def forward(self, packed_input, packed_posterior,fw_adjs,bw_adjs,fw_edgeid,bw_edgeid,fw_edgedep,bw_edgedep,hidden=None):
#input: pack(data x n_feature ,batch_size)
#posterior: pack(data x src_len ,batch_size)
assert isinstance(packed_input,PackedSequence)
input = packed_input.data
if self.alpha and self.training:
input = data_dropout(input,self.alpha)
word_fix_embed = self.word_fix_lut(input[:,TXT_WORD])
lemma_emb = self.lemma_lut(input[:,TXT_LEMMA])
pos_emb = self.pos_lut(input[:,TXT_POS])
ner_emb = self.ner_lut(input[:,TXT_NER])
emb = torch.cat([word_fix_embed,lemma_emb,pos_emb,ner_emb],1)# data,embed
emb = PackedSequence(emb, packed_input.batch_sizes)
outputs, hidden_t = self.sencrnn(emb, hidden)
# print("outputs",outputs)
# print("self.Rnnsize",self.Rnnsize)
node_embedding = self.node_embedding(outputs, fw_adjs, bw_adjs, fw_edgeid, bw_edgeid, fw_edgedep, bw_edgedep)
poster_emb,amr_len = self.posteriorIndictedEmb(node_embedding,packed_posterior)
# print("poster_emb", poster_emb)
if hidden == None:
Outputs = self.rnn(poster_emb, hidden)[0] # h layer*batch_size*hidden_size
else:
print("yang hidden")
Outputs = self.rnn(poster_emb, hidden)[0]
return DoublePackedSequence(Outputs,amr_len,Outputs.data)
def forward(self, packed_input: PackedSequence, hidden=None):
#input: pack(data x n_feature ,batch_size)
input = packed_input.data
if self.alpha and self.training:
input = data_dropout(input, self.alpha)
word_fix_embed = self.word_fix_lut(input[:, TXT_WORD])
lemma_emb = self.lemma_lut(input[:, TXT_LEMMA])
pos_emb = self.pos_lut(input[:, TXT_POS])
ner_emb = self.ner_lut(input[:, TXT_NER])
emb = self.drop_emb(torch.cat([lemma_emb, pos_emb, ner_emb],
1)) # data,embed
emb = torch.cat([word_fix_embed, emb], 1) # data,embed
emb = PackedSequence(emb, packed_input.batch_sizes)
outputs, hidden_t = self.rnn(emb, hidden)
return outputs
def forward(self, input, index,src_enc):
assert isinstance(input, MyPackedSequence),input
input,lengths = input
if self.alpha and self.training:
input = data_dropout(input,self.alpha)
cat_embed = self.cat_lut(input[:,AMR_CAT])
lemma_embed = self.lemma_lut(input[:,AMR_LE])
amr_emb = torch.cat([cat_embed,lemma_embed],1)
# print (input,lengths)
head_emb = self.getEmb(index,src_enc) #packed, mydoublepacked
root_emb = torch.cat([amr_emb,head_emb.data],1)
root_emb = self.root(root_emb)
return MyPackedSequence(root_emb,lengths)
def forward(self, packed_input,hidden=None):
#input: pack(data x n_feature ,batch_size)
#posterior: pack(data x src_len ,batch_size)
assert isinstance(packed_input,PackedSequence)
input = packed_input.data
if self.alpha and self.training:
input = data_dropout(input,self.alpha)
word_fix_embed = self.word_fix_lut(input[:,TXT_WORD])
lemma_emb = self.lemma_lut(input[:,TXT_LEMMA])
pos_emb = self.pos_lut(input[:,TXT_POS])
ner_emb = self.ner_lut(input[:,TXT_NER])
emb = torch.cat([word_fix_embed,lemma_emb,pos_emb,ner_emb],1)# data,embed
emb = PackedSequence(emb, packed_input.batch_sizes)
outputs = self.rnn(emb, hidden)[0]
return outputs
def forward(self, input, index,src_enc):
assert isinstance(input, MyPackedSequence),input
input,lengths = input
if self.alpha and self.training:
input = data_dropout(input,self.alpha)
cat_embed = self.cat_lut(input[:,AMR_CAT])
lemma_embed = self.lemma_lut(input[:,AMR_LE])
amr_emb = torch.cat([cat_embed,lemma_embed],1)
# print (input,lengths)
head_emb_t,dep_emb_t,length_pairs = self.getEmb(index,src_enc) #packed, mydoublepacked
head_emb = torch.cat([amr_emb,head_emb_t.data],1)
dep_amr_emb_t = myunpack(*MyPackedSequence(amr_emb,lengths))
dep_amr_emb = [ emb.unsqueeze(0).expand(emb.size(0),emb.size(0),emb.size(-1)) for emb in dep_amr_emb_t]
mydouble_amr_emb = mydoublepack(dep_amr_emb,length_pairs)
# print ("rel_encoder",mydouble_amr_emb.data.size(),dep_emb_t.data.size())
dep_emb = torch.cat([mydouble_amr_emb.data,dep_emb_t.data],-1)
# emb_unpacked = myunpack(emb,lengths)
head_packed = MyPackedSequence(self.head(head_emb),lengths) # total,rel_dim
head_amr_packed = MyPackedSequence(amr_emb,lengths) # total,rel_dim
# print ("dep_emb",dep_emb.size())
size = dep_emb.size()
dep = self.dep(dep_emb.view(-1,size[-1])).view(size[0],size[1],-1)
dep_packed = MyDoublePackedSequence(MyPackedSequence(dep,mydouble_amr_emb[0][1]),mydouble_amr_emb[1],dep)
return head_amr_packed,head_packed,dep_packed #,MyPackedSequence(emb,lengths)