Pytorch 卷积自动编码器

Question

如何构建卷积自动编码器的解码器部分？假设我有这个

(input -> conv2d -> maxpool2d -> maxunpool2d -> convTranspose2d -> output):

# CIFAR images shape = 3 x 32 x 32

class ConvDAE(nn.Module):
    def __init__(self):
        super().__init__()

        # input: batch x 3 x 32 x 32 -> output: batch x 16 x 16 x 16
        self.encoder = nn.Sequential(
            nn.Conv2d(3, 16, 3, stride=1, padding=1), # batch x 16 x 32 x 32
            nn.ReLU(),
            nn.BatchNorm2d(16),
            nn.MaxPool2d(2, stride=2) # batch x 16 x 16 x 16
        )

        # input: batch x 16 x 16 x 16 -> output: batch x 3 x 32 x 32
        self.decoder = nn.Sequential(
            # this line does not work
            # nn.MaxUnpool2d(2, stride=2, padding=0), # batch x 16 x 32 x 32
            nn.ConvTranspose2d(16, 16, 3, stride=2, padding=1, output_padding=1), # batch x 16 x 32 x 32
            nn.ReLU(),
            nn.BatchNorm2d(16),
            nn.ConvTranspose2d(16, 3, 3, stride=1, padding=1, output_padding=0), # batch x 3 x 32 x 32
            nn.ReLU()
        )

    def forward(self, x):
        print(x.size())
        out = self.encoder(x)
        print(out.size())
        out = self.decoder(out)
        print(out.size())
        return out

Pytorch 特定问题：为什么我不能在解码器部分使用 MaxUnpool2d 。这给了我以下错误：

TypeError: forward() missing 1 required positional argument: 'indices'

And the conceptual question: Shouldn't we do in decoder inverse of whatever we did in encoder? I saw some implementations and it seems they only care about the dimensions of input and output of decoder. Here and here are some examples.

Answer 1

对于问题的火炬部分，unpool 模块将池模块返回的索引作为必需的位置参数，该索引将通过return_indices=True. 所以你可以做

class ConvDAE(nn.Module):
    def __init__(self):
        super().__init__()

        # input: batch x 3 x 32 x 32 -> output: batch x 16 x 16 x 16
        self.encoder = nn.Sequential(
            nn.Conv2d(3, 16, 3, stride=1, padding=1), # batch x 16 x 32 x 32
            nn.ReLU(),
            nn.BatchNorm2d(16),
            nn.MaxPool2d(2, stride=2, return_indices=True)
        )

        self.unpool = nn.MaxUnpool2d(2, stride=2, padding=0)

        self.decoder = nn.Sequential( 
            nn.ConvTranspose2d(16, 16, 3, stride=2, padding=1, output_padding=1), 
            nn.ReLU(),
            nn.BatchNorm2d(16),
            nn.ConvTranspose2d(16, 3, 3, stride=1, padding=1, output_padding=0), 
            nn.ReLU()
        )

    def forward(self, x):
        print(x.size())
        out, indices = self.encoder(x)
        out = self.unpool(out, indices)
        out = self.decoder(out)
        print(out.size())
        return out

至于问题的一般部分，我认为最先进的技术不是使用对称解码器部分，因为已经表明，去卷积/转置卷积会产生棋盘效应，并且许多方法倾向于使用上采样模块。您将通过 PyTorch 渠道更快地找到更多信息。