Add files via upload

Model.py

@@ -1,52 +1,52 @@
-from torchvision import models
-import torch.nn as nn
-
-class model(nn.Module):
-	def __init__(self, input_dim, output_dim):
-		super(model, self).__init__()
-		self.restored = False
-		self.input_dim = input_dim
-		self.output_dim = output_dim
-
-		num = len(input_dim)
-		feature = []
-
-		for i in range(num):
-			feature.append(
-			nn.Sequential(
-			nn.Linear(self.input_dim[i],2*self.input_dim[i]),
-			nn.BatchNorm1d(2*self.input_dim[i]),
-			nn.LeakyReLU(0.1, True),
-			nn.Linear(2*self.input_dim[i],2*self.input_dim[i]),
-			nn.BatchNorm1d(2*self.input_dim[i]),
-			nn.LeakyReLU(0.1, True),
-			nn.Linear(2*self.input_dim[i],self.input_dim[i]),
-			nn.BatchNorm1d(self.input_dim[i]),
-			nn.LeakyReLU(0.1, True),
-			nn.Linear(self.input_dim[i],self.output_dim),
-			nn.BatchNorm1d(self.output_dim),
-			nn.LeakyReLU(0.1, True),
-		))
-
-		self.feature = nn.ModuleList(feature)
-
-		self.feature_show = nn.Sequential(
-			nn.Linear(self.output_dim,self.output_dim),
-			nn.BatchNorm1d(self.output_dim),
-			nn.LeakyReLU(0.1, True),
-			nn.Linear(self.output_dim,self.output_dim),
-			nn.BatchNorm1d(self.output_dim),
-			nn.LeakyReLU(0.1, True),
-			nn.Linear(self.output_dim,self.output_dim),
-		)
-
-	def forward(self, input_data, domain):
-		feature = self.feature[domain](input_data)
-		feature = self.feature_show(feature)
-
-		return feature
-
-
-
-
-
+from torchvision import models
+import torch.nn as nn
+
+class model(nn.Module):
+	def __init__(self, input_dim, output_dim):
+		super(model, self).__init__()
+		self.restored = False
+		self.input_dim = input_dim
+		self.output_dim = output_dim
+
+		num = len(input_dim)
+		feature = []
+
+		for i in range(num):
+			feature.append(
+			nn.Sequential(
+			nn.Linear(self.input_dim[i],2*self.input_dim[i]),
+			nn.BatchNorm1d(2*self.input_dim[i]),
+			nn.LeakyReLU(0.1, True),
+			nn.Linear(2*self.input_dim[i],2*self.input_dim[i]),
+			nn.BatchNorm1d(2*self.input_dim[i]),
+			nn.LeakyReLU(0.1, True),
+			nn.Linear(2*self.input_dim[i],self.input_dim[i]),
+			nn.BatchNorm1d(self.input_dim[i]),
+			nn.LeakyReLU(0.1, True),
+			nn.Linear(self.input_dim[i],self.output_dim),
+			nn.BatchNorm1d(self.output_dim),
+			nn.LeakyReLU(0.1, True),
+		))
+
+		self.feature = nn.ModuleList(feature)
+
+		self.feature_show = nn.Sequential(
+			nn.Linear(self.output_dim,self.output_dim),
+			nn.BatchNorm1d(self.output_dim),
+			nn.LeakyReLU(0.1, True),
+			nn.Linear(self.output_dim,self.output_dim),
+			nn.BatchNorm1d(self.output_dim),
+			nn.LeakyReLU(0.1, True),
+			nn.Linear(self.output_dim,self.output_dim),
+		)
+
+	def forward(self, input_data, domain):
+		feature = self.feature[domain](input_data)
+		feature = self.feature_show(feature)
+
+		return feature
+
+
+
+
+