def inference_bucket(config):
"""Inference for bucket.
"""
# create model and compile
model = Model(config)
model.compile()
sess = model.sess
# restore model
saver = tf.train.Saver()
saver.restore(sess, config.restore)
sys.stderr.write('model restored' + '\n')
'''
print(tf.global_variables())
print(tf.trainable_variables())
'''
num_buckets = 0
total_duration_time = 0.0
bucket = []
while 1:
try:
line = sys.stdin.readline()
except KeyboardInterrupt:
break
if not line: break
line = line.strip()
if not line and len(bucket) >= 1:
start_time = time.time()
inp, feed_dict = feed.build_input_feed_dict(model, bucket, Input)
if 'bert' in config.emb_class:
# compute bert embedding at runtime
bert_embeddings = sess.run([model.bert_embeddings_subgraph],
feed_dict=feed_dict)
# update feed_dict
feed_dict[model.bert_embeddings] = feed.align_bert_embeddings(
config, bert_embeddings,
inp.example['bert_wordidx2tokenidx'], -1)
logits_indices, sentence_lengths = sess.run(
[model.logits_indices, model.sentence_lengths],
feed_dict=feed_dict)
tags = config.logit_indices_to_tags(logits_indices[0],
sentence_lengths[0])
for i in range(len(bucket)):
out = bucket[i] + ' ' + tags[i]
sys.stdout.write(out + '\n')
sys.stdout.write('\n')
bucket = []
duration_time = time.time() - start_time
out = 'duration_time : ' + str(duration_time) + ' sec'
tf.logging.info(out)
num_buckets += 1
if num_buckets != 1: # first one may takes longer time, so ignore in computing duration.
total_duration_time += duration_time
if line: bucket.append(line)
if len(bucket) != 0:
start_time = time.time()
inp, feed_dict = feed.build_input_feed_dict(model, bucket, Input)
if 'bert' in config.emb_class:
# compute bert embedding at runtime
bert_embeddings = sess.run([model.bert_embeddings_subgraph],
feed_dict=feed_dict)
# update feed_dict
feed_dict[model.bert_embeddings] = feed.align_bert_embeddings(
config, bert_embeddings, inp.example['bert_wordidx2tokenidx'],
-1)
logits_indices, sentence_lengths = sess.run(
[model.logits_indices, model.sentence_lengths],
feed_dict=feed_dict)
tags = config.logit_indices_to_tags(logits_indices[0],
sentence_lengths[0])
for i in range(len(bucket)):
out = bucket[i] + ' ' + tags[i]
sys.stdout.write(out + '\n')
sys.stdout.write('\n')
duration_time = time.time() - start_time
out = 'duration_time : ' + str(duration_time) + ' sec'
tf.logging.info(out)
num_buckets += 1
total_duration_time += duration_time
out = 'total_duration_time : ' + str(total_duration_time) + ' sec' + '\n'
out += 'average processing time / bucket : ' + str(
total_duration_time / (num_buckets - 1)) + ' sec'
tf.logging.info(out)
sess.close()
def inference_line(config):
"""Inference for raw string.
"""
def get_entity(doc, begin, end):
for ent in doc.ents:
# check included
if ent.start_char <= begin and end <= ent.end_char:
if ent.start_char == begin: return 'B-' + ent.label_
else: return 'I-' + ent.label_
return 'O'
def build_bucket(nlp, line):
bucket = []
doc = nlp(line)
for token in doc:
begin = token.idx
end = begin + len(token.text) - 1
temp = []
'''
print(token.i, token.text, token.lemma_, token.pos_, token.tag_, token.dep_,
token.shape_, token.is_alpha, token.is_stop, begin, end)
'''
temp.append(token.text)
temp.append(token.tag_)
temp.append('O') # no chunking info
entity = get_entity(doc, begin, end)
temp.append(entity) # entity by spacy
temp = ' '.join(temp)
bucket.append(temp)
return bucket
import spacy
nlp = spacy.load('en')
# create model and compile
model = Model(config)
model.compile()
sess = model.sess
# restore model
saver = tf.train.Saver()
saver.restore(sess, config.restore)
tf.logging.info('model restored' + '\n')
while 1:
try:
line = sys.stdin.readline()
except KeyboardInterrupt:
break
if not line: break
line = line.strip()
if not line: continue
# create bucket
try:
bucket = build_bucket(nlp, line)
except Exception as e:
sys.stderr.write(str(e) + '\n')
continue
inp, feed_dict = feed.build_input_feed_dict(model, bucket)
if 'bert' in config.emb_class:
# compute bert embedding at runtime
bert_embeddings = sess.run([model.bert_embeddings_subgraph],
feed_dict=feed_dict)
# update feed_dict
feed_dict[model.bert_embeddings] = feed.align_bert_embeddings(
config, bert_embeddings, inp.example['bert_wordidx2tokenidx'],
-1)
logits_indices, sentence_lengths = sess.run(
[model.logits_indices, model.sentence_lengths],
feed_dict=feed_dict)
tags = config.logit_indices_to_tags(logits_indices[0],
sentence_lengths[0])
for i in range(len(bucket)):
out = bucket[i] + ' ' + tags[i]
sys.stdout.write(out + '\n')
sys.stdout.write('\n')
sess.close()