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import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
import numpy as np
from utils import pickle_load
class GraphConvolution(nn.Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
"""
def __init__(self, in_features, out_features, bias=True):
super(GraphConvolution, self).__init__()
self.in_features = in_features
self.out_features = out_features
self.weight = nn.Linear(in_features, out_features)
'''
self.weight = Parameter(torch.FloatTensor(in_features, out_features))
if bias:
self.bias = Parameter(torch.FloatTensor(out_features))
else:
self.register_parameter('bias', None)
self.reset_parameters()
'''
self.adj = torch.FloatTensor(np.load('./data/movie/kg_adj_mat.npy'))
def reset_parameters(self):
stdv = 1. / math.sqrt(self.weight.size(1))
self.weight.data.uniform_(-stdv, stdv)
if self.bias is not None:
self.bias.data.uniform_(-stdv, stdv)
def forward(self, input):
#support = torch.mm(input, self.weight)
support = self.weight(input)
output = torch.spmm(self.adj, support)
return output
'''
if self.bias is not None:
return output + self.bias
else:
return output
'''
def __repr__(self):
return self.__class__.__name__ + ' (' \
+ str(self.in_features) + ' -> ' \
+ str(self.out_features) + ')'
class GCN_GRU(nn.Module):
def __init__(self, config, nfeat, entity_vocab, relation_vocab):
super(GCN_GRU, self).__init__()
# self.kge_model.entity_embedding = torch.nn.Embedding(entity_max, nfeat)
# uniform_range = 6 / np.sqrt(nfeat)
# self.kge_model.entity_embedding.weight.data.uniform_(-uniform_range, uniform_range)
# self.relation_emb = torch.nn.Embedding(relation_max, nfeat)
# uniform_range = 6 / np.sqrt(nfeat)
# self.relation_emb.weight.data.uniform_(-uniform_range, uniform_range)
entity_max = max(entity_vocab.values())
relation_max = max(relation_vocab.values())
self.n_hop_kg = pickle_load(f'{config.preprocess_results_dir}/n_hop_kg.pkl')
self.kge_model = KGEModel(
model_name="TransE",
nentity=entity_max,
nrelation=relation_max,
hidden_dim=nfeat,
gamma=12.0,
double_entity_embedding=True,
double_relation_embedding=True,
evaluator=None
)
self.gc1 = GraphConvolution(nfeat, nfeat)
self.gc2 = GraphConvolution(nfeat, nfeat)
self.h = torch.randn(2,1,20)
self.gru = nn.GRU(50, 20, 2)
self.criterion = nn.MarginRankingLoss(margin=1.0)
def get_n_hop(self, entity_id):
return self.n_hop_kg[entity_id]
def distance(self, triplets):
assert triplets.size()[1] == 3
heads = triplets[:, 0]
relations = triplets[:, 1]
tails = triplets[:, 2]
return (self.kge_model.entity_embedding.weight[heads] +
self.relation_emb.weight[relations] -
self.kge_model.entity_embedding.weight[tails]).norm(p=1, dim=1)
def TransE_forward(self, pos_triplet, neg_triplet):
# -1 to avoid nan for OOV vector
self.kge_model.entity_embedding.weight.data[:-1, :].div_(self.kge_model.entity_embedding.weight.data[:-1, :].norm(p=2, dim=1, keepdim=True))
pos_distance = self.distance(pos_triplet)
neg_distance = self.distance(neg_triplet)
target = torch.tensor([-1], dtype=torch.long)
return self.criterion(pos_distance, neg_distance, target)
def forward_GCN(self, x):
# GCN
out = F.relu(self.gc1(self.kge_model.entity_embedding.weight))
out = self.gc2(out)
out = F.log_softmax(out, dim=1)[x]
return out
def forward(self, x):
# GCN
out = F.relu(self.gc1(self.kge_model.entity_embedding.weight))
out = self.gc2(out)
out = F.log_softmax(out, dim=1)[x].reshape(1,1,-1)
# GRU
out, self.h = self.gru(out, self.h)
out = out.reshape(-1)
return out
class GRU(nn.Module):
def __init__(self, config, nfeat, entity_vocab, relation_vocab):
super(GRU, self).__init__()
entity_max = max(entity_vocab.values())
relation_max = max(relation_vocab.values())
self.n_hop_kg = pickle_load(f'{config.preprocess_results_dir}/n_hop_kg.pkl')
self.kge_model.entity_embedding = torch.nn.Embedding(entity_max, nfeat)
uniform_range = 6 / np.sqrt(nfeat)
self.kge_model.entity_embedding.weight.data.uniform_(-uniform_range, uniform_range)
self.relation_emb = torch.nn.Embedding(relation_max, nfeat)
uniform_range = 6 / np.sqrt(nfeat)
self.relation_emb.weight.data.uniform_(-uniform_range, uniform_range)
self.h = torch.randn(2,1,20)
self.gru = nn.GRU(50, 20, 2)
self.criterion = nn.MarginRankingLoss(margin=1.0)
def get_n_hop(self, entity_id):
return self.n_hop_kg[entity_id]
def distance(self, triplets):
assert triplets.size()[1] == 3
heads = triplets[:, 0]
relations = triplets[:, 1]
tails = triplets[:, 2]
return (self.kge_model.entity_embedding.weight[heads] + self.relation_emb.weight[relations] - self.kge_model.entity_embedding.weight[tails]).norm(p=1, dim=1)
def TransE_forward(self, pos_triplet, neg_triplet):
# -1 to avoid nan for OOV vector
self.kge_model.entity_embedding.weight.data[:-1, :].div_(self.kge_model.entity_embedding.weight.data[:-1, :].norm(p=2, dim=1, keepdim=True))
pos_distance = self.distance(pos_triplet)
neg_distance = self.distance(neg_triplet)
target = torch.tensor([-1], dtype=torch.long)
return self.criterion(pos_distance, neg_distance, target)
def forward_GCN(self, x):
# GCN
out = F.relu(self.gc1(self.kge_model.entity_embedding.weight))
out = self.gc2(out)
out = F.log_softmax(out, dim=1)[x]
return out
def forward(self, x):
x = self.kge_model.entity_embedding.weight[x]
x = x.reshape(1,1,-1)
# GRU
out, self.h = self.gru(x, self.h)
out = out.reshape(-1)
return out
class Net(nn.Module):
def __init__(self):
super().__init__()
self.dense1 = nn.Linear(20, 128)
self.dense2 = nn.Linear(128, 128)
self.dense3 = nn.Linear(128, 3)
def forward(self, x):
x = F.relu(self.dense1(x))
x = F.relu(self.dense2(x))
x = self.dense3(x)
return x