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import torch
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import torch.nn as nn
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class SiameseNetwork(nn.Module):
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def __init__(self):
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super(SiameseNetwork, self).__init__()
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self.cnn1 = nn.Sequential(
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nn.Conv2d(1, 96, kernel_size=11, stride=1),
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nn.ReLU(inplace=True),
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nn.LocalResponseNorm(5, alpha=0.0001, beta=0.75, k=2),
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nn.MaxPool2d(3, stride=2),
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nn.Conv2d(96, 256, kernel_size=5, stride=1, padding=2),
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nn.ReLU(inplace=True),
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nn.LocalResponseNorm(5, alpha=0.0001, beta=0.75, k=2),
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nn.MaxPool2d(3, stride=2),
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nn.Dropout2d(p=0.3),
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nn.Conv2d(256, 384, kernel_size=3, stride=1, padding=1),
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nn.ReLU(inplace=True),
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nn.Conv2d(384, 256, kernel_size=3, stride=1, padding=1),
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nn.ReLU(inplace=True),
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nn.MaxPool2d(3, stride=2),
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nn.Dropout2d(p=0.3),
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)
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self.fc1 = nn.Sequential(
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nn.Linear(25600, 1024),
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nn.ReLU(inplace=True),
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nn.Dropout2d(p=0.5),
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nn.Linear(1024, 128),
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nn.ReLU(inplace=True),
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nn.Linear(128, 2)
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)
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def forward_once(self, x):
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output = self.cnn1(x)
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output = output.view(output.size()[0], -1)
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output = self.fc1(output)
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return output
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def forward(self, input1, input2):
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output1 = self.forward_once(input1)
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output2 = self.forward_once(input2)
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return output1, output2
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def load_model(model_path):
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model = SiameseNetwork()
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model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))
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model.eval()
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return model
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