71 lines
2.0 KiB
Python
71 lines
2.0 KiB
Python
import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from base.base_net import BaseNet
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class SubTer_LeNet(BaseNet):
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def __init__(self, rep_dim=1024):
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super().__init__()
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self.rep_dim = rep_dim
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self.pool = nn.MaxPool2d(2, 2)
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self.conv1 = nn.Conv2d(1, 8, 5, bias=False, padding=2)
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self.bn1 = nn.BatchNorm2d(8, eps=1e-04, affine=False)
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self.conv2 = nn.Conv2d(8, 4, 5, bias=False, padding=2)
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self.bn2 = nn.BatchNorm2d(4, eps=1e-04, affine=False)
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self.fc1 = nn.Linear(4 * 512 * 8, self.rep_dim, bias=False)
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def forward(self, x):
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x = x.view(-1, 1, 32, 2048)
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x = self.conv1(x)
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x = self.pool(F.leaky_relu(self.bn1(x)))
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x = self.conv2(x)
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x = self.pool(F.leaky_relu(self.bn2(x)))
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x = x.view(int(x.size(0)), -1)
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x = self.fc1(x)
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return x
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class SubTer_LeNet_Decoder(BaseNet):
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def __init__(self, rep_dim=1024):
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super().__init__()
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self.rep_dim = rep_dim
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# Decoder network
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self.fc3 = nn.Linear(self.rep_dim, 4 * 512 * 8, bias=False)
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self.bn3 = nn.BatchNorm2d(4, eps=1e-04, affine=False)
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self.deconv1 = nn.ConvTranspose2d(4, 8, 5, bias=False, padding=2)
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self.bn4 = nn.BatchNorm2d(8, eps=1e-04, affine=False)
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self.deconv2 = nn.ConvTranspose2d(8, 1, 5, bias=False, padding=2)
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def forward(self, x):
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x = self.fc3(x)
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x = x.view(int(x.size(0)), 4, 8, 512)
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x = F.interpolate(F.leaky_relu(self.bn3(x)), scale_factor=2)
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x = self.deconv1(x)
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x = F.interpolate(F.leaky_relu(self.bn4(x)), scale_factor=2)
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x = self.deconv2(x)
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x = torch.sigmoid(x)
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return x
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class SubTer_LeNet_Autoencoder(BaseNet):
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def __init__(self, rep_dim=1024):
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super().__init__()
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self.rep_dim = rep_dim
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self.encoder = SubTer_LeNet(rep_dim=rep_dim)
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self.decoder = SubTer_LeNet_Decoder(rep_dim=rep_dim)
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def forward(self, x):
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x = self.encoder(x)
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x = self.decoder(x)
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return x
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