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model.py
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model.py
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import torch
from torch.nn import functional as F, Parameter
from torch.autograd import Variable
from torch.nn.init import xavier_normal_, xavier_uniform_
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
class Complex(torch.nn.Module):
def __init__(self, args, num_entities, num_relations):
super(Complex, self).__init__()
self.num_entities = num_entities
self.emb_e_real = torch.nn.Embedding(num_entities, args.embedding_dim, padding_idx=0)
self.emb_e_img = torch.nn.Embedding(num_entities, args.embedding_dim, padding_idx=0)
self.emb_rel_real = torch.nn.Embedding(num_relations, args.embedding_dim, padding_idx=0)
self.emb_rel_img = torch.nn.Embedding(num_relations, args.embedding_dim, padding_idx=0)
self.inp_drop = torch.nn.Dropout(args.input_drop)
self.loss = torch.nn.BCELoss()
def init(self):
xavier_normal_(self.emb_e_real.weight.data)
xavier_normal_(self.emb_e_img.weight.data)
xavier_normal_(self.emb_rel_real.weight.data)
xavier_normal_(self.emb_rel_img.weight.data)
def forward(self, e1, rel):
e1_embedded_real = self.emb_e_real(e1).squeeze()
rel_embedded_real = self.emb_rel_real(rel).squeeze()
e1_embedded_img = self.emb_e_img(e1).squeeze()
rel_embedded_img = self.emb_rel_img(rel).squeeze()
e1_embedded_real = self.inp_drop(e1_embedded_real)
rel_embedded_real = self.inp_drop(rel_embedded_real)
e1_embedded_img = self.inp_drop(e1_embedded_img)
rel_embedded_img = self.inp_drop(rel_embedded_img)
# complex space bilinear product (equivalent to HolE)
realrealreal = torch.mm(e1_embedded_real*rel_embedded_real, self.emb_e_real.weight.transpose(1,0))
realimgimg = torch.mm(e1_embedded_real*rel_embedded_img, self.emb_e_img.weight.transpose(1,0))
imgrealimg = torch.mm(e1_embedded_img*rel_embedded_real, self.emb_e_img.weight.transpose(1,0))
imgimgreal = torch.mm(e1_embedded_img*rel_embedded_img, self.emb_e_real.weight.transpose(1,0))
pred = realrealreal + realimgimg + imgrealimg - imgimgreal
pred = torch.sigmoid(pred)
return pred
class DistMult(torch.nn.Module):
def __init__(self, args, num_entities, num_relations):
super(DistMult, self).__init__()
self.emb_e = torch.nn.Embedding(num_entities, args.embedding_dim, padding_idx=0)
self.emb_rel = torch.nn.Embedding(num_relations, args.embedding_dim, padding_idx=0)
self.inp_drop = torch.nn.Dropout(args.input_drop)
self.loss = torch.nn.BCELoss()
def init(self):
xavier_normal_(self.emb_e.weight.data)
xavier_normal_(self.emb_rel.weight.data)
def forward(self, e1, rel):
e1_embedded= self.emb_e(e1)
rel_embedded= self.emb_rel(rel)
e1_embedded = e1_embedded.squeeze()
rel_embedded = rel_embedded.squeeze()
e1_embedded = self.inp_drop(e1_embedded)
rel_embedded = self.inp_drop(rel_embedded)
pred = torch.mm(e1_embedded*rel_embedded, self.emb_e.weight.transpose(1,0))
pred = torch.sigmoid(pred)
return pred
class ConvE(torch.nn.Module):
def __init__(self, args, num_entities, num_relations):
super(ConvE, self).__init__()
self.emb_e = torch.nn.Embedding(num_entities, args.embedding_dim, padding_idx=0)
self.emb_rel = torch.nn.Embedding(num_relations, args.embedding_dim, padding_idx=0)
self.inp_drop = torch.nn.Dropout(args.input_drop)
self.hidden_drop = torch.nn.Dropout(args.hidden_drop)
self.feature_map_drop = torch.nn.Dropout2d(args.feat_drop)
self.loss = torch.nn.BCELoss()
self.emb_dim1 = args.embedding_shape1
self.emb_dim2 = args.embedding_dim // self.emb_dim1
self.conv1 = torch.nn.Conv2d(1, 32, (3, 3), 1, 0, bias=args.use_bias)
self.bn0 = torch.nn.BatchNorm2d(1)
self.bn1 = torch.nn.BatchNorm2d(32)
self.bn2 = torch.nn.BatchNorm1d(args.embedding_dim)
self.register_parameter('b', Parameter(torch.zeros(num_entities)))
self.fc = torch.nn.Linear(args.hidden_size,args.embedding_dim)
print(num_entities, num_relations)
def init(self):
xavier_normal_(self.emb_e.weight.data)
xavier_normal_(self.emb_rel.weight.data)
def forward(self, e1, rel):
e1_embedded= self.emb_e(e1).view(-1, 1, self.emb_dim1, self.emb_dim2)
rel_embedded = self.emb_rel(rel).view(-1, 1, self.emb_dim1, self.emb_dim2)
stacked_inputs = torch.cat([e1_embedded, rel_embedded], 2)
stacked_inputs = self.bn0(stacked_inputs)
x= self.inp_drop(stacked_inputs)
x= self.conv1(x)
x= self.bn1(x)
x= F.relu(x)
x = self.feature_map_drop(x)
x = x.view(x.shape[0], -1)
x = self.fc(x)
x = self.hidden_drop(x)
x = self.bn2(x)
x = F.relu(x)
x = torch.mm(x, self.emb_e.weight.transpose(1,0))
x += self.b.expand_as(x)
pred = torch.sigmoid(x)
return pred
# Add your own model here
class MyModel(torch.nn.Module):
def __init__(self, num_entities, num_relations):
super(DistMult, self).__init__()
self.emb_e = torch.nn.Embedding(num_entities, args.embedding_dim, padding_idx=0)
self.emb_rel = torch.nn.Embedding(num_relations, args.embedding_dim, padding_idx=0)
self.inp_drop = torch.nn.Dropout(args.input_drop)
self.loss = torch.nn.BCELoss()
def init(self):
xavier_normal_(self.emb_e.weight.data)
xavier_normal_(self.emb_rel.weight.data)
def forward(self, e1, rel):
e1_embedded= self.emb_e(e1)
rel_embedded= self.emb_rel(rel)
# Add your model function here
# The model function should operate on the embeddings e1 and rel
# and output scores for all entities (you will need a projection layer
# with output size num_relations (from constructor above)
# generate output scores here
prediction = torch.sigmoid(output)
return prediction