PyTorch实现简单的变分自动编码器VAE

import os
import torch
import torch.nn.functional as F
from torch import nn
from torch.utils.data import DataLoader
from torchvision.datasets import MNIST
from torchvision import transforms as tfs
from torchvision.utils import save_image

# Hyper parameters
EPOCH = 1
LR = 1e-3
BATCHSIZE = 128

im_tfs = tfs.Compose([
    tfs.ToTensor(),    # Converts a PIL.Image or numpy.ndarray to
                       # torch.FloatTensor of shape (C x H x W) and normalize in the range [0.0, 1.0]
    tfs.Normalize([0.5], [0.5])   # 把[0.0, 1.0]的数据扩大范围到[-1., 1]
])

train_set = MNIST(
    root=‘/Users/wangpeng/Desktop/all/CS/Courses/Deep Learning/mofan_PyTorch/mnist/‘,   # mnist has been downloaded before, use it directly
    train=True,
    transform=im_tfs,
)
train_loader = DataLoader(train_set, batch_size=BATCHSIZE, shuffle=True)


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

        self.fc1 = nn.Linear(784, 400)
        self.fc21 = nn.Linear(400, 20)   # mean
        self.fc22 = nn.Linear(400, 20)   # var
        self.fc3 = nn.Linear(20, 400)
        self.fc4 = nn.Linear(400, 784)

    def encode(self, x):
        h1 = F.relu(self.fc1(x))
        return self.fc21(h1), self.fc22(h1)

    def reparametrize(self, mu, logvar):
        std = logvar.mul(0.5).exp_()                     # 矩阵点对点相乘之后再把这些元素作为e的指数
        eps = torch.FloatTensor(std.size()).normal_()    # 生成随机数组
        if torch.cuda.is_available():
            eps = eps.cuda()
        return eps.mul(std).add_(mu)    # 用一个标准正态分布乘标准差，再加上均值，使隐含向量变为正太分布

    def decode(self, z):
        h3 = F.relu(self.fc3(z))
        return torch.tanh(self.fc4(h3))

    def forward(self, x):
        mu, logvar = self.encode(x)          # 编码
        z = self.reparametrize(mu, logvar)   # 重新参数化成正态分布
        return self.decode(z), mu, logvar    # 解码，同时输出均值方差


net = VAE()  # 实例化网络
if torch.cuda.is_available():
    net = net.cuda()

reconstruction_function = nn.MSELoss(size_average=False)


def loss_function(recon_x, x, mu, logvar):
    """
    recon_x: generating images
    x: origin images
    mu: latent mean
    logvar: latent log variance
    """
    MSE = reconstruction_function(recon_x, x)
    # loss = 0.5 * sum(1 + log(sigma^2) - mu^2 - sigma^2)
    KLD_element = mu.pow(2).add_(logvar.exp()).mul_(-1).add_(1).add_(logvar)
    KLD = torch.sum(KLD_element).mul_(-0.5)
    # KL divergence
    return MSE + KLD


optimizer = torch.optim.Adam(net.parameters(), lr=LR)


def to_img(x):   # x shape (bachsize, 28*28), x中每个像素点的大小范围[-1., 1.]
    ‘‘‘
    定义一个函数将最后的结果转换回图片
    ‘‘‘
    x = 0.5 * (x + 1.)
    x = x.clamp(0, 1)
    x = x.view(x.shape[0], 1, 28, 28)
    return x


for epoch in range(EPOCH):
    for iteration, (im, y) in enumerate(train_loader):
        im = im.view(im.shape[0], -1)
        if torch.cuda.is_available():
            im = im.cuda()
        recon_im, mu, logvar = net(im)
        loss = loss_function(recon_im, im, mu, logvar) / im.shape[0]   # 将 loss 平均
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        if iteration % 100 == 0:
            print(‘epoch: {:2d} | iteration: {:4d} | Loss: {:.4f}‘.format(epoch, iteration, loss.data.numpy()))
            save = to_img(recon_im.cpu().data)
            if not os.path.exists(‘./vae_img‘):
                os.mkdir(‘./vae_img‘)
            save_image(save, ‘./vae_img/image_{}_{}.png‘.format(epoch, iteration))


# test
code = torch.randn(1, 20)   # 随机给一个符合正态分布的张量
out = net.decode(code)
img = to_img(out)
save_image(img, ‘./vae_img/test_img.png‘)