VQ-VAE(Neural Discrete Representation Learning)论文解读及实现

pytorch 实现git地址
论文地址：Neural Discrete Representation Learning

1 论文核心知识点

• encoder
  将图片通过encoder得到图片点表征
  如输入shape [32,3,32,32]
  通过encoder后输出 [32,64,8,8] (其中64位输出维度)

• 量化码本
  先随机构建一个码本，维度与encoder保持一致
  这里定义512个离散特征，码本shap 为[512,64]

• encoder 码本中向量最近查找
  encoder输出shape [32,64,8,8], 经过维度变换 shape [3288,64]
  在码本中找到最相近的向量，并替换为码本中相似向量
  输出shape [3288,64]，维度变换后，shape 为 [32,64,8,8]

• decoder
  将上述数据，喂给decoder，还原原始图片

• loss
  loss 包含两部分
  a . encoder输出和码本向量接近
  b. 重构loss,重构图片与原图片接近

2 论文实现

2.1 encoder

encoder是常用的图片卷积神经网络
输入x shape [32,3,32,32]
输出 shape [32,128,8,8]

    def __init__(self, in_dim, h_dim, n_res_layers, res_h_dim):
        super(Encoder, self).__init__()
        kernel = 4
        stride = 2
        self.conv_stack = nn.Sequential(
            nn.Conv2d(in_dim, h_dim // 2, kernel_size=kernel,
                      stride=stride, padding=1),
            nn.ReLU(),
            nn.Conv2d(h_dim // 2, h_dim, kernel_size=kernel,
                      stride=stride, padding=1),
            nn.ReLU(),
            nn.Conv2d(h_dim, h_dim, kernel_size=kernel-1,
                      stride=stride-1, padding=1),
            ResidualStack(
                h_dim, h_dim, res_h_dim, n_res_layers)

        )

    def forward(self, x):
        return self.conv_stack(x)

2.2 VectorQuantizer 向量量化层

• 输入：
  为encoder的输出z,shape : [32,64,8,8]
• 码本维度：
  encoder维度变换为[2024,64]，和码本embeddign shape [512,64]计算相似度
• 相似计算：使用$(x?y{)}^2=x^2+y^2?2xy$计算和码本的相似度
• z_q生成
  然后取码本中最相似的向量替换encoder中的向量
• z_1维度：
  得到z_q shape [2024,64],经维度变换 shape [32,64,8,8] ，维度与输入z一致
• 损失函数：
  使 z_q和z接近，构建损失函数
  

decoder 层

decoder层比较简单，与encoder层相反
输入x shape 【32，64，8，8】
输出shape [32,3,32,32]

class Decoder(nn.Module):
    """
    This is the p_phi (x|z) network. Given a latent sample z p_phi 
    maps back to the original space z -> x.

    Inputs:
    - in_dim : the input dimension
    - h_dim : the hidden layer dimension
    - res_h_dim : the hidden dimension of the residual block
    - n_res_layers : number of layers to stack

    """

    def __init__(self, in_dim, h_dim, n_res_layers, res_h_dim):
        super(Decoder, self).__init__()
        kernel = 4
        stride = 2

        self.inverse_conv_stack = nn.Sequential(
            nn.ConvTranspose2d(
                in_dim, h_dim, kernel_size=kernel-1, stride=stride-1, padding=1),
            ResidualStack(h_dim, h_dim, res_h_dim, n_res_layers),
            nn.ConvTranspose2d(h_dim, h_dim // 2,
                               kernel_size=kernel, stride=stride, padding=1),
            nn.ReLU(),
            nn.ConvTranspose2d(h_dim//2, 3, kernel_size=kernel,
                               stride=stride, padding=1)
        )

    def forward(self, x):
        return self.inverse_conv_stack(x)

2.3 损失函数

损失函数为重构损失和embedding损失之和

• decoder 输出为图片重构x_hat
• embedding损失，为encoder和码本的embedding近似损失
• 重点：（decoder计算损失时，由于中间有取最小值，导致梯度不连续，因此decoder loss 不能直接对encocer推荐进行求导，采用了复制梯度的方式： z_q = z + (z_q - z).detach()，及

    for i in range(args.n_updates):
        (x, _) = next(iter(training_loader))
        x = x.to(device)
        optimizer.zero_grad()

        embedding_loss, x_hat, perplexity = model(x)
        recon_loss = torch.mean((x_hat - x)**2) / x_train_var
        loss = recon_loss + embedding_loss

        loss.backward()
        optimizer.step()