def forward(self, x, length):
batch_size = x.size(0)
maxlength = x.size(1)
hidden = [self.init_hidden(batch_size) for l in range(self.bi_num)]
weight = self.init_attention_weight(batch_size, maxlength)
out_final = self.init_final_out(batch_size)
out = [x, reverse_padded_sequence(x, length, batch_first=True)]
for l in range(self.bi_num):
out[l] = pack_padded_sequence(out[l], length, batch_first=True)
out[l], hidden[l] = self.layer1[l](out[l], hidden[l])
out[l], _ = pad_packed_sequence(out[l], batch_first=True)
if (l == 1):
out[l] = reverse_padded_sequence(out[l],
length,
batch_first=True)
if (self.bi_num == 1): out = out[0]
else: out = torch.cat(out, 2)
potential = self.simple_attention(out)
for inx, l in enumerate(length):
weight[inx,
0:l] = torch.squeeze(F.softmax(potential[inx, 0:l], dim=0),
1)
out_final = torch.squeeze(torch.bmm(torch.unsqueeze(weight, 1), out),
1)
out_final = self.layer2(out_final)
return out_final, length
def forward(self, x, length, r):
batch_size = x.size(0)
maxlength = x.size(1)
hidden = [self.init_hidden(batch_size) for l in range(self.bi_num)]
weight = self.init_attention_weight(batch_size, maxlength, r)
oneMat = Variable(torch.ones(batch_size, r, r)).cuda()
out = [x, reverse_padded_sequence(x, length, batch_first=True)]
for l in range(self.bi_num):
out[l] = pack_padded_sequence(out[l], length, batch_first=True)
out[l], hidden[l] = self.layer1[l](out[l], hidden[l])
out[l], _ = pad_packed_sequence(out[l], batch_first=True)
if (l == 1):
out[l] = reverse_padded_sequence(out[l],
length,
batch_first=True)
if (self.bi_num == 1): out = out[0]
else: out = torch.cat(out, 2)
potential = self.simple_attention(out)
for inx, l in enumerate(length):
weight[inx, 0:l, :] = F.softmax(potential[inx, 0:l, :], dim=0)
weight = torch.transpose(weight, 1, 2)
out_final = torch.bmm(weight, out)
out_final = out_final.view(batch_size, -1)
out_final = self.layer2(out_final)
penalty = torch.sum(
torch.sum(
torch.sum(
torch.pow(
torch.bmm(weight, torch.transpose(weight, 1, 2)) -
oneMat, 2.0), 0), 0), 0)
return out_final, length, penalty
def forward(self,x1,x2,length):
batch_size=x1.size(0)
maxlength=int(np.max(length))
hidden=[ self.init_hidden(batch_size) for l in range(self.bi_num)]
# weight=self.init_attention_weight(batch_size,maxlength)
# out_final=self.init_final_out(batch_size)
x1=x1.view(-1,self.input_channels,self.input_dim1)
out=self.cov1(x1)
out=self.cov2(out)
out=out.view(batch_size,maxlength,self.dimAfterCov)
out=torch.cat((out,x2),dim=2)
out=[out,reverse_padded_sequence(out,length,batch_first=True)]
for l in range(self.bi_num):
out[l]=pack_padded_sequence(out[l],length,batch_first=True)
out[l],hidden[l]=self.layer1[l](out[l],hidden[l])
out[l],_=pad_packed_sequence(out[l],batch_first=True)
if(l==1):out[l]=reverse_padded_sequence(out[l],length,batch_first=True)
# potential=self.simple_attention(out)
# for inx,l in enumerate(length):weight[inx,0:l]=F.softmax(potential[inx,0:l],dim=0)
# for inx,l in enumerate(length):out_final[inx,:]=torch.matmul(weight[inx,:],torch.squeeze(out[inx,:,:]))
if(self.bi_num==1):out=out[0]
else:out=torch.cat(out,2)
out=self.layer2(out)
out=torch.squeeze(out)
return out,length
def forward(self,x,length):
batch_size=x.size(0)
hidden=[ self.init_hidden(batch_size) for l in range(self.bi_num)]
out=[x,reverse_padded_sequence(x,length,batch_first=True)]
for l in range(self.bi_num):
out[l]=pack_padded_sequence(out[l],length,batch_first=True)
out[l],hidden[l]=self.layer1[l](out[l],hidden[l])
out[l],_=pad_packed_sequence(out[l],batch_first=True)
if(l==1):out[l]=reverse_padded_sequence(out[l],length,batch_first=True)
if(self.bi_num==1):out=out[0]
else:out=torch.cat(out,2)
out=self.layer2(out)
out=torch.squeeze(out)
return out,length
