pytorch有自带的VAE吗

是的，PyTorch中有自带的VAE（Variational Autoencoder）模块，可以通过以下代码进行导入：

import torch.nn as nn

class VAE(nn.Module):
    def __init__(self, input_size, hidden_size, latent_size):
        super(VAE, self).__init__()

        self.encoder = nn.Sequential(
            nn.Linear(input_size, hidden_size),
            nn.ReLU(),
            nn.Linear(hidden_size, latent_size * 2)
        )

        self.decoder = nn.Sequential(
            nn.Linear(latent_size, hidden_size),
            nn.ReLU(),
            nn.Linear(hidden_size, input_size),
            nn.Sigmoid()
        )

    def encode(self, x):
        h = self.encoder(x)
        mu, logvar = torch.chunk(h, 2, dim=-1)
        return mu, logvar

    def reparameterize(self, mu, logvar):
        std = torch.exp(0.5 * logvar)
        eps = torch.randn_like(std)
        return mu + eps * std

    def decode(self, z):
        x = self.decoder(z)
        return x

    def forward(self, x):
        mu, logvar = self.encode(x)
        z = self.reparameterize(mu, logvar)
        x_recon = self.decode(z)
        return x_recon, mu, logvar

这个实现是一个基本的VAE，包括Encoder和Decoder两个模块，其中Encoder将输入数据映射为均值和方差，Decoder将隐变量解码为输出数据。