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inference.py
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inference.py
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from __future__ import print_function
import sys
import time
import argparse
import tensorflow as tf
import numpy as np
from embvec import EmbVec
from config import Config
from model import Model
from input import Input
import feed
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)):
predict = config.embvec.oot_tag # ex) 'O'
if i < sentence_lengths[0]: predict = tags[i]
out = bucket[i] + ' ' + predict
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 take 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)):
predict = config.embvec.oot_tag # ex) 'O'
if i < sentence_lengths[0]: predict = tags[i]
out = bucket[i] + ' ' + predict
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()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--emb_path', type=str, help='path to word embedding vector + vocab(.pkl)', required=True)
parser.add_argument('--config_path', type=str, default='data/config.json', help='path to config.json')
parser.add_argument('--wrd_dim', type=int, help='dimension of word embedding vector', required=True)
parser.add_argument('--word_length', type=int, default=15, help='max word length')
parser.add_argument('--restore', type=str, help='path to saved model(ex, ./checkpoint/ner_model)', required=True)
parser.add_argument('--mode', type=str, default='bulk', help='bulk, bucket, line')
args = parser.parse_args()
tf.logging.set_verbosity(tf.logging.INFO)
config = Config(args, is_training=False, emb_class='glove', use_crf=True)
if args.mode == 'bucket': inference_bucket(config)
if args.mode == 'line': inference_line(config)