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"""Common functions for ST and MT.""" |
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import nltk |
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import numpy as np |
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class ErrorCalculator(object): |
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"""Calculate BLEU for ST and MT models during training. |
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:param y_hats: numpy array with predicted text |
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:param y_pads: numpy array with true (target) text |
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:param char_list: vocabulary list |
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:param sym_space: space symbol |
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:param sym_pad: pad symbol |
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:param report_bleu: report BLUE score if True |
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""" |
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def __init__(self, char_list, sym_space, sym_pad, report_bleu=False): |
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"""Construct an ErrorCalculator object.""" |
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super(ErrorCalculator, self).__init__() |
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self.char_list = char_list |
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self.space = sym_space |
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self.pad = sym_pad |
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self.report_bleu = report_bleu |
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if self.space in self.char_list: |
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self.idx_space = self.char_list.index(self.space) |
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else: |
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self.idx_space = None |
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def __call__(self, ys_hat, ys_pad): |
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"""Calculate corpus-level BLEU score. |
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:param torch.Tensor ys_hat: prediction (batch, seqlen) |
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:param torch.Tensor ys_pad: reference (batch, seqlen) |
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:return: corpus-level BLEU score in a mini-batch |
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:rtype float |
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""" |
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bleu = None |
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if not self.report_bleu: |
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return bleu |
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bleu = self.calculate_corpus_bleu(ys_hat, ys_pad) |
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return bleu |
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def calculate_corpus_bleu(self, ys_hat, ys_pad): |
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"""Calculate corpus-level BLEU score in a mini-batch. |
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:param torch.Tensor seqs_hat: prediction (batch, seqlen) |
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:param torch.Tensor seqs_true: reference (batch, seqlen) |
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:return: corpus-level BLEU score |
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:rtype float |
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""" |
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seqs_hat, seqs_true = [], [] |
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for i, y_hat in enumerate(ys_hat): |
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y_true = ys_pad[i] |
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eos_true = np.where(y_true == -1)[0] |
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ymax = eos_true[0] if len(eos_true) > 0 else len(y_true) |
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seq_hat = [self.char_list[int(idx)] for idx in y_hat[:ymax]] |
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seq_true = [self.char_list[int(idx)] for idx in y_true if int(idx) != -1] |
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seq_hat_text = "".join(seq_hat).replace(self.space, " ") |
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seq_hat_text = seq_hat_text.replace(self.pad, "") |
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seq_true_text = "".join(seq_true).replace(self.space, " ") |
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seqs_hat.append(seq_hat_text) |
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seqs_true.append(seq_true_text) |
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bleu = nltk.bleu_score.corpus_bleu([[ref] for ref in seqs_true], seqs_hat) |
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return bleu * 100 |
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