ner任务 tf_serving 调用问题

[kehlaaa]

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Question

你好，我按照v2.0.1文档中提到的方法，将 Bert + CRF 实现的一个NER模型使用tf_serving docker部署好了，

import requests
import numpy as np
from kashgari.processors import load_processors_from_model

text_processor, label_processor = load_processors_from_model('/Users/brikerman/Desktop/tf-serving/1603683152')

samples = [
    ['hello', 'world'],
    ['你', '好', '世', '界']
]
tensor = text_processor.transform(samples)

instances = [{
   "Input-Token": i.tolist(),
   "Input-Segment": np.zeros(i.shape).tolist()
} for i in tensor]

# predict
r = requests.post("http://localhost:8501/v1/models/bgru:predict", json={"instances": instances})
predictions = r.json()['predictions']
# Convert result back to labels
labels = label_processor.inverse_transform(np.array(predictions).argmax(-1))
print(labels)

在最后输出类型时，报了如下错误：

Traceback (most recent call last):
  File "ner_pred.py", line 168, in <module>
    labels = label_processor.inverse_transform(np.array(predictions).argmax(-1))
  File "/usr/local/miniconda3/lib/python3.7/site-packages/kashgari/processors/sequence_processor.py", line 154, in inverse_transform
    for index, seq in enumerate(labels):
TypeError: 'numpy.int64' object is not iterable

因为接触深度学习时间较短，问题比较基础，麻烦解答一下，谢谢