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#!/usr/bin/env python3
# encoding: utf-8
# Copyright 2019 Kyoto University (Hirofumi Inaguma)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Common functions for ST and MT."""
import nltk
import numpy as np
class ErrorCalculator(object):
"""Calculate BLEU for ST and MT models during training.
:param y_hats: numpy array with predicted text
:param y_pads: numpy array with true (target) text
:param char_list: vocabulary list
:param sym_space: space symbol
:param sym_pad: pad symbol
:param report_bleu: report BLUE score if True
"""
def __init__(self, char_list, sym_space, sym_pad, report_bleu=False):
"""Construct an ErrorCalculator object."""
super(ErrorCalculator, self).__init__()
self.char_list = char_list
self.space = sym_space
self.pad = sym_pad
self.report_bleu = report_bleu
if self.space in self.char_list:
self.idx_space = self.char_list.index(self.space)
else:
self.idx_space = None
def __call__(self, ys_hat, ys_pad):
"""Calculate corpus-level BLEU score.
:param torch.Tensor ys_hat: prediction (batch, seqlen)
:param torch.Tensor ys_pad: reference (batch, seqlen)
:return: corpus-level BLEU score in a mini-batch
:rtype float
"""
bleu = None
if not self.report_bleu:
return bleu
bleu = self.calculate_corpus_bleu(ys_hat, ys_pad)
return bleu
def calculate_corpus_bleu(self, ys_hat, ys_pad):
"""Calculate corpus-level BLEU score in a mini-batch.
:param torch.Tensor seqs_hat: prediction (batch, seqlen)
:param torch.Tensor seqs_true: reference (batch, seqlen)
:return: corpus-level BLEU score
:rtype float
"""
seqs_hat, seqs_true = [], []
for i, y_hat in enumerate(ys_hat):
y_true = ys_pad[i]
eos_true = np.where(y_true == -1)[0]
ymax = eos_true[0] if len(eos_true) > 0 else len(y_true)
# NOTE: padding index (-1) in y_true is used to pad y_hat
# because y_hats is not padded with -1
seq_hat = [self.char_list[int(idx)] for idx in y_hat[:ymax]]
seq_true = [self.char_list[int(idx)] for idx in y_true if int(idx) != -1]
seq_hat_text = "".join(seq_hat).replace(self.space, " ")
seq_hat_text = seq_hat_text.replace(self.pad, "")
seq_true_text = "".join(seq_true).replace(self.space, " ")
seqs_hat.append(seq_hat_text)
seqs_true.append(seq_true_text)
bleu = nltk.bleu_score.corpus_bleu([[ref] for ref in seqs_true], seqs_hat)
return bleu * 100
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