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model_mygo.py
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import torch
import torch.nn as nn
from model_new import *
class MyGO(nn.Module):
def __init__(
self,
num_ent,
num_rel,
ent_vis_mask,
ent_txt_mask,
dim_str,
num_head,
dim_hid,
num_layer_enc_ent,
num_layer_enc_rel,
num_layer_dec,
dropout = 0.1,
emb_dropout = 0.6,
vis_dropout = 0.1,
txt_dropout = 0.1,
visual_token_index = None,
text_token_index = None,
score_function = "tucker",
text_tokenizer = "bert",
visual_tokenizer = "vqgan"
):
super(MyGO, self).__init__()
self.dim_str = dim_str
self.num_head = num_head
self.dim_hid = dim_hid
self.num_ent = num_ent
self.num_rel = num_rel
# Load Different Tokenizers
if text_tokenizer == "bert":
textual_tokens = torch.load("tokens/textual.pth")
elif text_tokenizer == "roberta":
textual_tokens = torch.load("tokens/textual_roberta.pth")
elif text_tokenizer == "llama":
textual_tokens = torch.load("tokens/textual_llama.pth")
else:
raise NotImplementedError
if visual_tokenizer == "beit":
visual_tokens = torch.load("tokens/visual.pth")
elif visual_tokenizer == "vqgan":
visual_tokens = torch.load("tokens/visual_vqgan.pth")
else:
raise NotImplementedError
self.visual_token_index = visual_token_index
self.visual_token_embedding = nn.Embedding.from_pretrained(visual_tokens).requires_grad_(False)
self.text_token_index = text_token_index
self.text_token_embedding = nn.Embedding.from_pretrained(textual_tokens).requires_grad_(False)
self.score_function = score_function
self.img_dim = visual_tokens.shape[1]
self.txt_dim = textual_tokens.shape[1]
self.visual_token_embedding.requires_grad_(False)
self.text_token_embedding.requires_grad_(False)
false_ents = torch.full((self.num_ent,1),False).cuda()
self.ent_mask = torch.cat([false_ents, false_ents, ent_vis_mask, ent_txt_mask], dim = 1)
# print(self.ent_mask.shape)
false_rels = torch.full((self.num_rel,1),False).cuda()
self.rel_mask = torch.cat([false_rels, false_rels], dim = 1)
self.ent_token = nn.Parameter(torch.Tensor(1, 1, dim_str))
self.rel_token = nn.Parameter(torch.Tensor(1, 1, dim_str))
self.ent_embeddings = nn.Parameter(torch.Tensor(num_ent, 1, dim_str))
self.rel_embeddings = nn.Parameter(torch.Tensor(num_rel, 1 ,dim_str))
self.lp_token = nn.Parameter(torch.Tensor(1,dim_str))
self.str_ent_ln = nn.LayerNorm(dim_str)
self.str_rel_ln = nn.LayerNorm(dim_str)
self.vis_ln = nn.LayerNorm(dim_str)
self.txt_ln = nn.LayerNorm(dim_str)
self.embdr = nn.Dropout(p = emb_dropout)
self.visdr = nn.Dropout(p = vis_dropout)
self.txtdr = nn.Dropout(p = txt_dropout)
self.pos_str_ent = nn.Parameter(torch.Tensor(1,1,dim_str))
self.pos_vis_ent = nn.Parameter(torch.Tensor(1,1,dim_str))
self.pos_txt_ent = nn.Parameter(torch.Tensor(1,1,dim_str))
self.pos_str_rel = nn.Parameter(torch.Tensor(1,1,dim_str))
self.pos_vis_rel = nn.Parameter(torch.Tensor(1,1,dim_str))
self.pos_txt_rel = nn.Parameter(torch.Tensor(1,1,dim_str))
self.pos_head = nn.Parameter(torch.Tensor(1,1,dim_str))
self.pos_rel = nn.Parameter(torch.Tensor(1,1,dim_str))
self.pos_tail = nn.Parameter(torch.Tensor(1,1,dim_str))
# Old Setting
self.proj_ent_vis = nn.Linear(self.img_dim, dim_str)
self.proj_ent_txt = nn.Linear(self.txt_dim, dim_str)
# self.proj_rel_vis = nn.Linear(dim_vis * 3, dim_str)
ent_encoder_layer = nn.TransformerEncoderLayer(dim_str, num_head, dim_hid, dropout, batch_first = True)
self.ent_encoder = nn.TransformerEncoder(ent_encoder_layer, num_layer_enc_ent)
rel_encoder_layer = nn.TransformerEncoderLayer(dim_str, num_head, dim_hid, dropout, batch_first = True)
self.rel_encoder = nn.TransformerEncoder(rel_encoder_layer, num_layer_enc_rel)
decoder_layer = nn.TransformerEncoderLayer(dim_str, num_head, dim_hid, dropout, batch_first = True)
self.decoder = nn.TransformerEncoder(decoder_layer, num_layer_dec)
self.contrastive = ContrastiveLoss(temp=0.5)
self.num_con = 512
self.num_vis = ent_vis_mask.shape[1]
if self.score_function == "tucker":
self.tucker_decoder = TuckERLayer(dim_str, dim_str)
else:
pass
self.init_weights()
# torch.save(self.visual_token_embedding, open("visual_token.pth", "wb"))
# torch.save(self.text_token_embedding, open("textual_token.pth", "wb"))
def init_weights(self):
nn.init.xavier_uniform_(self.ent_embeddings)
nn.init.xavier_uniform_(self.rel_embeddings)
nn.init.xavier_uniform_(self.proj_ent_vis.weight)
nn.init.xavier_uniform_(self.proj_ent_txt.weight)
nn.init.xavier_uniform_(self.ent_token)
nn.init.xavier_uniform_(self.rel_token)
nn.init.xavier_uniform_(self.lp_token)
nn.init.xavier_uniform_(self.pos_str_ent)
nn.init.xavier_uniform_(self.pos_vis_ent)
nn.init.xavier_uniform_(self.pos_txt_ent)
nn.init.xavier_uniform_(self.pos_str_rel)
nn.init.xavier_uniform_(self.pos_vis_rel)
nn.init.xavier_uniform_(self.pos_txt_rel)
nn.init.xavier_uniform_(self.pos_head)
nn.init.xavier_uniform_(self.pos_rel)
nn.init.xavier_uniform_(self.pos_tail)
def forward(self):
ent_tkn = self.ent_token.tile(self.num_ent, 1, 1)
rep_ent_str = self.embdr(self.str_ent_ln(self.ent_embeddings)) + self.pos_str_ent
entity_visual_tokens = self.visual_token_embedding(self.visual_token_index)
rep_ent_vis = self.visdr(self.vis_ln(self.proj_ent_vis(entity_visual_tokens))) + self.pos_vis_ent
entity_text_tokens = self.text_token_embedding(self.text_token_index)
rep_ent_txt = self.txtdr(self.txt_ln(self.proj_ent_txt(entity_text_tokens))) + self.pos_txt_ent
ent_seq = torch.cat([ent_tkn, rep_ent_str, rep_ent_vis, rep_ent_txt], dim = 1)
ent_embs = self.ent_encoder(ent_seq, src_key_padding_mask = self.ent_mask)[:,0]
rep_rel_str = self.embdr(self.str_rel_ln(self.rel_embeddings))
# torch.save(ent_embs, open("/data1/zhangyichi/DiscreteKGC/embeddings/entity_matrix.pth", "wb"))
return torch.cat([ent_embs, self.lp_token], dim = 0), rep_rel_str.squeeze(dim=1)
def contrastive_loss(self, emb_ent1):
ent_tkn = self.ent_token.tile(self.num_ent, 1, 1)
rep_ent_str = self.embdr(self.str_ent_ln(self.ent_embeddings)) + self.pos_str_ent
entity_visual_tokens = self.visual_token_embedding(self.visual_token_index)
rep_ent_vis = self.visdr(self.vis_ln(self.proj_ent_vis(entity_visual_tokens))) + self.pos_vis_ent
entity_text_tokens = self.text_token_embedding(self.text_token_index)
rep_ent_txt = self.txtdr(self.txt_ln(self.proj_ent_txt(entity_text_tokens))) + self.pos_txt_ent
ent_seq = torch.cat([ent_tkn, rep_ent_str, rep_ent_vis, rep_ent_txt], dim = 1)
ent_embs = self.ent_encoder(ent_seq, src_key_padding_mask = self.ent_mask)[:,0]
emb_ent2 = torch.cat([ent_embs, self.lp_token], dim = 0)
select_ents = torch.randperm(emb_ent1.shape[0])[: self.num_con]
contrastive_loss = self.contrastive(emb_ent1[select_ents], emb_ent2[select_ents])
# print(contrastive_loss)
return contrastive_loss
def contrastive_loss_finegrained(self, emb_ent1):
ent_tkn = self.ent_token.tile(self.num_ent, 1, 1)
rep_ent_str = self.embdr(self.str_ent_ln(self.ent_embeddings)) + self.pos_str_ent
entity_visual_tokens = self.visual_token_embedding(self.visual_token_index)
rep_ent_vis = self.visdr(self.vis_ln(self.proj_ent_vis(entity_visual_tokens))) + self.pos_vis_ent
entity_text_tokens = self.text_token_embedding(self.text_token_index)
rep_ent_txt = self.txtdr(self.txt_ln(self.proj_ent_txt(entity_text_tokens))) + self.pos_txt_ent
ent_seq = torch.cat([ent_tkn, rep_ent_str, rep_ent_vis, rep_ent_txt], dim = 1)
# ent_embs: [ent_num, seq_len, embed_dim]
ent_embs = self.ent_encoder(ent_seq, src_key_padding_mask = self.ent_mask)
print(ent_embs.shape, self.lp_token.shape)
emb_ent2 = torch.cat([ent_embs[:,0], self.lp_token], dim = 0)
ent_emb3 = torch.cat([torch.mean(ent_embs, dim=1), self.lp_token], dim = 0)
ent_emb4 = torch.cat([torch.mean(ent_embs[:, 2: 2 + self.num_vis, :], dim=1), self.lp_token], dim=0)
ent_emb5 = torch.cat([torch.mean(ent_embs[:, 2 + self.num_vis: -1, :], dim=1), self.lp_token], dim=0)
select_ents = torch.randperm(emb_ent1.shape[0])[: self.num_con]
contrastive_loss = 0
for emb in [emb_ent2, ent_emb3, ent_emb4, ent_emb5]:
contrastive_loss += self.contrastive(emb_ent1[select_ents], emb[select_ents])
contrastive_loss /= 4
return contrastive_loss
def score(self, emb_ent, emb_rel, triplets):
# args:
# emb_ent: [num_ent, emb_dim]
# emb_rel: [num_rel, emb_dim]
# triples: [batch_size, 3]
# return:
# scores: [batch_size, num_ent]
h_seq = emb_ent[triplets[:,0] - self.num_rel].unsqueeze(dim = 1) + self.pos_head
r_seq = emb_rel[triplets[:,1] - self.num_ent].unsqueeze(dim = 1) + self.pos_rel
t_seq = emb_ent[triplets[:,2] - self.num_rel].unsqueeze(dim = 1) + self.pos_tail
dec_seq = torch.cat([h_seq, r_seq, t_seq], dim = 1)
output_dec = self.decoder(dec_seq)
rel_emb = output_dec[:, 1, :]
ctx_emb = output_dec[triplets == self.num_ent + self.num_rel]
# indexs = triplets != self.num_ent + self.num_rel
# indexs[:, 1] = False
# ent_emb = output_dec[indexs]
if self.score_function == "tucker":
tucker_emb = self.tucker_decoder(ctx_emb, rel_emb)
score = torch.mm(tucker_emb, emb_ent[:-1].transpose(1, 0))
else:
# output_dec = self.decoder(dec_seq)
score = torch.inner(ctx_emb, emb_ent[:-1])
return score