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# MIT License
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# Copyright (c) 2024 Ontolearn Team
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# -----------------------------------------------------------------------------
import torch, torch.nn as nn
import random
from typing import List
from ontolearn.nces_modules import *
[docs]
class LengthLearner_LSTM(nn.Module):
"""LSTM architecture"""
def __init__(self, input_size, output_size, proj_dim=256, rnn_n_layers=2, drop_prob=0.2):
super().__init__()
self.name = 'LSTM'
self.loss = nn.CrossEntropyLoss()
self.lstm = nn.LSTM(input_size, proj_dim, rnn_n_layers,
dropout=drop_prob, batch_first=True)
self.dropout = nn.Dropout(drop_prob)
self.fc1 = nn.Linear(2*proj_dim, proj_dim)
self.fc2 = nn.Linear(proj_dim, proj_dim)
self.fc3 = nn.Linear(proj_dim, output_size)
[docs]
def forward(self, x1, x2):
''' Forward pass through the network.'''
x1, _ = self.lstm(x1)
x1 = x1.sum(1).contiguous().view(x1.shape[0], -1)
x2, _ = self.lstm(x2)
x2 = x2.sum(1).contiguous().view(x2.shape[0], -1)
x = torch.cat([x1, x2], dim=-1)
x = self.fc1(x)
x = torch.selu(x)
x = self.dropout(x)
x = self.fc2(x)
x = x + torch.tanh(x)
x = self.fc3(x)
return x
[docs]
class LengthLearner_GRU(nn.Module):
"""GRU architecture"""
def __init__(self, input_size, output_size, proj_dim=256, rnn_n_layers=2, drop_prob=0.2):
super().__init__()
self.name = 'GRU'
self.loss = nn.CrossEntropyLoss()
self.gru = nn.GRU(input_size, proj_dim, rnn_n_layers,
dropout=drop_prob, batch_first=True)
self.dropout = nn.Dropout(drop_prob)
self.fc1 = nn.Linear(2*proj_dim, proj_dim)
self.fc2 = nn.Linear(proj_dim, proj_dim)
self.fc3 = nn.Linear(proj_dim, output_size)
[docs]
def forward(self, x1, x2):
''' Forward pass through the network.'''
x1, _ = self.gru(x1)
x1 = x1.sum(1).contiguous().view(x1.shape[0], -1)
x2, _ = self.gru(x2)
x2 = x2.sum(1).contiguous().view(x2.shape[0], -1)
x = torch.cat([x1, x2], dim=-1)
x = self.fc1(x)
x = torch.selu(x)
x = self.dropout(x)
x = self.fc2(x)
x = x + torch.tanh(x)
x = self.fc3(x)
return x
[docs]
class LengthLearner_CNN(nn.Module):
"""CNN architecture"""
def __init__(self, input_size, output_size, num_examples, proj_dim=256, kernel_size: list=[[5,7], [5,7]], stride: list=[[3,3], [3,3]], drop_prob=0.2):
super().__init__()
assert isinstance(kernel_size, list) and isinstance(kernel_size[0], list), "kernel size and stride must be lists of lists, e.g., [[5,7], [5,7]]"
self.name = 'CNN'
self.loss = nn.CrossEntropyLoss()
self.conv1 = nn.Conv2d(in_channels=1, out_channels=4, kernel_size=(kernel_size[0][0], kernel_size[0][1]), stride=(stride[0][0], stride[0][1]), padding=(0,0))
self.conv2 = nn.Conv2d(in_channels=4, out_channels=8, kernel_size=(kernel_size[1][0], kernel_size[1][1]), stride=(stride[1][0], stride[1][1]), padding=(0,0))
self.dropout1d = nn.Dropout(drop_prob)
self.dropout2d = nn.Dropout2d(drop_prob)
conv_out_dim = 3536
self.fc1 = nn.Linear(conv_out_dim, proj_dim)
self.fc2 = nn.Linear(proj_dim, proj_dim)
self.fc3 = nn.Linear(proj_dim, output_size)
[docs]
def forward(self, x1, x2):
''' Forward pass through the network.'''
x1 = x1.unsqueeze(1)
x2 = x2.unsqueeze(1)
x = torch.cat([x1, x2], dim=-2)
x = self.conv1(x)
x = torch.selu(x)
x = self.dropout2d(x)
x = self.conv2(x)
x = x.view(x.shape[0], -1)
x = self.fc1(x)
x = torch.selu(x)
x = self.dropout1d(x)
x = self.fc2(x)
x = x + torch.tanh(x)
x = self.fc3(x)
return x