initial work for elpv and subter datasets

elpv as example dataset/implementation
subter with final dataset

Deep-SAD-PyTorch/src/networks/subter_LeNet.py

@@ -0,0 +1,70 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from base.base_net import BaseNet
+
+
+class SubTer_LeNet(BaseNet):
+
+    def __init__(self, rep_dim=1024):
+        super().__init__()
+
+        self.rep_dim = rep_dim
+        self.pool = nn.MaxPool2d(2, 2)
+
+        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.fc1 = nn.Linear(4 * 512 * 8, self.rep_dim, bias=False)
+
+    def forward(self, x):
+        x = x.view(-1, 1, 32, 2048)
+        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 = x.view(int(x.size(0)), -1)
+        x = self.fc1(x)
+        return x
+
+
+class SubTer_LeNet_Decoder(BaseNet):
+
+    def __init__(self, rep_dim=1024):
+        super().__init__()
+
+        self.rep_dim = rep_dim
+
+        # Decoder network
+        self.fc3 = nn.Linear(self.rep_dim, 4 * 512 * 8, bias=False)
+        self.bn3 = nn.BatchNorm2d(4, eps=1e-04, affine=False)
+        self.deconv1 = nn.ConvTranspose2d(4, 8, 5, bias=False, padding=2)
+        self.bn4 = nn.BatchNorm2d(8, eps=1e-04, affine=False)
+        self.deconv2 = nn.ConvTranspose2d(8, 1, 5, bias=False, padding=2)
+
+    def forward(self, x):
+        x = self.fc3(x)
+        x = x.view(int(x.size(0)), 4, 8, 512)
+        x = F.interpolate(F.leaky_relu(self.bn3(x)), scale_factor=2)
+        x = self.deconv1(x)
+        x = F.interpolate(F.leaky_relu(self.bn4(x)), scale_factor=2)
+        x = self.deconv2(x)
+        x = torch.sigmoid(x)
+        return x
+
+
+class SubTer_LeNet_Autoencoder(BaseNet):
+
+    def __init__(self, rep_dim=1024):
+        super().__init__()
+
+        self.rep_dim = rep_dim
+        self.encoder = SubTer_LeNet(rep_dim=rep_dim)
+        self.decoder = SubTer_LeNet_Decoder(rep_dim=rep_dim)
+
+    def forward(self, x):
+        x = self.encoder(x)
+        x = self.decoder(x)
+        return x