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""" Translation main class """
import os
import torch
from onmt.constants import DefaultTokens
from onmt.inputters.text_dataset import TextMultiField
from onmt.utils.alignment import build_align_pharaoh
class TranslationBuilder(object):
"""
Build a word-based translation from the batch output
of translator and the underlying dictionaries.
Replacement based on "Addressing the Rare Word
Problem in Neural Machine Translation" :cite:`Luong2015b`
Args:
data (onmt.inputters.Dataset): Data.
fields (List[Tuple[str, torchtext.data.Field]]): data fields
n_best (int): number of translations produced
replace_unk (bool): replace unknown words using attention
has_tgt (bool): will the batch have gold targets
"""
def __init__(self, data, fields, n_best=1, replace_unk=False,
has_tgt=False, phrase_table=""):
self.data = data
self.fields = fields
self._has_text_src = isinstance(
dict(self.fields)["src"], TextMultiField)
self.n_best = n_best
self.replace_unk = replace_unk
self.phrase_table_dict = {}
if phrase_table != "" and os.path.exists(phrase_table):
with open(phrase_table) as phrase_table_fd:
for line in phrase_table_fd:
phrase_src, phrase_trg = line.rstrip("\n").split(
DefaultTokens.PHRASE_TABLE_SEPARATOR)
self.phrase_table_dict[phrase_src] = phrase_trg
self.has_tgt = has_tgt
def _build_target_tokens(self, src, src_vocab, src_raw, pred, attn):
tgt_field = dict(self.fields)["tgt"].base_field
vocab = tgt_field.vocab
tokens = []
for tok in pred:
if tok < len(vocab):
tokens.append(vocab.itos[tok])
else:
tokens.append(src_vocab.itos[tok - len(vocab)])
if tokens[-1] == tgt_field.eos_token:
tokens = tokens[:-1]
break
if self.replace_unk and attn is not None and src is not None:
for i in range(len(tokens)):
if tokens[i] == tgt_field.unk_token:
_, max_index = attn[i][:len(src_raw)].max(0)
tokens[i] = src_raw[max_index.item()]
if self.phrase_table_dict:
src_tok = src_raw[max_index.item()]
if src_tok in self.phrase_table_dict:
tokens[i] = self.phrase_table_dict[src_tok]
return tokens
def from_batch(self, translation_batch):
batch = translation_batch["batch"]
assert(len(translation_batch["gold_score"]) ==
len(translation_batch["predictions"]))
batch_size = batch.batch_size
preds, pred_score, attn, align, gold_score, indices = list(zip(
*sorted(zip(translation_batch["predictions"],
translation_batch["scores"],
translation_batch["attention"],
translation_batch["alignment"],
translation_batch["gold_score"],
batch.indices.data),
key=lambda x: x[-1])))
if not any(align): # when align is a empty nested list
align = [None] * batch_size
# Sorting
inds, perm = torch.sort(batch.indices)
if self._has_text_src:
src = batch.src[0][:, :, 0].index_select(1, perm)
else:
src = None
tgt = batch.tgt[:, :, 0].index_select(1, perm) \
if self.has_tgt else None
translations = []
for b in range(batch_size):
if self._has_text_src:
src_vocab = self.data.src_vocabs[inds[b]] \
if self.data.src_vocabs else None
src_raw = self.data.examples[inds[b]].src[0]
else:
src_vocab = None
src_raw = None
pred_sents = [self._build_target_tokens(
src[:, b] if src is not None else None,
src_vocab, src_raw,
preds[b][n],
align[b][n] if align[b] is not None else attn[b][n])
for n in range(self.n_best)]
gold_sent = None
if tgt is not None:
gold_sent = self._build_target_tokens(
src[:, b] if src is not None else None,
src_vocab, src_raw,
tgt[1:, b] if tgt is not None else None, None)
translation = Translation(
src[:, b] if src is not None else None,
src_raw, pred_sents, attn[b], pred_score[b],
gold_sent, gold_score[b], align[b]
)
translations.append(translation)
return translations
class Translation(object):
"""Container for a translated sentence.
Attributes:
src (LongTensor): Source word IDs.
src_raw (List[str]): Raw source words.
pred_sents (List[List[str]]): Words from the n-best translations.
pred_scores (List[List[float]]): Log-probs of n-best translations.
attns (List[FloatTensor]) : Attention distribution for each
translation.
gold_sent (List[str]): Words from gold translation.
gold_score (List[float]): Log-prob of gold translation.
word_aligns (List[FloatTensor]): Words Alignment distribution for
each translation.
"""
__slots__ = ["src", "src_raw", "pred_sents", "attns", "pred_scores",
"gold_sent", "gold_score", "word_aligns"]
def __init__(self, src, src_raw, pred_sents,
attn, pred_scores, tgt_sent, gold_score, word_aligns):
self.src = src
self.src_raw = src_raw
self.pred_sents = pred_sents
self.attns = attn
self.pred_scores = pred_scores
self.gold_sent = tgt_sent
self.gold_score = gold_score
self.word_aligns = word_aligns
def log(self, sent_number):
"""
Log translation.
"""
msg = ['\nSENT {}: {}\n'.format(sent_number, self.src_raw)]
best_pred = self.pred_sents[0]
best_score = self.pred_scores[0]
pred_sent = ' '.join(best_pred)
msg.append('PRED {}: {}\n'.format(sent_number, pred_sent))
msg.append("PRED SCORE: {:.4f}\n".format(best_score))
if self.word_aligns is not None:
pred_align = self.word_aligns[0]
pred_align_pharaoh = build_align_pharaoh(pred_align)
pred_align_sent = ' '.join(pred_align_pharaoh)
msg.append("ALIGN: {}\n".format(pred_align_sent))
if self.gold_sent is not None:
tgt_sent = ' '.join(self.gold_sent)
msg.append('GOLD {}: {}\n'.format(sent_number, tgt_sent))
msg.append(("GOLD SCORE: {:.4f}\n".format(self.gold_score)))
if len(self.pred_sents) > 1:
msg.append('\nBEST HYP:\n')
for score, sent in zip(self.pred_scores, self.pred_sents):
msg.append("[{:.4f}] {}\n".format(score, sent))
return "".join(msg)
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