def translate(args, net, src_vocab, tgt_vocab, active_out=None):
"done"
sentences = [l.split() for l in args.text]
translated = []
infer_dataset = ParallelDataset(args.text, args.ref_text, src_vocab,
tgt_vocab)
if args.batch_size is not None:
infer_dataset.BATCH_SIZE = args.batch_size
if args.max_batch_size is not None:
infer_dataset.max_batch_size = args.max_batch_size
if args.tokens_per_batch is not None:
infer_dataset.tokens_per_batch = args.tokens_per_batch
infer_dataiter = iter(
infer_dataset.get_iterator(shuffle=True,
group_by_size=True,
include_indices=True))
for (raw_batch, indices) in infer_dataiter:
src_mask = (raw_batch.src != src_vocab.stoi[config.PAD]).unsqueeze(-2)
if args.use_cuda:
src, src_mask = raw_batch.src.cuda(), src_mask.cuda()
else:
src = raw_batch.src
generated, gen_len = greedy(args, net, src, src_mask, src_vocab,
tgt_vocab)
new_translations = gen_batch2str(src, raw_batch.tgt, generated,
gen_len, src_vocab, tgt_vocab,
indices, active_out)
translated.extend(new_translations)
return translated
def translate(args, net, src_vocab, tgt_vocab):
"done"
sentences = [l.split() for l in args.text]
translated = []
infer_dataset = ParallelDataset(args.text, args.ref_text, src_vocab,
tgt_vocab)
if args.batch_size is not None:
infer_dataset.BATCH_SIZE = args.batch_size
if args.max_batch_size is not None:
infer_dataset.max_batch_size = args.max_batch_size
if args.tokens_per_batch is not None:
infer_dataset.tokens_per_batch = args.tokens_per_batch
infer_dataiter = iter(infer_dataset.get_iterator(True, True))
for raw_batch in infer_dataiter:
src_mask = (raw_batch.src != src_vocab.stoi[config.PAD]).unsqueeze(-2)
if args.use_cuda:
src, src_mask = raw_batch.src.cuda(), src_mask.cuda()
if args.greedy:
generated, gen_len = greedy(args, net, src, src_mask, src_vocab,
tgt_vocab)
else:
generated, gen_len = generate_beam(args, net, src, src_mask,
src_vocab, tgt_vocab)
new_translations = gen_batch2str(src, raw_batch.tgt, generated,
gen_len, src_vocab, tgt_vocab)
for res_sent in new_translations:
print(res_sent)
translated.extend(new_translations)
return translated
def translate(args, net, src_vocab, tgt_vocab):
"done"
sentences = [l.split() for l in args.text]
translated = []
if args.greedy:
infer_dataset = ParallelDataset(args.text, args.ref_text, src_vocab,
tgt_vocab)
if args.batch_size is not None:
infer_dataset.BATCH_SIZE = args.batch_size
if args.max_batch_size is not None:
infer_dataset.max_batch_size = args.max_batch_size
if args.tokens_per_batch is not None:
infer_dataset.tokens_per_batch = args.tokens_per_batch
infer_dataiter = iter(infer_dataset.get_iterator(True, True))
num_sents = 0
for raw_batch in infer_dataiter:
src_mask = (raw_batch.src !=
src_vocab.stoi[config.PAD]).unsqueeze(-2)
if args.use_cuda:
src, src_mask = raw_batch.src.cuda(), src_mask.cuda()
generated, gen_len = greedy(args, net, src, src_mask, src_vocab,
tgt_vocab)
new_translations = gen_batch2str(src, raw_batch.tgt, generated,
gen_len, src_vocab, tgt_vocab)
print('src size : {}'.format(src.size()))
'''
for res_sent in new_translations:
print(res_sent)
translated.extend(new_translations)
'''
else:
for i_s, sentence in enumerate(sentences):
s_trans = translate_sentence(sentence, net, args, src_vocab,
tgt_vocab)
s_trans = remove_special_tok(remove_bpe(s_trans))
translated.append(s_trans)
print(translated[-1])
return translated
