def evaluate(encoder,
decoder,
voc,
sentence,
beam_size,
k,
p,
max_length=MAX_LENGTH,
hidvar=0):
indexes_batch = [indexesFromSentence(voc, sentence)] # [1, seq_len]
lengths = [len(indexes) for indexes in indexes_batch]
input_batch = torch.LongTensor(indexes_batch).transpose(0, 1)
input_batch = input_batch.to(device)
encoder_outputs, encoder_hidden = encoder(
input_batch, lengths, None) # tutorial: instead in class of decoder
if hidvar:
z = Variable(torch.randn([4, 1, hidvar.z_hidden_size]))
z = z.to(device)
decoder_hidden = torch.cat(
[encoder_hidden[:decoder.n_layers], z[:decoder.n_layers]], 2)
else:
decoder_hidden = encoder_hidden[:decoder.n_layers]
if beam_size == 1:
return decode(decoder, decoder_hidden, encoder_outputs, voc, k, p)
else:
return beam_decode(decoder, decoder_hidden, encoder_outputs, voc,
beam_size)
def evaluate(encoder, decoder, voc, sentence, beam_size, max_length=MAX_LENGTH):
indexes_batch = [indexesFromSentence(voc, sentence)] #[1, seq_len]
lengths = [len(indexes) for indexes in indexes_batch]
input_batch = Variable(torch.LongTensor(indexes_batch), volatile=True).transpose(0, 1)
input_batch = input_batch.cuda() if USE_CUDA else input_batch
encoder_outputs, encoder_hidden = encoder(input_batch, lengths, None)
decoder_hidden = encoder_hidden[:decoder.n_layers]
if beam_size == 1:
return decode(decoder, decoder_hidden, encoder_outputs, voc)
else:
return beam_decode(decoder, decoder_hidden, encoder_outputs, voc, beam_size)
def evaluate(encoder, decoder, voc, sentence, beam_size, max_length=MAX_LENGTH):
indexes_batch = [indexesFromSentence(voc, sentence)] #[1, seq_len]
lengths = [len(indexes) for indexes in indexes_batch]
input_batch = Variable(torch.LongTensor(indexes_batch), volatile=True).transpose(0, 1)
input_batch = input_batch.cuda() if USE_CUDA else input_batch
encoder_outputs, encoder_hidden = encoder(input_batch, lengths, None)
decoder_hidden = encoder_hidden[:decoder.n_layers]
if beam_size == 1:
return decode(decoder, decoder_hidden, encoder_outputs, voc)
else:
return beam_decode(decoder, decoder_hidden, encoder_outputs, voc, beam_size)
def evaluate(encoder,
decoder,
pinyin_voc,
word_voc,
sentence,
beam_size,
max_length=MAX_LENGTH):
indexes_batch = [indexesFromSentence(pinyin_voc, sentence)] #[1, seq_len]
lengths = [len(indexes) for indexes in indexes_batch]
input_batch = torch.LongTensor(indexes_batch).transpose(0, 1)
input_batch = input_batch.to(device)
encoder_outputs, encoder_hidden = encoder(input_batch, lengths, None)
decoder_hidden = encoder_hidden[:decoder.n_layers]
if beam_size == 1:
return decode(decoder, decoder_hidden, encoder_outputs, word_voc,
len(indexes_batch[0]))
else:
return beam_decode(decoder, decoder_hidden, encoder_outputs, word_voc,
beam_size)
def evaluate(encoder,
decoder,
voc,
sentence,
beam_size,
max_length=MAX_LENGTH):
# TODO：evaluate 负责对一个输入句子生成一句回答
indexes_batch = [indexesFromSentence(voc, sentence)
] #把句子转换为index数字串，[1, seq_len]
lengths = [len(indexes) for indexes in indexes_batch]
input_batch = torch.LongTensor(indexes_batch).transpose(0,
1) #[ seq_len,1]
input_batch = input_batch.to(device)
encoder_outputs, encoder_hidden = encoder(input_batch, lengths, None)
decoder_hidden = encoder_hidden[:decoder.n_layers]
if beam_size == 1:
return decode(decoder, decoder_hidden, encoder_outputs, voc)
else:
return beam_decode(decoder, decoder_hidden, encoder_outputs, voc,
beam_size)