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models.py
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from abc import ABC, abstractmethod
from typing import Tuple, List, Dict
import torch
from torch import nn
import pickle
import torch.nn.functional as F
import numpy as np
from config import alpha,beta,random_gate,forget_gate,remember_rate,constant
class KBCModel(nn.Module, ABC):
@abstractmethod
def get_rhs(self, chunk_begin: int, chunk_size: int):
pass
@abstractmethod
def get_queries(self, queries: torch.Tensor):
pass
@abstractmethod
def score(self, x: torch.Tensor):
pass
def get_ranking(
self, queries: torch.Tensor,
filters: Dict[Tuple[int, int], List[int]],
batch_size: int = 1000, chunk_size: int = -1
):
"""
Returns filtered ranking for each queries.
:param queries: a torch.LongTensor of triples (lhs, rel, rhs)
:param filters: filters[(lhs, rel)] gives the rhs to filter from ranking
:param batch_size: maximum number of queries processed at once
:param chunk_size: maximum number of candidates processed at once
:return:
"""
if chunk_size < 0:
chunk_size = self.sizes[2]
ranks = torch.ones(len(queries))
with torch.no_grad():
c_begin = 0
while c_begin < self.sizes[2]:
b_begin = 0
while b_begin < len(queries):
these_queries = queries[b_begin:b_begin + batch_size]
if not constant:
r_embeddings, img_embeddings= self.get_rhs(c_begin, chunk_size)
h_r = self.get_queries(these_queries)
n = len(h_r)
scores_str = torch.ones(0, self.r_embeddings[0].weight.size(0)).cuda()
for i in range(n):
i_alpha = self.alpha[(these_queries[i, 1])]
single_score = h_r[[i], :] @ (
(1 - i_alpha) * self.r_embeddings[0].weight + i_alpha * img_embeddings).transpose(0,
1)
scores_str = torch.cat((scores_str, single_score.detach()), 0)
else:
rhs = self.get_rhs(c_begin, chunk_size)
q = self.get_queries(these_queries)
scores_str = q @ rhs
lhs_img = F.normalize(self.img_vec[these_queries[:,0]], p=2, dim=1)
rhs_img = F.normalize(self.img_vec, p=2, dim=1).transpose(0, 1)
score_img=lhs_img@rhs_img
# beta=0.95
if forget_gate:
scores=beta*scores_str+(1-beta)*score_img*self.rel_pd[these_queries[:,1]]
else:
scores = beta * scores_str + (1 - beta) * score_img
targets = self.score(these_queries)
for i, query in enumerate(these_queries):
filter_out = filters[(query[0].item(), query[1].item())]
filter_out += [queries[b_begin + i, 2].item()]
if chunk_size < self.sizes[2]:
filter_in_chunk = [
int(x - c_begin) for x in filter_out
if c_begin <= x < c_begin + chunk_size
]
scores[i, torch.LongTensor(filter_in_chunk)] = -1e6
else:
scores[i, torch.LongTensor(filter_out)] = -1e6
ranks[b_begin:b_begin + batch_size] += torch.sum(
(scores >=targets).float(), dim=1
).cpu()
b_begin += batch_size
c_begin += chunk_size
return ranks
class CP(KBCModel):
def __init__(
self, sizes: Tuple[int, int, int], rank: int,
init_size: float = 1e-3
):
super(CP, self).__init__()
self.sizes = sizes
self.rank = rank
self.lhs = nn.Embedding(sizes[0], rank, sparse=True)
self.rel = nn.Embedding(sizes[1], rank, sparse=True)
self.rhs = nn.Embedding(sizes[2], rank, sparse=True)
self.lhs.weight.data *= init_size
self.rel.weight.data *= init_size
self.rhs.weight.data *= init_size
def score(self, x):
lhs = self.lhs(x[:, 0])
rel = self.rel(x[:, 1])
rhs = self.rhs(x[:, 2])
return torch.sum(lhs * rel * rhs, 1, keepdim=True)
def forward(self, x):
lhs = self.lhs(x[:, 0])
rel = self.rel(x[:, 1])
rhs = self.rhs(x[:, 2])
return (lhs * rel) @ self.rhs.weight.t(), (lhs, rel, rhs)
def get_rhs(self, chunk_begin: int, chunk_size: int):
return self.rhs.weight.data[
chunk_begin:chunk_begin + chunk_size
].transpose(0, 1)
def get_queries(self, queries: torch.Tensor):
return self.lhs(queries[:, 0]).data * self.rel(queries[:, 1]).data
def sc_wz_01(len,num_1):
A=[1 for i in range(num_1)]
B=[0 for i in range(len-num_1)]
C=A+B
np.random.shuffle(C)
return np.array(C,dtype=np.float)
class ComplEx(KBCModel):
def __init__(
self, sizes: Tuple[int, int, int], rank: int,
init_size: float = 1e-3,
img_info='img_vec_id_fb15k_20_vit.pickle',
sig_alpha='rel_MPR_SIG_vit_20.pickle',
rel_pd='rel_MPR_PD_vit_20_mrp{}.pickle'
):
super(ComplEx, self).__init__()
self.sizes = sizes
self.rank = rank
self.r_embeddings = nn.ModuleList([
nn.Embedding(s, 2 * rank, sparse=True)
for s in sizes[:2]
])
self.r_embeddings[0].weight.data *= init_size
self.r_embeddings[1].weight.data *= init_size
if not constant:
self.alpha=torch.from_numpy(pickle.load(open(sig_alpha, 'rb'))).cuda()
self.alpha=torch.cat((self.alpha,self.alpha),dim=0)
else:
self.alpha = nn.Parameter(torch.tensor(alpha), requires_grad=False) # [14951, 2000]
self.img_dimension = 1000
self.img_info = pickle.load(open(img_info, 'rb'))
self.img_vec = torch.from_numpy(self.img_info).float().cuda()
if not random_gate:
self.rel_pd=torch.from_numpy(pickle.load(open(rel_pd.format(remember_rate),'rb'))).cuda()
else:
tmp=pickle.load(open(rel_pd.format(remember_rate), 'rb'))
self.rel_pd=torch.from_numpy(sc_wz_01(len(tmp),np.sum(tmp))).unsqueeze(1).cuda()
self.rel_pd=torch.cat((self.rel_pd,self.rel_pd),dim=0)
# self.alpha[self.img_info['missed'], :] = 1
self.post_mats = nn.Parameter(torch.Tensor(self.img_dimension, 2 * rank), requires_grad=True)
nn.init.xavier_uniform(self.post_mats)
def score(self, x):
img_embeddings = self.img_vec.mm(self.post_mats)
if not constant:
lhs = (1 - self.alpha[(x[:, 1])]) * self.r_embeddings[0](x[:, 0]) + self.alpha[(x[:, 1])] * img_embeddings[
(x[:, 0])]
rel = self.r_embeddings[1](x[:, 1])
rhs = (1 - self.alpha[(x[:, 1])]) * self.r_embeddings[0](x[:, 2]) + self.alpha[(x[:, 1])] * img_embeddings[
(x[:, 2])]
rel_pd = self.rel_pd[(x[:, 1])]
lhs_img = self.img_vec[(x[:, 0])]
rhs_img = self.img_vec[(x[:, 2])]
if forget_gate:
score_img = torch.cosine_similarity(lhs_img, rhs_img, 1).unsqueeze(1) * rel_pd
else:
score_img = torch.cosine_similarity(lhs_img, rhs_img, 1).unsqueeze(1)
lhs = lhs[:, :self.rank], lhs[:, self.rank:]
rel = rel[:, :self.rank], rel[:, self.rank:]
rhs = rhs[:, :self.rank], rhs[:, self.rank:]
score_str = torch.sum(
(lhs[0] * rel[0] - lhs[1] * rel[1]) * rhs[0] +
(lhs[0] * rel[1] + lhs[1] * rel[0]) * rhs[1],
1, keepdim=True
)
# beta = 0.95
return beta * score_str + (1 - beta) * score_img
else:
embedding = (1 - self.alpha) * self.r_embeddings[0].weight + self.alpha* img_embeddings
lhs = embedding[(x[:, 0])]
rel = self.r_embeddings[1](x[:, 1])
rhs = embedding[(x[:, 2])]
rel_pd = self.rel_pd[(x[:, 1])]
lhs_img=self.img_vec[(x[:, 0])]
rhs_img=self.img_vec[(x[:, 2])]
# score_img = torch.cosine_similarity(lhs_img, rhs_img, 1).unsqueeze(1)
if forget_gate:
score_img=torch.cosine_similarity(lhs_img,rhs_img,1).unsqueeze(1)*rel_pd
else:
score_img = torch.cosine_similarity(lhs_img,rhs_img, 1).unsqueeze(1)
lhs = lhs[:, :self.rank], lhs[:, self.rank:]
rel = rel[:, :self.rank], rel[:, self.rank:]
rhs = rhs[:, :self.rank], rhs[:, self.rank:]
score_str=torch.sum(
(lhs[0] * rel[0] - lhs[1] * rel[1]) * rhs[0] +
(lhs[0] * rel[1] + lhs[1] * rel[0]) * rhs[1],
1, keepdim=True
)
# beta = 0.95
return beta*score_str+(1-beta)*score_img
def forward(self, x):
img_embeddings = self.img_vec.mm(self.post_mats)
if not constant:
lhs = (1 - self.alpha[(x[:, 1])]) * self.r_embeddings[0](x[:, 0]) + self.alpha[(x[:, 1])] * img_embeddings[(x[:, 0])]
rel = self.r_embeddings[1](x[:, 1])
rhs = (1 - self.alpha[(x[:, 1])]) * self.r_embeddings[0](x[:, 2]) + self.alpha[(x[:, 1])] * img_embeddings[(x[:, 2])]
lhs = lhs[:, :self.rank], lhs[:, self.rank:]
rel = rel[:, :self.rank], rel[:, self.rank:]
rhs = rhs[:, :self.rank], rhs[:, self.rank:]
h_r = torch.cat((lhs[0] * rel[0] - lhs[1] * rel[1], lhs[0] * rel[1] + lhs[1] * rel[0]), dim=-1)
n = len(h_r)
ans = torch.ones(0, self.r_embeddings[0].weight.size(0)).cuda()
for i in range(n):
i_alpha = self.alpha[(x[i, 1])]
single_score = h_r[[i], :] @ (
(1 - i_alpha) * self.r_embeddings[0].weight + i_alpha * img_embeddings).transpose(0, 1)
ans = torch.cat((ans, single_score.detach()), 0)
return (ans), (torch.sqrt(lhs[0] ** 2 + lhs[1] ** 2),
torch.sqrt(rel[0] ** 2 + rel[1] ** 2),
torch.sqrt(rhs[0] ** 2 + rhs[1] ** 2))
else:
embedding = (1 - self.alpha) * self.r_embeddings[0].weight + self.alpha * img_embeddings
lhs = embedding[(x[:, 0])]
rel = self.r_embeddings[1](x[:, 1])
rhs = embedding[(x[:, 2])]
lhs = lhs[:, :self.rank], lhs[:, self.rank:]
rel = rel[:, :self.rank], rel[:, self.rank:]
rhs = rhs[:, :self.rank], rhs[:, self.rank:]
to_score = embedding
to_score = to_score[:, :self.rank], to_score[:, self.rank:]
return (
(lhs[0] * rel[0] - lhs[1] * rel[1]) @ to_score[0].transpose(0, 1) +
(lhs[0] * rel[1] + lhs[1] * rel[0]) @ to_score[1].transpose(0, 1)
), (
torch.sqrt(lhs[0] ** 2 + lhs[1] ** 2),
torch.sqrt(rel[0] ** 2 + rel[1] ** 2),
torch.sqrt(rhs[0] ** 2 + rhs[1] ** 2)
)
def get_rhs(self, chunk_begin: int, chunk_size: int):
img_embeddings = self.img_vec.mm(self.post_mats)
if not constant:
return self.r_embeddings[0].weight.data[
chunk_begin:chunk_begin + chunk_size
],img_embeddings
else:
embedding = (1 - self.alpha) * self.r_embeddings[0].weight + self.alpha * img_embeddings
return embedding[
chunk_begin:chunk_begin + chunk_size
].transpose(0, 1)
def get_queries(self, queries: torch.Tensor):
img_embeddings = self.img_vec.mm(self.post_mats)
if not constant:
lhs = (1 - self.alpha[(queries[:, 1])]) * self.r_embeddings[0](queries[:, 0]) + self.alpha[(queries[:, 1])] * img_embeddings[
(queries[:, 0])]
rel = self.r_embeddings[1](queries[:, 1])
lhs = lhs[:, :self.rank], lhs[:, self.rank:]
rel = rel[:, :self.rank], rel[:, self.rank:]
return torch.cat([
lhs[0] * rel[0] - lhs[1] * rel[1],
lhs[0] * rel[1] + lhs[1] * rel[0]
], 1)
else:
embedding = (1 - self.alpha) * self.r_embeddings[0].weight + self.alpha * img_embeddings
lhs = embedding[(queries[:, 0])]
rel = self.r_embeddings[1](queries[:, 1])
lhs = lhs[:, :self.rank], lhs[:, self.rank:]
rel = rel[:, :self.rank], rel[:, self.rank:]
return torch.cat([
lhs[0] * rel[0] - lhs[1] * rel[1],
lhs[0] * rel[1] + lhs[1] * rel[0]
], 1)
if __name__ == '__main__':
pickle.pickle.load(open(img_info, 'rb'))