ai生成的我只是搬运工
StyleGAN模型训练和生成虚拟人脸的具体代码示例可以通过参考多个研究论文中的实现细节来获取。以下是一些关键步骤和代码片段的概述：
1. 数据准备
需要准备一个大规模的真实人脸数据集，例如FFHQ（Flickr-Faces-HQ）数据集。这个数据集将用于训练StyleGAN模型。
import torch
from torchvision import datasets, transforms# 数据预处理
transform = transforms.Compose([transforms.Resize(256),transforms.CenterCrop(256),transforms.ToTensor(),transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])# 加载数据集
dataset = datasets.ImageFolder(root='path_to_ffhq', transform=transform)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=32, shuffle=True)
2. 模型定义
定义StyleGAN的生成器和判别器模型。StyleGAN的生成器和判别器通常使用卷积神经网络（CNN）来实现。
import torch.nn as nnclass Generator(nn.Module):def __init__(self, nz):super(Generator, self).__init__()self.main = nn.Sequential(nn.ConvTranspose2d(nz, ngf * 8, 4, 1, 0, bias=False),nn.BatchNorm2d(ngf * 8),nn.ReLU(True),nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False),nn.BatchNorm2d(ngf * 4),nn.ReLU(True),nn.ConvTranspose2d(ngf * 4, ngf * 2, 4, 2, 1, bias=False),nn.BatchNorm2d(ngf * 2),nn.ReLU(True),nn.ConvTranspose2d(ngf * 2, ngf, 4, 2, 1, bias=False),nn.BatchNorm2d(ngf),nn.ReLU(True),nn.ConvTranspose2d(ngf, nc, 4, 2, 1, bias=False),nn.Tanh())def forward(self, input):return self.main(input)class Discriminator(nn.Module):def __init__(self):super(Discriminator, self).__init__()self.main = nn.Sequential(nn.Conv2d(nc, ndf, 4, 2, 1, bias=False),nn.LeakyReLU(0.2, inplace=True),nn.Conv2d(ndf, ndf * 2, 4, 2, 1, bias=False),nn.BatchNorm2d(ndf * 2),nn.LeakyReLU(0.2, inplace=True),nn.Conv2d(ndf * 2, ndf * 4, 4, 2, 1, bias=False),nn.BatchNorm2d(ndf * 4),nn.LeakyReLU(0.2, inplace=True),nn.Conv2d(ndf * 4, ndf * 8, 4, 2, 1, bias=False),nn.BatchNorm2d(ndf * 8),nn.LeakyReLU(0.2, inplace=True),nn.Conv2d(ndf * 8, 1, 4, 1, 0, bias=False),nn.Sigmoid())def forward(self, input):return self.main(input)
3. 训练过程
在训练过程中，生成器和判别器交替更新，生成器试图生成逼真的图像以欺骗判别器，而判别器则试图区分真实图像和生成图像。
import torch.optim as optim# 初始化模型
netG = Generator(nz)
netD = Discriminator()# 定义损失函数和优化器
criterion = nn.BCELoss()
optimizerG = optim.Adam(netG.parameters(), lr=lr)
optimizerD = optim.Adam(netD.parameters(), lr=lr)# 训练循环
for epoch in range(num_epochs):for i, data in enumerate(dataloader):real_images = data[0]batch_size = real_images.size(0)# 训练判别器netD.zero_grad()real_labels = torch.ones(batch_size, 1)fake_labels = torch.zeros(batch_size, 1)# 真实图像的损失output = netD(real_images)errD_real = criterion(output, real_labels)errD_real.backward()# 生成假图像noise = torch.randn(batch_size, nz)fake_images = netG(noise)# 假图像的损失output = netD(fake_images.detach())errD_fake = criterion(output, fake_labels)errD_fake.backward()# 总损失errD = errD_real + errD_fakeoptimizerD.step()# 训练生成器netG.zero_grad()output = netD(fake_images)errG = criterion(output, real_labels)errG.backward()optimizerG.step()if i % 100 == 0:print(f'Epoch [{epoch}/{num_epochs}], Step [{i}/{len(dataloader)}], Loss_D: {errD.item()}, Loss_G: {errG.item()}')
4. 生成虚拟人脸
训练完成后，可以使用训练好的生成器来生成虚拟人脸。
# 生成虚拟人脸
fixed_noise = torch.randn(64, nz)
fake_images = netG(fixed_noise)# 保存生成的图像
save_image(fake_images.detach().cpu(), 'fake_faces.png', nrow=8)