def dev_offline(file):
"""
do online prediction. each time make prediction for one instance.
you can change to a batch if you want.
:param line: a list. element is: [dummy_label,text_a,text_b]
:return:
"""
def convert(line, label):
feature = convert_single_example_dev(2, line, label, label2id,
FLAGS.max_seq_length, tokenizer)
input_ids = np.reshape([feature.input_ids], (1, FLAGS.max_seq_length))
input_mask = np.reshape([feature.input_mask],
(1, FLAGS.max_seq_length))
segment_ids = np.reshape([feature.segment_ids],
(1, FLAGS.max_seq_length))
label_ids = np.reshape([feature.label_ids], (1, FLAGS.max_seq_length))
return input_ids, input_mask, segment_ids, label_ids
global graph
with graph.as_default():
# sess.run(tf.global_variables_initializer())
input_ids_p = tf.placeholder(tf.int32, [1, FLAGS.max_seq_length],
name="input_ids")
input_mask_p = tf.placeholder(tf.int32, [1, FLAGS.max_seq_length],
name="input_mask")
label_ids_p = tf.placeholder(tf.int32, [1, FLAGS.max_seq_length],
name="label_ids")
segment_ids_p = tf.placeholder(tf.int32, [1, FLAGS.max_seq_length],
name="segment_ids")
bert_config = modeling_bert.BertConfig.from_json_file(
args.bert_config_file)
(total_loss, logits, trans,
pred_ids) = create_model(bert_config, args.is_training, input_ids_p,
input_mask_p, segment_ids_p, label_ids_p,
num_labels, args.use_one_hot_embeddings)
saver = tf.train.Saver()
saver.restore(sess, tf.train.latest_checkpoint(args.output_dir))
tokenizer = tokenization.FullTokenizer(
vocab_file=args.vocab_file, do_lower_case=FLAGS.do_lower_case)
# 获取id2char字典
id2char = tokenizer.inv_vocab
dev_texts, dev_labels = zip(*parse_file(file))
start = datetime.now()
pred_labels_all = []
true_labels_all = []
x_all = []
for index, text in enumerate(dev_texts):
sentence = str(text)
input_ids, input_mask, segment_ids, label_ids = convert(
sentence, dev_labels[index])
feed_dict = {
input_ids_p: input_ids,
input_mask_p: input_mask,
segment_ids_p: segment_ids,
label_ids_p: label_ids
}
# run session get current feed_dict result
y_pred = sess.run([pred_ids], feed_dict)
# print(list(y_pred[0][0]))
# print(len(list(y_pred[0][0])))
sent_tag = []
y_pred_clean = []
input_ids_clean = []
y_true_clean = []
# 去除 [CLS] 和 [SEP]获取正确的tag范围
for index_b, id in enumerate(list(np.reshape(input_ids, -1))):
char = id2char[id]
tag = id2label[list(y_pred[0][0])[index_b]]
if char == "[CLS]":
continue
if char == "[SEP]":
break
input_ids_clean.append(id)
sent_tag.append(tag)
y_pred_clean.append(list(y_pred[0][0])[index_b])
y_true_clean.append(label_ids[0][index_b])
pred_labels_all.append(y_pred_clean)
true_labels_all.append(y_true_clean)
x_all.append(input_ids_clean)
print('预测标签与真实标签评价结果......')
print(pred_labels_all)
print(len(pred_labels_all))
print(true_labels_all)
print(len(true_labels_all))
metrics = Metrics(true_labels_all,
pred_labels_all,
id2label,
remove_O=True)
metrics.report_scores()
# metrics.report_confusion_matrix()
print('预测实体与真实实体评价结果......')
precision, recall, f1 = entity_metrics(x_all, pred_labels_all,
true_labels_all, id2char,
id2label)
print("Dev P/R/F1: {} / {} / {}".format(round(precision, 2),
round(recall, 2), round(f1,
2)))
print('Time used: {} sec'.format((datetime.now() - start).seconds))
def predict_outline():
"""
do offline prediction. each time make prediction for one instance.
you can change to a batch if you want.
"""
# TODO 以文件形式预测结果
def convert(line):
feature = convert_single_example(line, label2id, FLAGS.max_seq_length,
tokenizer)
input_ids = np.reshape([feature.input_ids],
(args.batch_size, FLAGS.max_seq_length))
input_mask = np.reshape([feature.input_mask],
(args.batch_size, FLAGS.max_seq_length))
segment_ids = np.reshape([feature.segment_ids],
(args.batch_size, FLAGS.max_seq_length))
label_ids = np.reshape([feature.label_ids],
(args.batch_size, FLAGS.max_seq_length))
return input_ids, input_mask, segment_ids, label_ids
global graph
with graph.as_default():
print("going to restore checkpoint")
# sess.run(tf.global_variables_initializer())
input_ids_p = tf.placeholder(tf.int32,
[args.batch_size, FLAGS.max_seq_length],
name="input_ids")
input_mask_p = tf.placeholder(tf.int32,
[args.batch_size, FLAGS.max_seq_length],
name="input_mask")
label_ids_p = tf.placeholder(tf.int32,
[args.batch_size, FLAGS.max_seq_length],
name="label_ids")
segment_ids_p = tf.placeholder(tf.int32,
[args.batch_size, FLAGS.max_seq_length],
name="segment_ids")
bert_config = modeling_bert.BertConfig.from_json_file(
args.bert_config_file)
(total_loss, logits, trans,
pred_ids) = create_model(bert_config, args.is_training, input_ids_p,
input_mask_p, segment_ids_p, label_ids_p,
num_labels, args.use_one_hot_embeddings)
saver = tf.train.Saver()
saver.restore(sess, tf.train.latest_checkpoint(args.output_dir))
tokenizer = tokenization.FullTokenizer(
vocab_file=args.vocab_file, do_lower_case=FLAGS.do_lower_case)
# 获取id2char字典
id2char = tokenizer.inv_vocab
# TODO 以文件形式预测结果
while True:
print('input the test sentence:')
sentence = str(input())
start = datetime.now()
if len(sentence) < 2:
print(sentence)
continue
# print('your input is:{}'.format(sentence))
input_ids, input_mask, segment_ids, label_ids = convert(sentence)
feed_dict = {
input_ids_p: input_ids,
input_mask_p: input_mask,
segment_ids_p: segment_ids,
label_ids_p: label_ids
}
# run session get current feed_dict result
y_pred = sess.run([pred_ids], feed_dict)
sent_tag = []
y_pred_clean = []
input_ids_clean = []
# 去除 [CLS] 和 [SEP]获取正确的tag范围
for index, id in enumerate(list(np.reshape(input_ids, -1))):
char = id2char[id]
tag = id2label[list(y_pred[0][0])[index]]
if char == "[CLS]":
continue
if char == "[SEP]":
break
input_ids_clean.append(id)
sent_tag.append(tag)
y_pred_clean.append(list(y_pred[0][0])[index])
sent_tag = ' '.join(sent_tag)
print(sentence + '\n' + sent_tag)
entity = get_entity([sentence], [y_pred_clean], id2label)
print('predict_result:')
print(entity)
print('Time used: {} sec'.format((datetime.now() - start).seconds))