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translate.py
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from __future__ import division
from builtins import bytes
import os
import argparse
import math
import codecs
import torch
import onmt
import onmt.IO
import opts
from itertools import takewhile, count
try:
from itertools import zip_longest
except ImportError:
from itertools import izip_longest as zip_longest
parser = argparse.ArgumentParser(description='translate.py')
opts.add_md_help_argument(parser)
parser.add_argument('-model', required=True,
help='Path to model .pt file')
parser.add_argument('-src', required=True,
help='Source sequence to decode (one line per sequence)')
parser.add_argument('-src_img_dir', default="",
help='Source image directory')
parser.add_argument('-tgt',
help='True target sequence (optional)')
parser.add_argument('-output', default='pred.txt',
help="""Path to output the predictions (each line will
be the decoded sequence""")
parser.add_argument('-beam_size', type=int, default=5,
help='Beam size')
parser.add_argument('-batch_size', type=int, default=30,
help='Batch size')
parser.add_argument('-max_sent_length', type=int, default=100,
help='Maximum sentence length.')
parser.add_argument('-replace_unk', action="store_true",
help="""Replace the generated UNK tokens with the source
token that had highest attention weight. If phrase_table
is provided, it will lookup the identified source token and
give the corresponding target token. If it is not provided
(or the identified source token does not exist in the
table) then it will copy the source token""")
parser.add_argument('-verbose', action="store_true",
help='Print scores and predictions for each sentence')
parser.add_argument('-attn_debug', action="store_true",
help='Print best attn for each word')
parser.add_argument('-dump_beam', type=str, default="",
help='File to dump beam information to.')
parser.add_argument('-n_best', type=int, default=1,
help="""If verbose is set, will output the n_best
decoded sentences""")
parser.add_argument('-gpu', type=int, default=-1,
help="Device to run on")
# options most relevant to summarization
parser.add_argument('-dynamic_dict', action='store_true',
help="Create dynamic dictionaries")
parser.add_argument('-share_vocab', action='store_true',
help="Share source and target vocabulary")
def report_score(name, score_total, words_total):
print("%s AVG SCORE: %.4f, %s PPL: %.4f" % (
name, score_total / words_total,
name, math.exp(-score_total/words_total)))
def get_src_words(src_indices, index2str):
words = []
raw_words = (index2str[i] for i in src_indices)
words = takewhile(lambda w: w != onmt.IO.PAD_WORD, raw_words)
return " ".join(words)
def main():
opt = parser.parse_args()
dummy_parser = argparse.ArgumentParser(description='train.py')
opts.model_opts(dummy_parser)
dummy_opt = dummy_parser.parse_known_args([])[0]
opt.cuda = opt.gpu > -1
if opt.cuda:
torch.cuda.set_device(opt.gpu)
translator = onmt.Translator(opt, dummy_opt.__dict__)
out_file = codecs.open(opt.output, 'w', 'utf-8')
pred_score_total, pred_words_total = 0, 0
gold_score_total, gold_words_total = 0, 0
if opt.dump_beam != "":
import json
translator.initBeamAccum()
data = onmt.IO.ONMTDataset(opt.src, opt.tgt, translator.fields, None)
test_data = onmt.IO.OrderedIterator(
dataset=data, device=opt.gpu,
batch_size=opt.batch_size, train=False, sort=False,
shuffle=False)
counter = count(1)
for batch in test_data:
pred_batch, gold_batch, pred_scores, gold_scores, attn, src \
= translator.translate(batch, data)
pred_score_total += sum(score[0] for score in pred_scores)
pred_words_total += sum(len(x[0]) for x in pred_batch)
if opt.tgt:
gold_score_total += sum(gold_scores)
gold_words_total += sum(len(x) for x in batch.tgt[1:])
# z_batch: an iterator over the predictions, their scores,
# the gold sentence, its score, and the source sentence for each
# sentence in the batch. It has to be zip_longest instead of
# plain-old zip because the gold_batch has length 0 if the target
# is not included.
z_batch = zip_longest(
pred_batch, gold_batch,
pred_scores, gold_scores,
(sent.squeeze(1) for sent in src.split(1, dim=1)))
for pred_sents, gold_sent, pred_score, gold_score, src_sent in z_batch:
n_best_preds = [" ".join(pred) for pred in pred_sents[:opt.n_best]]
out_file.write('\n'.join(n_best_preds))
out_file.write('\n')
out_file.flush()
if opt.verbose:
sent_number = next(counter)
words = get_src_words(
src_sent, translator.fields["src"].vocab.itos)
os.write(1, bytes('\nSENT %d: %s\n' %
(sent_number, words), 'UTF-8'))
best_pred = n_best_preds[0]
best_score = pred_score[0]
os.write(1, bytes('PRED %d: %s\n' %
(sent_number, best_pred), 'UTF-8'))
print("PRED SCORE: %.4f" % best_score)
if opt.tgt:
tgt_sent = ' '.join(gold_sent)
os.write(1, bytes('GOLD %d: %s\n' %
(sent_number, tgt_sent), 'UTF-8'))
print("GOLD SCORE: %.4f" % gold_score)
if len(n_best_preds) > 1:
print('\nBEST HYP:')
for score, sent in zip(pred_score, n_best_preds):
os.write(1, bytes("[%.4f] %s\n" % (score, sent),
'UTF-8'))
report_score('PRED', pred_score_total, pred_words_total)
if opt.tgt:
report_score('GOLD', gold_score_total, gold_words_total)
if opt.dump_beam:
json.dump(translator.beam_accum,
codecs.open(opt.dump_beam, 'w', 'utf-8'))
if __name__ == "__main__":
main()