Source code for ontolearn.nces_modules

"""NCES modules."""
# From https://github.com/juho-lee/set_transformer

import torch
import torch.nn as nn
import torch.nn.functional as F
import math


[docs] class MAB(nn.Module): """MAB module.""" def __init__(self, dim_Q, dim_K, dim_V, num_heads, ln=False): super(MAB, self).__init__() self.dim_V = dim_V self.num_heads = num_heads self.fc_q = nn.Linear(dim_Q, dim_V) self.fc_k = nn.Linear(dim_K, dim_V) self.fc_v = nn.Linear(dim_K, dim_V) if ln: self.ln0 = nn.LayerNorm(dim_V) self.ln1 = nn.LayerNorm(dim_V) self.fc_o = nn.Linear(dim_V, dim_V)
[docs] def forward(self, Q, K): Q = self.fc_q(Q) K, V = self.fc_k(K), self.fc_v(K) dim_split = self.dim_V // self.num_heads Q_ = torch.cat(Q.split(dim_split, 2), 0) K_ = torch.cat(K.split(dim_split, 2), 0) V_ = torch.cat(V.split(dim_split, 2), 0) A = torch.softmax(Q_.bmm(K_.transpose(1, 2))/math.sqrt(self.dim_V), 2) O = torch.cat((Q_ + A.bmm(V_)).split(Q.size(0), 0), 2) O = O if getattr(self, 'ln0', None) is None else self.ln0(O) O = O + F.relu(self.fc_o(O)) O = O if getattr(self, 'ln1', None) is None else self.ln1(O) return O
[docs] class SAB(nn.Module): """SAB module.""" def __init__(self, dim_in, dim_out, num_heads, ln=False): super(SAB, self).__init__() self.mab = MAB(dim_in, dim_in, dim_out, num_heads, ln=ln)
[docs] def forward(self, X): return self.mab(X, X)
[docs] class ISAB(nn.Module): """ISAB module.""" def __init__(self, dim_in, dim_out, num_heads, num_inds, ln=False): super(ISAB, self).__init__() self.I = nn.Parameter(torch.Tensor(1, num_inds, dim_out)) nn.init.xavier_uniform_(self.I) self.mab0 = MAB(dim_out, dim_in, dim_out, num_heads, ln=ln) self.mab1 = MAB(dim_in, dim_out, dim_out, num_heads, ln=ln)
[docs] def forward(self, X): H = self.mab0(self.I.repeat(X.size(0), 1, 1), X) return self.mab1(X, H)
[docs] class PMA(nn.Module): """PMA module.""" def __init__(self, dim, num_heads, num_seeds, ln=False): super(PMA, self).__init__() self.S = nn.Parameter(torch.Tensor(1, num_seeds, dim)) nn.init.xavier_uniform_(self.S) self.mab = MAB(dim, dim, dim, num_heads, ln=ln)
[docs] def forward(self, X): return self.mab(self.S.repeat(X.size(0), 1, 1), X)