写一段用pytorch实现的GAN插值过程的代码

以下是一个使用 PyTorch 实现的 GAN 插值过程的示例代码：

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

# 定义生成器和判别器模型
class Generator(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc = nn.Linear(100, 128)
        self.relu = nn.ReLU()
        self.fc2 = nn.Linear(128, 784)
        self.tanh = nn.Tanh()

    def forward(self, x):
        x = self.fc(x)
        x = self.relu(x)
        x = self.fc2(x)
        x = self.tanh(x)
        return x

class Discriminator(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc1 = nn.Linear(784, 128)
        self.relu = nn.ReLU()
        self.fc2 = nn.Linear(128, 1)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        x = self.fc1(x)
        x = self.relu(x)
        x = self.fc2(x)
        x = self.sigmoid(x)
        return x

# 定义损失函数和优化器
criterion = nn.BCELoss()
generator = Generator()
discriminator = Discriminator()
optimizer_g = optim.Adam(generator.parameters(), lr=0.0002, betas=(0.5, 0.999))
optimizer_d = optim.Adam(discriminator.parameters(), lr=0.0002, betas=(0.5, 0.999))

# 训练 GAN 模型
for epoch in range(num_epochs):
    for i, (real_images, _) in enumerate(data_loader):
        # 训练判别器
        real_labels = torch.ones(batch_size, 1)
        fake_labels = torch.zeros(batch_size, 1)
        real_images = real_images.view(-1, 784)
        real_outputs = discriminator(real_images)
        d_loss_real = criterion(real_outputs, real_labels)

        z = torch.randn(batch_size, 100)
        fake_images = generator(z)
        fake_outputs = discriminator(fake_images.detach())
        d_loss_fake = criterion(fake_outputs, fake_labels)

        d_loss = d_loss_real + d_loss_fake
        discriminator.zero_grad()
        d_loss.backward()
        optimizer_d.step()

        # 训练生成器
        z = torch.randn(batch_size, 100)
        fake_images = generator(z)
        fake_outputs = discriminator(fake_images)
        g_loss = criterion(fake_outputs, real_labels)

        generator.zero_grad()
        g_loss.backward()
        optimizer_g.step()

        # 计算插值图像
        z1 = torch.randn(1, 100)
        z2 = torch.randn(1, 100)
        x1 = generator(z1)
        x2 = generator(z2)
        alpha = torch.linspace(0, 1, num=10).reshape(-1, 1)
        z_interp = alpha * z1 + (1 - alpha) * z2
        x_interp = generator(z_interp)

        # 保存插值图像
        if i % 100 == 0:
            save_image(x_interp.data, f"images/{epoch}_{i}.png", nrow=10, normalize=True)

这段代码使用了 PyTorch 框架，定义了一个简单的生成器和判别器模型，然后通过训练 GAN 模型得到了这两个模型的参数。接着，使用随机噪声向量生成一些图像，并计算出它们的插值图像。最后，将插值图像保存到本地文件夹中。