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model.py
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model.py
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import torch
class Encoder(torch.nn.Module):
def __init__(self, vocab_size, embed_dim, hidden_dim, layers, class_num, encoder, sememe_num, chara_num, mode):
super().__init__()
self.vocab_size = vocab_size
self.embed_dim = 200
self.hidden_dim = 768
self.layers = layers
self.class_num = class_num
self.sememe_num = sememe_num
self.chara_num = chara_num
self.embedding = torch.nn.Embedding(self.vocab_size, self.embed_dim, padding_idx=0, max_norm=5, sparse=True)
self.embedding.weight.requires_grad = False
self.embedding_dropout = torch.nn.Dropout(0.2)
self.encoder = encoder
self.fc = torch.nn.Linear(self.hidden_dim, self.embed_dim)
self.loss = torch.nn.CrossEntropyLoss()
self.relu = torch.nn.ReLU()
if 'P' in mode:
self.fc2 = torch.nn.Linear(self.hidden_dim, 13)
if 's' in mode:
self.fc1 = torch.nn.Linear(self.hidden_dim, self.sememe_num)
if 'c' in mode:
self.fc3 = torch.nn.Linear(self.hidden_dim, self.chara_num)
if 'C' in mode:
self.fc_C1 = torch.nn.Linear(self.hidden_dim, 12)
self.fc_C2 = torch.nn.Linear(self.hidden_dim, 95)
self.fc_C3 = torch.nn.Linear(self.hidden_dim, 1425)
def forward(self, operation, x=None, w=None, ws=None, wP=None, wc=None, wC=None, msk_s=None, msk_c=None, mode=None):
# x: T(bat, max_word_num)
# w: T(bat)
# x_embedding: T(bat, max_word_num, embed_dim)
x = x.long()
attention_mask = torch.gt(x, 0).to(torch.int64)
h = self.encoder(x, attention_mask=attention_mask)[0]
h_1 = self.embedding_dropout(h[:,0,:])
vd = self.fc(h_1)
# score0: T(bat, 30000) = [bat, emb] .mm [class_num, emb].t()
score0 = vd.mm(self.embedding.weight.data[[range(self.class_num)]].t())
score = score0
if 'C' in mode:
# scC[i]: T(bat, Ci_size)
# 词林的层次分类训练的慢，其实这样不公平，不平衡，因为词预测先收敛了，而cilin的分类还没效果，其他信息的利用也有同样的问题，不一定同时收敛！！！
scC = [self.fc_C1(h_1), self.fc_C2(h_1), self.fc_C3(h_1)]
score2 = torch.zeros((score0.shape[0], score0.shape[1]), dtype=torch.float32)
rank = 0.6
for i in range(3):
# wC[i]: T(class_num, Ci_size)
# C_sc: T(bat, class_num)
score2 += self.relu(scC[i].mm(wC[i].t())*(rank**i))
#----------add mean cilin-class score to those who have no cilin-class
mean_cilin_sc = torch.mean(score2, 1)
score2 = score2*(1-msk_c) + mean_cilin_sc.unsqueeze(1).mm(msk_c.unsqueeze(0))
#----------
score = score + score2/2
if 'P' in mode:
## POS prediction
# score_POS: T(bat, 13) pos_num=12+1
score_POS = self.fc2(h_1)
# s: (class_num, 13) multi-hot
# weight_sc: T(bat, class_num) = [bat, 13] .mm [class_num, 13].t()
weight_sc = self.relu(score_POS.mm(wP.t()))
#print(torch.max(weight_sc), torch.min(weight_sc))
score = score + weight_sc
if 's' in mode:
## sememe prediction
# pos_score: T(bat, max_word_num, sememe_num)
pos_score = self.fc1(h)
# sem_score: T(bat, sememe_num)
sem_score, _ = torch.max(pos_score, dim=1)
# score: T(bat, class_num) = [bat, sememe_num] .mm [class_num, sememe_num].t()
score1 = self.relu(sem_score.mm(ws.t()))
#----------add mean sememe score to those who have no sememes
# mean_sem_sc: T(bat)
mean_sem_sc = torch.mean(score1, 1)
# msk: T(class_num)
score1 = score1 + mean_sem_sc.unsqueeze(1).mm(msk_s.unsqueeze(0))
#----------
score = score + score1
if 'c' in mode:
## character prediction
# pos_score: T(bat, max_word_num, sememe_num)
pos_score = self.fc3(h)
# chara_score: T(bat, chara_num)
chara_score, _ = torch.max(pos_score, dim=1)
#chara_score = torch.sum(pos_score * alpha, 1)
# score: T(bat, class_num) = [bat, sememe_num] .mm [class_num, sememe_num].t()
score3 = self.relu(chara_score.mm(wc.t()))
score = score + score3
'''
if RD_mode == 'CC':
# fine-tune depended on the target word shouldn't exist in the definition.
#score_res = score.clone().detach()
mask1 = torch.lt(x, self.class_num).to(torch.int64)
mask2 = torch.ones((score.shape[0], score.shape[1]), dtype=torch.float32)
for i in range(x.shape[0]):
mask2[i][x[i]*mask1[i]] = 0.
score = score * mask2 + (-1e6)*(1-mask2)
'''
#_, indices = torch.sort(score, descending=True)
if operation == 'train':
loss = self.loss(score, w.long())
return loss, score, indices
elif operation == 'test':
return score #, indices