network_sign.py

#create a siamese network
class SiameseNetwork(nn.Module):
    def __init__(self):
        super(SiameseNetwork, self).__init__()
        # Setting up the Sequential of CNN Layers
        self.cnn1 = nn.Sequential(
            nn.Conv2d(1, 96, kernel_size=11,stride=1),
            nn.ReLU(inplace=True),
            nn.LocalResponseNorm(5,alpha=0.0001,beta=0.75,k=2),
            nn.MaxPool2d(3, stride=2),
            
            nn.Conv2d(96, 256, kernel_size=5,stride=1,padding=2),
            nn.ReLU(inplace=True),
            nn.LocalResponseNorm(5,alpha=0.0001,beta=0.75,k=2),
            nn.MaxPool2d(3, stride=2),
            nn.Dropout2d(p=0.3),

            nn.Conv2d(256,384 , kernel_size=3,stride=1,padding=1),
            nn.ReLU(inplace=True),
            
            nn.Conv2d(384,256 , kernel_size=3,stride=1,padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(3, stride=2),
            nn.Dropout2d(p=0.3),
        )
        # Defining the fully connected layers
        self.fc1 = nn.Sequential(
            nn.Linear(30976, 1024),
            nn.ReLU(inplace=True),
            nn.Dropout2d(p=0.5),
            
            nn.Linear(1024, 128),
            nn.ReLU(inplace=True),
            
            nn.Linear(128,2))
        
    def forward_once(self, x):
        # Forward pass 
        output = self.cnn1(x)
        output = output.view(output.size()[0], -1)
        output = self.fc1(output)
        return output

    def forward(self, input1, input2):
        # forward pass of input 1
        output1 = self.forward_once(input1)
        # forward pass of input 2
        output2 = self.forward_once(input2)
        return output1, output2