def decode_slot(model, utterance, class_string, memory, cuda):
sent_lis = process_sent(utterance)
if len(sent_lis) == 0:
return []
data, lengths = SlotDataset.data_info(utterance, memory, cuda)
act_inputs, slot_label = SlotDataset.label_info(class_string, memory, cuda)
# Model processing
## encoder
outputs, hiddens = model.encoder(data, lengths)
#h_T = hiddens[0].transpose(0, 1).contiguous().view(-1, model.enc_hid_all_dim)
h_T = hiddens[0]
## slot prediction
slot_scores = model.slot_predict(h_T, act_inputs)
slot_scores = slot_scores.data.cpu().view(-1,).numpy()
pred_slots = [i for i,p in enumerate(slot_scores) if p > 0.5]
slots = [memory['idx2slot'][i] for i in pred_slots]
return slots
def decode_slot(model, cnet, class_string, memory, cuda):
result = process_cn_example(cnet, memory['enc2idx'])
if result is None:
return []
data, lengths = SlotDataset.data_info(cnet, memory, cuda)
act_inputs, slot_label = SlotDataset.label_info(class_string, memory, cuda)
# Model processing
## encoder
outputs, hiddens = model.encoder(data, lengths)
h_T = hiddens[0].transpose(0, 1).contiguous().view(-1,
model.enc_hid_all_dim)
## slot prediction
slot_scores = model.slot_predict(h_T, act_inputs)
slot_scores = slot_scores.data.cpu().view(-1, ).numpy()
pred_slots = [i for i, p in enumerate(slot_scores) if p > 0.5]
slots = [memory['idx2slot'][i] for i in pred_slots]
return slots
