Yibing Liu
8 years ago
parent
f20c6d643f
commit
8033845615
@ -0,0 +1,62 @@
|
||||
"""External Scorer for Beam Search Decoder."""
|
||||
from __future__ import absolute_import
|
||||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
|
||||
import os
|
||||
import kenlm
|
||||
import numpy as np
|
||||
|
||||
|
||||
class Scorer(object):
|
||||
"""External defined scorer to evaluate a sentence in beam search
|
||||
decoding, consisting of language model and word count.
|
||||
|
||||
:param alpha: Parameter associated with language model.
|
||||
:type alpha: float
|
||||
:param beta: Parameter associated with word count.
|
||||
:type beta: float
|
||||
:model_path: Path to load language model.
|
||||
:type model_path: basestring
|
||||
"""
|
||||
|
||||
def __init__(self, alpha, beta, model_path):
|
||||
self._alpha = alpha
|
||||
self._beta = beta
|
||||
if not os.path.isfile(model_path):
|
||||
raise IOError("Invaid language model path: %s" % model_path)
|
||||
self._language_model = kenlm.LanguageModel(model_path)
|
||||
|
||||
# n-gram language model scoring
|
||||
def language_model_score(self, sentence):
|
||||
#log10 prob of last word
|
||||
log_cond_prob = list(
|
||||
self._language_model.full_scores(sentence, eos=False))[-1][0]
|
||||
return np.power(10, log_cond_prob)
|
||||
|
||||
# word insertion term
|
||||
def word_count(self, sentence):
|
||||
words = sentence.strip().split(' ')
|
||||
return len(words)
|
||||
|
||||
# execute evaluation
|
||||
def __call__(self, sentence, log=False):
|
||||
"""Evaluation function, gathering all the different scores
|
||||
and return the final one.
|
||||
|
||||
:param sentence: The input sentence for evalutation
|
||||
:type sentence: basestring
|
||||
:param log: Whether return the score in log representation.
|
||||
:type log: bool
|
||||
:return: Evaluation score, in the decimal or log.
|
||||
:rtype: float
|
||||
"""
|
||||
lm = self.language_model_score(sentence)
|
||||
word_cnt = self.word_count(sentence)
|
||||
if log == False:
|
||||
score = np.power(lm, self._alpha) \
|
||||
* np.power(word_cnt, self._beta)
|
||||
else:
|
||||
score = self._alpha * np.log(lm) \
|
||||
+ self._beta * np.log(word_cnt)
|
||||
return score
|
||||
@ -0,0 +1,90 @@
|
||||
"""Test decoders."""
|
||||
from __future__ import absolute_import
|
||||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
|
||||
import unittest
|
||||
from decoder import *
|
||||
|
||||
|
||||
class TestDecoders(unittest.TestCase):
|
||||
def setUp(self):
|
||||
self.vocab_list = ["\'", ' ', 'a', 'b', 'c', 'd']
|
||||
self.beam_size = 20
|
||||
self.probs_seq1 = [[
|
||||
0.06390443, 0.21124858, 0.27323887, 0.06870235, 0.0361254,
|
||||
0.18184413, 0.16493624
|
||||
], [
|
||||
0.03309247, 0.22866108, 0.24390638, 0.09699597, 0.31895462,
|
||||
0.0094893, 0.06890021
|
||||
], [
|
||||
0.218104, 0.19992557, 0.18245131, 0.08503348, 0.14903535,
|
||||
0.08424043, 0.08120984
|
||||
], [
|
||||
0.12094152, 0.19162472, 0.01473646, 0.28045061, 0.24246305,
|
||||
0.05206269, 0.09772094
|
||||
], [
|
||||
0.1333387, 0.00550838, 0.00301669, 0.21745861, 0.20803985,
|
||||
0.41317442, 0.01946335
|
||||
], [
|
||||
0.16468227, 0.1980699, 0.1906545, 0.18963251, 0.19860937,
|
||||
0.04377724, 0.01457421
|
||||
]]
|
||||
self.probs_seq2 = [[
|
||||
0.08034842, 0.22671944, 0.05799633, 0.36814645, 0.11307441,
|
||||
0.04468023, 0.10903471
|
||||
], [
|
||||
0.09742457, 0.12959763, 0.09435383, 0.21889204, 0.15113123,
|
||||
0.10219457, 0.20640612
|
||||
], [
|
||||
0.45033529, 0.09091417, 0.15333208, 0.07939558, 0.08649316,
|
||||
0.12298585, 0.01654384
|
||||
], [
|
||||
0.02512238, 0.22079203, 0.19664364, 0.11906379, 0.07816055,
|
||||
0.22538587, 0.13483174
|
||||
], [
|
||||
0.17928453, 0.06065261, 0.41153005, 0.1172041, 0.11880313,
|
||||
0.07113197, 0.04139363
|
||||
], [
|
||||
0.15882358, 0.1235788, 0.23376776, 0.20510435, 0.00279306,
|
||||
0.05294827, 0.22298418
|
||||
]]
|
||||
self.best_path_result = ["ac'bdc", "b'da"]
|
||||
self.beam_search_result = ['acdc', "b'a"]
|
||||
|
||||
def test_best_path_decoder_1(self):
|
||||
bst_result = ctc_best_path_decode(self.probs_seq1, self.vocab_list)
|
||||
self.assertEqual(bst_result, self.best_path_result[0])
|
||||
|
||||
def test_best_path_decoder_2(self):
|
||||
bst_result = ctc_best_path_decode(self.probs_seq2, self.vocab_list)
|
||||
self.assertEqual(bst_result, self.best_path_result[1])
|
||||
|
||||
def test_beam_search_decoder_1(self):
|
||||
beam_result = ctc_beam_search_decoder(
|
||||
probs_seq=self.probs_seq1,
|
||||
beam_size=self.beam_size,
|
||||
vocabulary=self.vocab_list,
|
||||
blank_id=len(self.vocab_list))
|
||||
self.assertEqual(beam_result[0][1], self.beam_search_result[0])
|
||||
|
||||
def test_beam_search_decoder_2(self):
|
||||
beam_result = ctc_beam_search_decoder(
|
||||
probs_seq=self.probs_seq2,
|
||||
beam_size=self.beam_size,
|
||||
vocabulary=self.vocab_list,
|
||||
blank_id=len(self.vocab_list))
|
||||
self.assertEqual(beam_result[0][1], self.beam_search_result[1])
|
||||
|
||||
def test_beam_search_nproc_decoder(self):
|
||||
beam_results = ctc_beam_search_decoder_nproc(
|
||||
probs_split=[self.probs_seq1, self.probs_seq2],
|
||||
beam_size=self.beam_size,
|
||||
vocabulary=self.vocab_list,
|
||||
blank_id=len(self.vocab_list))
|
||||
self.assertEqual(beam_results[0][0][1], self.beam_search_result[0])
|
||||
self.assertEqual(beam_results[1][0][1], self.beam_search_result[1])
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
Loading…
Reference in new issue