta2))

# 训练模型
for epoch in range(epochs):
    for i, (data, _) in enumerate(train_loader):
        # 真实数据
        real = Variable(data.cuda())
        real_target = Variable(torch.ones(real.size(0), 1).cuda())

        # 噪声数据
        noise = Variable(torch.randn(real.size(0), 3, 256, 256).cuda())
        fake = G(noise)
        fake_target = Variable(torch.zeros(fake.size(0), 1).cuda())

        # 训练判别器
        optimizerD.zero_grad()
        real_loss = criterion(D(real), real_target)
        fake_loss = criterion(D(fake.detach()), fake_target)
        d_loss = real_loss + fake_loss
        d_loss.backward()
        optimizerD.step()

        # 训练生成器
        optimizerG.zero_grad()
        fake_loss = criterion(D(fake), real_target)
        g_loss = fake_loss
        g_loss.backward()
        optimizerG.step()

        # 打印损失值
        if i % 10 == 0:
            print('[%d/%d][%d/%d] Loss_D: %.4f Loss_G: %.4f' % (epoch, epochs, i, len(train_loader), d_loss.item(), g_loss.item()))

# 保存模型
torch.save(G.state_dict(), './generator.pth')

此代码定义了一个生成器和一个判别器，使用了对抗训练算法来提高图像分辨率。在训练过程中，生成器生成虚假的图像，判别器则用来区分真实图像和虚假图像。随着训练的进行，生成器逐渐学习到如何生成更加真实的图像，从而提高图像分辨率。