models.py

"""
Model ideas implementation
"""
import torch.nn as nn
import torch.nn.functional as F
class EmbeddingNet(nn.Module):
    def __init__(self):
        super(EmbeddingNet, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=1, out_channels=32, kernel_size=5)
        self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=5)
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
        self.fc1 = nn.Linear(in_features=64 * 4 * 4, out_features=256)
        self.fc2 = nn.Linear(in_features=256, out_features=256)
        self.fc3 = nn.Linear(in_features=256, out_features=2)

    def forward(self, x):
        output = self.pool(F.relu(self.conv1(x)))
        output = self.pool(F.relu(self.conv2(output)))
        output = output.view(-1, 64 * 4 * 4)
        output = F.relu(self.fc1(output))
        output = F.relu(self.fc2(output))
        output = self.fc3(output)
        return output

    #def get_embedding(self, x):
    #    return self.forward(x)

class TripletNet(nn.Module):
    def __init__(self, embedding_net):
        super(TripletNet, self).__init__()
        self.embedding_net = embedding_net

    def forward(self, x1, x2, x3):
        output1 = self.embedding_net(x1)
        output2 = self.embedding_net(x2)
        output3 = self.embedding_net(x3)
        return output1, output2, output3

    #def get_embedding(self, x):
    #    return self.embedding_net(x)

class mini_vgg(nn.Module):
    def __init__(self):
        super(mini_vgg, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=1, out_channels=32, kernel_size=3, padding=3)
        self.conv2 = nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3, padding=1)
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
        # 16*16
        self.conv3 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, padding=1)
        self.conv4 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, padding=1)
        self.fc1 = nn.Linear(in_features=64 * 8 * 8, out_features=512)
        self.fc2 = nn.Linear(in_features=512, out_features=10)
    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = self.pool(F.relu(self.conv2(x)))
        x = F.relu(self.conv3(x))
        x = self.pool(F.relu(self.conv4(x)))
        x = x.view(-1, 64 * 8 * 8)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return x

class CNNModel5(nn.Module):
    def __init__(self):
        super(CNNModel5, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=1, out_channels=6, kernel_size=5, padding=2)
        self.bn1 = nn.BatchNorm2d(6)
        self.conv2 = nn.Conv2d(in_channels=6, out_channels=16, kernel_size=5)
        self.bn2 = nn.BatchNorm2d(16)
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)

        self.fc1 = nn.Linear(in_features=16 * 5 * 5, out_features=128)
        self.dropout = nn.Dropout(0.5)
        self.fc2 = nn.Linear(in_features=128, out_features=64)
        self.out = nn.Linear(in_features=64, out_features=10)

    def forward(self, x):
        x = self.pool(F.elu(self.bn1(self.conv1(x))))
        x = self.pool(F.elu(self.bn2(self.conv2(x))))
        x = x.view(-1, 16 * 5 * 5)
        x = F.elu(self.fc1(x))
        x = self.dropout(x)
        x = F.elu(self.fc2(x))
        x = self.dropout(x)
        x = self.out(x)
        return x