text: import torch
  import torch.nn as nn
  import torch.nn.functional as F
  
  from base.base_net import BaseNet
  
  
  class CAMPUS_LeNet(BaseNet):
  
      def __init__(self):
          super().__init__()
  
          self.rep_dim = 100
          self.pool = nn.MaxPool2d(4, 4)
  
          self.conv1 = nn.Conv2d(1, 8, 5, bias=False, padding=2)
          self.bn1 = nn.BatchNorm2d(8, eps=1e-04, affine=False)
          self.conv2 = nn.Conv2d(8, 4, 5, bias=False, padding=2)
          self.bn2 = nn.BatchNorm2d(4, eps=1e-04, affine=False)
          self.conv3 = nn.Conv2d(4, 2, 5, bias=False, padding=2)
          self.bn3= nn.BatchNorm2d(2, eps=1e-04, affine=False)
          self.fc1 = nn.Linear(2 * 10 * 10, self.rep_dim, bias=False)
  
      def forward(self, x):
          x = self.conv1(x)
          x = self.pool(F.leaky_relu(self.bn1(x)))
          x = self.conv2(x)
          x = self.pool(F.leaky_relu(self.bn2(x)))
          x = self.conv3(x)
          x = self.pool(F.leaky_relu(self.bn3(x)))
          x = x.view(x.size(0), -1)
          x = self.fc1(x)
          return x
  
  
  class CAMPUS_LeNet_Autoencoder(BaseNet):
  
      def __init__(self):
          super().__init__()
  
          self.rep_dim = 100
          self.pool = nn.MaxPool2d(4, 4)
  
          # Encoder (must match the Deep SVDD network above)
          self.conv1 = nn.Conv2d(1, 8, 5, bias=False, padding=2)
          self.bn1 = nn.BatchNorm2d(8, eps=1e-04, affine=False)
          self.conv2 = nn.Conv2d(8, 4, 5, bias=False, padding=2)
          self.bn2 = nn.BatchNorm2d(4, eps=1e-04, affine=False)
          self.conv3 = nn.Conv2d(4, 2, 5, bias=False, padding=2)
          self.bn3 = nn.BatchNorm2d(2, eps=1e-04, affine=False)
          self.fc1 = nn.Linear(2 * 10 * 10, self.rep_dim, bias=False)
  
          # Decoder
          self.deconv1 = nn.ConvTranspose2d(1, 2, 5, bias=False, padding=2)
          self.bn3 = nn.BatchNorm2d(2, eps=1e-04, affine=False)
          self.deconv2 = nn.ConvTranspose2d(2, 4, 5, bias=False, padding=2)
          self.bn4 = nn.BatchNorm2d(4, eps=1e-04, affine=False)
          self.deconv3 = nn.ConvTranspose2d(4, 8, 5, bias=False, padding=2)
          self.bn5 = nn.BatchNorm2d(8, eps=1e-04, affine=False)
          self.deconv4 = nn.ConvTranspose2d(8, 1, 5, bias=False, padding=2)
  
      def forward(self, x):
          x = self.conv1(x)
          x = self.pool(F.leaky_relu(self.bn1(x)))
          x = self.conv2(x)
          x = self.pool(F.leaky_relu(self.bn2(x)))
          x = self.conv3(x)
          x = self.pool(F.leaky_relu(self.bn3(x)))
          x = x.view(x.size(0), -1)
          x = self.fc1(x)
          x = x.view(x.size(0), int(self.rep_dim / 100), 10, 10)
          x = F.interpolate(F.leaky_relu(x), scale_factor=1)
          x = self.deconv1(x)
          x = F.interpolate(F.leaky_relu(self.bn3(x)), scale_factor=4)
          x = self.deconv2(x)
          x = F.interpolate(F.leaky_relu(self.bn4(x)), scale_factor=4)
          x = self.deconv3(x)
          x = F.interpolate(F.leaky_relu(self.bn5(x)), scale_factor=4)
          x = self.deconv4(x)
          x = torch.sigmoid(x)
  
          return x
  