import torch
import torch.nn as nn

class SiameseNetwork(nn.Module):
    def __init__(self):
        super(SiameseNetwork, self).__init__()

        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),
        )

        self.fc1 = nn.Sequential(
            nn.Linear(25600, 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):
        output = self.cnn1(x)
        output = output.view(output.size()[0], -1)
        output = self.fc1(output)
        return output

    def forward(self, input1, input2):
        output1 = self.forward_once(input1)
        output2 = self.forward_once(input2)
        return output1, output2

# Function to load the trained model
def load_model(model_path):
    model = SiameseNetwork()
    model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))
    model.eval()
    return model