Beispiel #1
0
def decode_value(model, utterance, class_string, memory, cuda):

    sent_lis = process_sent(utterance)
    if len(sent_lis) == 0:
        return []

    data, lengths, extra_zeros, enc_batch_extend_vocab_idx, oov_list = \
            ValueDataset.data_info(utterance, memory, cuda)
    act_inputs, act_slot_pairs, values_inp, values_out = \
            ValueDataset.label_info(class_string, memory, oov_list, 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)

    ## value decoder
    s_decoder = model.enc_to_dec(hiddens)
    s_t_1 = s_decoder
    act_slot_ids = act_slot_pairs[0]
    y_t = torch.tensor([Constants.BOS]).view(1, 1)
    if cuda:
        y_t = y_t.cuda()
    value_ids = beam_search(model.value_decoder,
        act_slot_ids, extra_zeros,enc_batch_extend_vocab_idx,
        s_decoder, outputs, lengths,
        len(memory['dec2idx']), cuda
    )[1:-1]
    value_lis = []
    for vid in value_ids:
        if vid < len(memory['idx2dec']):
            value_lis.append(memory['idx2dec'][vid])
        else:
            value_lis.append(oov_list[vid - len(memory['idx2dec'])])
    values = [' '.join(value_lis)]

    slot = memory['idx2slot'][act_slot_pairs[0][0,1].item()]
    value = correct_value(slot, values[0])

    if value is None:
        return []

    values = [value]

    return values
Beispiel #2
0
def decode_value(model, cnet, class_string, memory, cuda):

    result = process_cn_example(cnet, memory['enc2idx'])

    if result is None:
        return []

    data, lengths, extra_zeros, enc_batch_extend_vocab_idx, oov_list = \
            ValueDataset.data_info(cnet, memory, cuda)
    act_inputs, act_slot_pairs, values_inp, values_out = \
            ValueDataset.label_info(class_string, memory, oov_list, cuda)

    # Model processing
    ## encoder
    outputs, hiddens = model.encoder(data, lengths)
    h_T = hiddens[0]

    ## value decoder
    s_decoder = model.enc_to_dec(hiddens)
    s_t_1 = s_decoder
    act_slot_ids = act_slot_pairs[0]
    y_t = torch.tensor([Constants.BOS]).view(1, 1)
    if cuda:
        y_t = y_t.cuda()
    value_ids = beam_search(model.value_decoder, act_slot_ids, extra_zeros,
                            enc_batch_extend_vocab_idx, s_decoder, outputs,
                            lengths, len(memory['dec2idx']), cuda)[1:-1]
    value_lis = []
    for vid in value_ids:
        if vid < len(memory['idx2dec']):
            value_lis.append(memory['idx2dec'][vid])
        else:
            value_lis.append(oov_list[vid - len(memory['idx2dec'])])
    values = [' '.join(value_lis)]

    return values