import torch import torch.nn as nn import torch.nn.functional as F from base.base_net import BaseNet class CIFAR10_LeNet(BaseNet): def __init__(self, rep_dim=128): super().__init__() self.rep_dim = rep_dim self.pool = nn.MaxPool2d(2, 2) self.conv1 = nn.Conv2d(3, 32, 5, bias=False, padding=2) self.bn2d1 = nn.BatchNorm2d(32, eps=1e-04, affine=False) self.conv2 = nn.Conv2d(32, 64, 5, bias=False, padding=2) self.bn2d2 = nn.BatchNorm2d(64, eps=1e-04, affine=False) self.conv3 = nn.Conv2d(64, 128, 5, bias=False, padding=2) self.bn2d3 = nn.BatchNorm2d(128, eps=1e-04, affine=False) self.fc1 = nn.Linear(128 * 4 * 4, self.rep_dim, bias=False) def forward(self, x): x = x.view(-1, 3, 32, 32) x = self.conv1(x) x = self.pool(F.leaky_relu(self.bn2d1(x))) x = self.conv2(x) x = self.pool(F.leaky_relu(self.bn2d2(x))) x = self.conv3(x) x = self.pool(F.leaky_relu(self.bn2d3(x))) x = x.view(int(x.size(0)), -1) x = self.fc1(x) return x class CIFAR10_LeNet_Decoder(BaseNet): def __init__(self, rep_dim=128): super().__init__() self.rep_dim = rep_dim self.deconv1 = nn.ConvTranspose2d(int(self.rep_dim / (4 * 4)), 128, 5, bias=False, padding=2) nn.init.xavier_uniform_(self.deconv1.weight, gain=nn.init.calculate_gain('leaky_relu')) self.bn2d4 = nn.BatchNorm2d(128, eps=1e-04, affine=False) self.deconv2 = nn.ConvTranspose2d(128, 64, 5, bias=False, padding=2) nn.init.xavier_uniform_(self.deconv2.weight, gain=nn.init.calculate_gain('leaky_relu')) self.bn2d5 = nn.BatchNorm2d(64, eps=1e-04, affine=False) self.deconv3 = nn.ConvTranspose2d(64, 32, 5, bias=False, padding=2) nn.init.xavier_uniform_(self.deconv3.weight, gain=nn.init.calculate_gain('leaky_relu')) self.bn2d6 = nn.BatchNorm2d(32, eps=1e-04, affine=False) self.deconv4 = nn.ConvTranspose2d(32, 3, 5, bias=False, padding=2) nn.init.xavier_uniform_(self.deconv4.weight, gain=nn.init.calculate_gain('leaky_relu')) def forward(self, x): x = x.view(int(x.size(0)), int(self.rep_dim / (4 * 4)), 4, 4) x = F.leaky_relu(x) x = self.deconv1(x) x = F.interpolate(F.leaky_relu(self.bn2d4(x)), scale_factor=2) x = self.deconv2(x) x = F.interpolate(F.leaky_relu(self.bn2d5(x)), scale_factor=2) x = self.deconv3(x) x = F.interpolate(F.leaky_relu(self.bn2d6(x)), scale_factor=2) x = self.deconv4(x) x = torch.sigmoid(x) return x class CIFAR10_LeNet_Autoencoder(BaseNet): def __init__(self, rep_dim=128): super().__init__() self.rep_dim = rep_dim self.encoder = CIFAR10_LeNet(rep_dim=rep_dim) self.decoder = CIFAR10_LeNet_Decoder(rep_dim=rep_dim) def forward(self, x): x = self.encoder(x) x = self.decoder(x) return x