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eval.py
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eval.py
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import logging
import os
import time
import tensorflow as tf
from models import load_model
import utils.iterator_utils as iterator_utils
import utils.text_utils as text_utils
from utils import bleu, metrics
from vocab.vocab_utils import build_reverse_vocab_table, convert_to_bpe, load_vocab_table
from utils.bleu_moses import moses_multi_bleu
from hparams import hparams
from argparse import ArgumentParser
logger = logging.getLogger("eval")
logging.basicConfig(level=logging.DEBUG, format="%(asctime)s\t%(message)s")
def check_vocab(args):
if not hparams.use_bpe:
return
source = args.source
target = args.target
src_bpe_file = os.path.join(args.model_path, 'bpe-{}.src'.format(hparams.bpe_num_symbols))
tgt_bpe_file = os.path.join(args.model_path, 'bpe-{}.tgt'.format(hparams.bpe_num_symbols))
in_src_bpe_file = os.path.join(args.model_path, 'bpe-input-{}.src'.format(hparams.bpe_num_symbols))
src_sents = convert_to_bpe(source, src_bpe_file)
with open(in_src_bpe_file, 'w', encoding='utf8') as f:
f.write('\n'.join(src_sents))
in_tgt_bpe_file = os.path.join(args.model_path, 'bpe-input-{}.tgt'.format(hparams.bpe_num_symbols))
tgt_sents = convert_to_bpe(target, tgt_bpe_file)
with open(in_tgt_bpe_file, 'w', encoding='utf8') as f:
f.write('\n'.join(tgt_sents))
def load_dataset(args):
in_src_file = args.source
in_tgt_file = args.target
if hparams.use_bpe:
in_src_file = os.path.join(args.model_path, 'bpe-input-{}.src'.format(hparams.bpe_num_symbols))
in_tgt_file = os.path.join(args.model_path, 'bpe-input-{}.tgt'.format(hparams.bpe_num_symbols))
src_vocab_file = os.path.join(args.model_path, 'vocab.{}.src'.format(hparams.bpe_num_symbols))
tgt_vocab_file = os.path.join(args.model_path, 'vocab.{}.tgt'.format(hparams.bpe_num_symbols))
else:
src_vocab_file = os.path.join(args.model_path, 'vocab.src')
tgt_vocab_file = os.path.join(args.model_path, 'vocab.tgt')
src_vocab, src_vocab_size = load_vocab_table(src_vocab_file)
tgt_vocab, tgt_vocab_size = load_vocab_table(tgt_vocab_file)
tgt_reverse_vocab = build_reverse_vocab_table(tgt_vocab_file, hparams)
with open(in_src_file, 'r', encoding='utf8') as f:
src_data_size = len(f.readlines())
src_dataset = tf.data.TextLineDataset(in_src_file)
tgt_dataset = tf.data.TextLineDataset(in_tgt_file)
return (src_vocab, tgt_vocab, src_dataset, tgt_dataset, tgt_reverse_vocab, src_vocab_size, tgt_vocab_size), src_data_size
def bytes2sent(byte_sents, eos):
sents = []
for byte_sent in byte_sents:
sent = text_utils.format_bpe_text(byte_sent, eos)
sent = sent.replace(' ##', '')
sents.append(sent)
return sents
def bpe2sent(bpe_sents, eos):
sents = []
for bpe_sent in bpe_sents:
sent = text_utils.format_bpe_text(bpe_sent, eos)
sents.append(sent)
return sents
def compute_bleu_score(references, translations, max_order=4, smooth=False):
bleu_score, _, _, _, _, _ = bleu.compute_bleu(references, translations, max_order, smooth)
print(bleu_score)
return bleu_score * 100
def main(args, max_data_size=0, shuffle=True, display=False):
hparams.set_hparam('batch_size', 10)
hparams.add_hparam('is_training', False)
check_vocab(args)
datasets, src_data_size = load_dataset(args)
iterator = iterator_utils.get_eval_iterator(hparams, datasets, hparams.eos, shuffle=shuffle)
src_vocab, tgt_vocab, src_dataset, tgt_dataset, tgt_reverse_vocab, src_vocab_size, tgt_vocab_size = datasets
hparams.add_hparam('vocab_size_source', src_vocab_size)
hparams.add_hparam('vocab_size_target', tgt_vocab_size)
sess, model = load_model(hparams, tf.contrib.learn.ModeKeys.EVAL, iterator, src_vocab, tgt_vocab, tgt_reverse_vocab)
if args.restore_step:
checkpoint_path = os.path.join(args.model_path, 'nmt.ckpt')
ckpt = '%s-%d' % (checkpoint_path, args.restore_step)
else:
ckpt = tf.train.latest_checkpoint(args.model_path)
saver = tf.train.Saver(tf.global_variables(), max_to_keep=5)
if ckpt:
saver.restore(sess, ckpt)
else:
raise Exception("can not found checkpoint file")
src_vocab_file = os.path.join(args.model_path, 'vocab.src')
src_reverse_vocab = build_reverse_vocab_table(src_vocab_file, hparams)
sess.run(tf.tables_initializer())
step_count = 1
with sess:
logger.info("starting evaluating...")
sess.run(iterator.initializer)
eos = hparams.eos.encode()
references = []
translations = []
start_time = time.time()
while True:
try:
if (max_data_size > 0) and (step_count * hparams.batch_size > max_data_size):
break
if step_count % 10 == 0:
t = time.time() - start_time
logger.info('step={0} total={1} time={2:.3f}'.format(step_count, step_count * hparams.batch_size, t))
start_time = time.time()
predictions, source, target, source_text, confidence = model.eval(sess)
reference = bpe2sent(target, eos)
if hparams.beam_width == 1:
translation = bytes2sent(list(predictions), eos)
else:
translation = bytes2sent(list(predictions[:, 0]), eos)
for s, r, t in zip(source, reference, translation):
if display:
source_sent = src_reverse_vocab.lookup(tf.constant(list(s), tf.int64))
source_sent = sess.run(source_sent)
source_sent = text_utils.format_bpe_text(source_sent, eos)
print('{}\n{}\n{}\n'.format(source_sent, r, t))
references.append(r)
translations.append(t)
if step_count % 100 == 0:
bleu_score = moses_multi_bleu(references, translations, args.model_path)
logger.info('bleu score = {0:.3f}'.format(bleu_score))
step_count += 1
except tf.errors.OutOfRangeError:
logger.info('Done eval data')
break
logger.info('compute bleu score...')
# bleu_score = compute_bleu_score(references, translations)
bleu_score = moses_multi_bleu(references, translations, args.model_path)
logger.info('bleu score = {0:.3f}'.format(bleu_score))
if __name__ == '__main__':
parser = ArgumentParser()
parser.add_argument('--source', required=True)
parser.add_argument('--target', required=True)
parser.add_argument('--model_path', required=True)
parser.add_argument('--restore_step', default=0)
args = parser.parse_args()
main(args)