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networks.py
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Tue, Jan 28, 16:45

networks.py
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import torch.nn as nn
import torch.nn.functional as F
# class EmbeddingNet(nn.Module):
# def __init__(self):
# super(EmbeddingNet, self).__init__()
# self.convnet = nn.Sequential(nn.Conv2d(1, 32, 5), nn.PReLU(),
# nn.MaxPool2d(2, stride=2),
# nn.Conv2d(32, 64, 5), nn.PReLU(),
# nn.MaxPool2d(2, stride=2))
# self.fc = nn.Sequential(nn.Linear(64 * 4 * 4, 256),
# nn.PReLU(),
# nn.Linear(256, 256),
# nn.PReLU(),
# nn.Linear(256, 2)
# )
# def forward(self, x):
# output = self.convnet(x)
# #print(output.shape)
# output = output.view(output.size()[0], -1)
# output = self.fc(output)
# return output
# def get_embedding(self, x):
# return self.forward(x)
# class EmbeddingNet(nn.Module):
# def __init__(self,dropout):
# super(EmbeddingNet, self).__init__()
# self.convnet = nn.Sequential(nn.Conv2d(in_channels=3, out_channels=4, kernel_size=3),
# nn.BatchNorm2d(4),
# nn.ReLU(),
# nn.MaxPool2d(2,2),
# nn.Dropout(dropout),
# nn.Conv2d(in_channels=4, out_channels=8, kernel_size=3),
# nn.BatchNorm2d(8),
# nn.ReLU(),
# nn.MaxPool2d(2,2),
# nn.Dropout(dropout),
# nn.Conv2d(in_channels=8, out_channels=16, kernel_size=3),
# nn.BatchNorm2d(16),
# nn.ReLU(),
# nn.MaxPool2d(2,2),
# nn.Dropout(dropout),
# nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3),
# nn.BatchNorm2d(32),
# nn.ReLU(),
# nn.MaxPool2d(2,2),
# nn.Dropout(dropout))
# self.fc = nn.Sequential(nn.Linear(32 *18 * 28, 2096),
# nn.ReLU(),
# nn.Dropout(dropout),
# nn.Linear(2096, 512),
# nn.ReLU(),
# nn.Dropout(dropout),
# nn.Linear(512, 64),
# nn.ReLU(),
# nn.Linear(64, 4)
# )
# def forward(self, x):
# output = self.convnet(x)
# output = output.view(-1, 32 *28 * 18)
# output = self.fc(output)
# return output
# def get_embedding(self, x):
# return self.forward(x)
class EmbeddingNet(nn.Module):
def __init__(self,dropout):
super(EmbeddingNet, self).__init__()
self.convnet = nn.Sequential(nn.Conv2d(in_channels=3, out_channels=4, kernel_size=3),
nn.BatchNorm2d(4),
nn.ReLU(),
nn.MaxPool2d(2,2),
nn.Dropout(dropout),
nn.Conv2d(in_channels=4, out_channels=8, kernel_size=3),
nn.BatchNorm2d(8),
nn.ReLU(),
nn.MaxPool2d(2,2),
nn.Dropout(dropout),
nn.Conv2d(in_channels=8, out_channels=16, kernel_size=3),
nn.BatchNorm2d(16),
nn.ReLU(),
nn.MaxPool2d(2,2),
nn.Dropout(dropout),
nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3),
nn.BatchNorm2d(32),
nn.ReLU(),
nn.MaxPool2d(2,2),
nn.Dropout(dropout),
nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3),
nn.BatchNorm2d(64),
nn.ReLU(),
nn.MaxPool2d(2,2),
nn.Dropout(dropout))
self.fc = nn.Sequential(nn.Linear(64 *8 * 13, 512),
nn.ReLU(),
nn.Dropout(dropout),
nn.Linear(512, 64),
nn.ReLU(),
nn.Dropout(dropout),
nn.Linear(64, 4)
)
def forward(self, x):
output = self.convnet(x)
output = output.view(-1, 64 *8 * 13)
output = self.fc(output)
return output
def get_embedding(self, x):
return self.forward(x)
class SiameseNet(nn.Module):
def __init__(self, embedding_net):
super(SiameseNet, self).__init__()
self.embedding_net = embedding_net
def forward(self, x1, x2):
output1 = self.embedding_net(x1)
output2 = self.embedding_net(x2)
return output1, output2
def get_embedding(self, x):
return self.embedding_net(x)

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