def beam_generate(self,h,c,tembs,vembs,gembs,nerd,beamsz,k):
#h,c,tembs,vembs,gembs,rembs = self.encode_inputs(title,entities,graph)
#h,c,tembs,vembs,gembs = self.encode_inputs(title,entities,graph)
embs = [x for x in [(self.t_attn,tembs),(self.g_attn,gembs),(self.e_attn,vembs)] if x[1] is not None]
outp = torch.LongTensor(vembs[0].size(0),1).fill_(self.starttok).cuda()
last = h.transpose(0,1)
outputs = []
beam = None
for i in range(self.maxlen):
outp = self.emb_w_vertex(outp.clone(),nerd)
enc = self.Embedding(outp)
decin = torch.cat((enc,last),2)
decout,(h,c) = self.dlstm(decin,(h,c))
last, vweight, _ = self.hierattn(decout,embs)
scalar = torch.sigmoid(self.switch(h))
outs = torch.cat((decout,last),2)
decoded = self.outlin(outs.contiguous().view(-1, self.args.hsz*2))
decoded = decoded.view(outs.size(0), outs.size(1), self.args.ntoks)
decoded = torch.softmax(decoded,2)
decoded[:,:,0].fill_(0)
decoded[:,:,1].fill_(0)
scalars = scalar.transpose(0,1)
decoded = torch.mul(decoded,1-scalars.expand_as(decoded))
vweights = torch.mul(vweight,scalars.expand_as(vweight))
decoded = torch.cat([decoded,vweights],2)
zero_vec = 1e-6*torch.ones_like(decoded)
decoded += zero_vec
decoded = decoded.log()
scores, words = decoded.topk(dim=2,k=k)
#scores = scores.transpose(0,1); words = words.transpose(0,1)
if not beam:
beam = Beam(words.squeeze(),scores.squeeze(),[h for i in range(beamsz)],
[c for i in range(beamsz)],[last for i in range(beamsz)],beamsz,k)
beam.endtok = self.endtok
newembs = []
for a,x in embs:
tmp = (x[0].repeat(len(beam.beam),1,1),x[1].repeat(len(beam.beam),1))
newembs.append((a,tmp))
embs = newembs
else:
if not beam.update(scores,words,h,c,last):
break
newembs = []
for a,x in embs:
tmp = (x[0][:len(beam.beam),:,:],x[1][:len(beam.beam)])
newembs.append((a,tmp))
embs = newembs
outp = beam.getwords()
h = beam.geth()
c = beam.getc()
last = beam.getlast()
return beam
