稀疏卷积去噪自编码器pytorch

以下是使用PyTorch实现稀疏卷积去噪自编码器的示例代码：

import torch
import torch.nn as nn
import torch.nn.functional as F

class SparseConvAutoencoder(nn.Module):
    def __init__(self):
        super(SparseConvAutoencoder, self).__init__()
        
        # Encoder
        self.conv1 = nn.Conv2d(1, 16, kernel_size=3, stride=2, padding=1)
        self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=2, padding=1)
        self.conv3 = nn.Conv2d(32, 64, kernel_size=3, stride=2, padding=1)
        
        # Decoder
        self.deconv1 = nn.ConvTranspose2d(64, 32, kernel_size=3, stride=2, padding=1, output_padding=1)
        self.deconv2 = nn.ConvTranspose2d(32, 16, kernel_size=3, stride=2, padding=1, output_padding=1)
        self.deconv3 = nn.ConvTranspose2d(16, 1, kernel_size=3, stride=2, padding=1, output_padding=1)
        
        # Sparse Convolution
        self.sparseconv1 = nn.Conv2d(16, 16, kernel_size=3, stride=1, padding=1, bias=False, groups=16)
        self.sparseconv2 = nn.Conv2d(32, 32, kernel_size=3, stride=1, padding=1, bias=False, groups=32)
        self.sparseconv3 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=False, groups=64)
        
    def forward(self, x):
        # Encoder
        x = F.relu(self.conv1(x))
        x = F.relu(self.sparseconv1(x))
        x = F.relu(self.conv2(x))
        x = F.relu(self.sparseconv2(x))
        x = F.relu(self.conv3(x))
        x = F.relu(self.sparseconv3(x))
        
        # Decoder
        x = F.relu(self.deconv1(x))
        x = F.relu(self.deconv2(x))
        x = torch.sigmoid(self.deconv3(x))
        
        return x

在这个模型中，我们使用了三个卷积层作为编码器，以及三个反卷积层作为解码器。为了实现稀疏卷积，我们还添加了三个稀疏卷积层，它们的groups参数设置为输出通道数，这样每个输出通道只与输入的一个子集通道进行卷积，从而实现了稀疏卷积。

在前向传播中，我们先对输入进行编码，然后对编码结果进行解码，并使用sigmoid激活函数将输出限制在[0,1]范围内。