test_serving_api.py

# -*- coding: utf-8 -*-
"""
   File Name：     test_serving
   Description :
   Author :       逸轩
   date：          2019/10/12

"""
from flask import Flask, request
from flask_cors import *
flaskAPP = Flask(import_name=__name__)
CORS(flaskAPP, supports_credentials=True)

import json
import tensorflow as tf
import tokenization
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '0'

flags = tf.flags

FLAGS = flags.FLAGS

## Required parameters
BERT_BASE_DIR = "./model_files/roberta-base-zh_law_epoch1/"  # "../model_files/inference_with_reason/checkpoint_bert/"

flags.DEFINE_string("bert_config_file", BERT_BASE_DIR + "bert_config.json",
                    "The config json file corresponding to the pre-trained BERT model. "
                    "This specifies the model architecture.")

flags.DEFINE_string("task_name", "sentence_pair", "The name of the task to train.")

flags.DEFINE_string("vocab_file", BERT_BASE_DIR + "vocab.txt",
                    "The vocabulary file that the BERT model was trained on.")

flags.DEFINE_string("init_checkpoint", BERT_BASE_DIR,  # model.ckpt-66870--> /model.ckpt-66870
                    "Initial checkpoint (usually from a pre-trained BERT model).")

flags.DEFINE_integer("max_seq_length", 128, # 128
                     "The maximum total input sequence length after WordPiece tokenization. "
                     "Sequences longer than this will be truncated, and sequences shorter "
                     "than this will be padded.")

flags.DEFINE_bool(
    "do_lower_case", True,
    "Whether to lower case the input text. Should be True for uncased "
    "models and False for cased models.")

flags.DEFINE_string("c", "gunicorn.conf",
                    "gunicorn.conf")  # data/sgns.target.word-word.dynwin5.thr10.neg5.dim300.iter5--->data/news_12g_baidubaike_20g_novel_90g_embedding_64.bin--->sgns.merge.char


class InputExample(object):
  """A single training/test example for simple sequence classification."""

  def __init__(self, guid, text_a, text_b=None, label=None):
    """Constructs a InputExample.

    Args:
      guid: Unique id for the example.
      text_a: string. The untokenized text of the first sequence. For single
        sequence tasks, only this sequence must be specified.
      text_b: (Optional) string. The untokenized text of the second sequence.
        Only must be specified for sequence pair tasks.
      label: (Optional) string. The label of the example. This should be
        specified for train and dev examples, but not for test examples.
    """
    self.guid = guid
    self.text_a = text_a
    self.text_b = text_b
    self.label = label

class PaddingInputExample(object):
  """Fake example so the num input examples is a multiple of the batch size.

  When running eval/predict on the TPU, we need to pad the number of examples
  to be a multiple of the batch size, because the TPU requires a fixed batch
  size. The alternative is to drop the last batch, which is bad because it means
  the entire output data won't be generated.

  We use this class instead of `None` because treating `None` as padding
  battches could cause silent errors.
  """


class InputFeatures(object):
  """A single set of features of data."""

  def __init__(self,
               input_ids,
               input_mask,
               segment_ids,
               label_id,
               is_real_example=True):
    self.input_ids = input_ids
    self.input_mask = input_mask
    self.segment_ids = segment_ids
    self.label_id = label_id
    self.is_real_example = is_real_example

def _truncate_seq_pair(tokens_a, tokens_b, max_length):
  """Truncates a sequence pair in place to the maximum length."""

  # This is a simple heuristic which will always truncate the longer sequence
  # one token at a time. This makes more sense than truncating an equal percent
  # of tokens from each, since if one sequence is very short then each token
  # that's truncated likely contains more information than a longer sequence.
  while True:
    total_length = len(tokens_a) + len(tokens_b)
    if total_length <= max_length:
      break
    if len(tokens_a) > len(tokens_b):
      tokens_a.pop()
    else:
      tokens_b.pop()


def convert_single_example(ex_index, example, label_list, max_seq_length,
                           tokenizer):
    """Converts a single `InputExample` into a single `InputFeatures`."""

    if isinstance(example, PaddingInputExample):
        return InputFeatures(
            input_ids=[0] * max_seq_length,
            input_mask=[0] * max_seq_length,
            segment_ids=[0] * max_seq_length,
            label_id=0,
            is_real_example=False)

    label_map = {}
    for (i, label) in enumerate(label_list):
        label_map[label] = i

    tokens_a = tokenizer.tokenize(example.text_a)
    tokens_b = None
    if example.text_b:
        tokens_b = tokenizer.tokenize(example.text_b)

    if tokens_b:
        # Modifies `tokens_a` and `tokens_b` in place so that the total
        # length is less than the specified length.
        # Account for [CLS], [SEP], [SEP] with "- 3"
        _truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)
    else:
        # Account for [CLS] and [SEP] with "- 2"
        if len(tokens_a) > max_seq_length - 2:
            tokens_a = tokens_a[0:(max_seq_length - 2)]

    # The convention in BERT is:
    # (a) For sequence pairs:
    #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
    #  type_ids: 0     0  0    0    0     0       0 0     1  1  1  1   1 1
    # (b) For single sequences:
    #  tokens:   [CLS] the dog is hairy . [SEP]
    #  type_ids: 0     0   0   0  0     0 0
    #
    # Where "type_ids" are used to indicate whether this is the first
    # sequence or the second sequence. The embedding vectors for `type=0` and
    # `type=1` were learned during pre-training and are added to the wordpiece
    # embedding vector (and position vector). This is not *strictly* necessary
    # since the [SEP] token unambiguously separates the sequences, but it makes
    # it easier for the model to learn the concept of sequences.
    #
    # For classification tasks, the first vector (corresponding to [CLS]) is
    # used as the "sentence vector". Note that this only makes sense because
    # the entire model is fine-tuned.
    tokens = []
    segment_ids = []
    tokens.append("[CLS]")
    segment_ids.append(0)
    for token in tokens_a:
        tokens.append(token)
        segment_ids.append(0)
    tokens.append("[SEP]")
    segment_ids.append(0)

    if tokens_b:
        for token in tokens_b:
            tokens.append(token)
            segment_ids.append(1)
        tokens.append("[SEP]")
        segment_ids.append(1)

    input_ids = tokenizer.convert_tokens_to_ids(tokens)

    # The mask has 1 for real tokens and 0 for padding tokens. Only real
    # tokens are attended to.
    input_mask = [1] * len(input_ids)

    # Zero-pad up to the sequence length.
    while len(input_ids) < max_seq_length:
        input_ids.append(0)
        input_mask.append(0)
        segment_ids.append(0)

    assert len(input_ids) == max_seq_length
    assert len(input_mask) == max_seq_length
    assert len(segment_ids) == max_seq_length

    # debug xmxoxo 2019/3/13
    # print(ex_index,example.text_a)

    label_id = label_map[example.label]
    if ex_index < 5:
        tf.logging.info("*** Example ***")
        tf.logging.info("guid: %s" % (example.guid))
        tf.logging.info("tokens: %s" % " ".join(
            [tokenization.printable_text(x) for x in tokens]))
        tf.logging.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
        tf.logging.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
        tf.logging.info("segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
        tf.logging.info("label: %s (id = %d)" % (example.label, label_id))

    feature = InputFeatures(
        input_ids=input_ids,
        input_mask=input_mask,
        segment_ids=segment_ids,
        label_id=label_id,
        is_real_example=True)
    return feature

label_list = ['0', '1']
max_seq_length = 128
gpu_config = tf.ConfigProto()
gpu_config.gpu_options.allow_growth = True

global graph
graph = tf.Graph()  # tf.get_default_graph()
global sess
sess = tf.Session(config=gpu_config, graph=graph)
# with sess2:
with graph.as_default():
    print("BERT.going to restore checkpoint:"+FLAGS.init_checkpoint)

    predict_fn = tf.contrib.predictor.from_saved_model('exported/1571297973')
    tokenizer = tokenization.FullTokenizer(vocab_file='roberta_zh_l12/vocab.txt', do_lower_case=True)
    print('模型加载完毕！正在监听》》》')

@flaskAPP.route('/predict_online', methods=['GET', 'POST'])
def predict_online():
    import time
    start = time.time()
    question = request.args.get('query')
    type_info = request.args.get('type_info')
    predict_example = InputExample("id", question, type_info, '0')
    feature = convert_single_example(100, predict_example, label_list,
                                     max_seq_length, tokenizer)

    prediction = predict_fn({
        "input_ids": [feature.input_ids],
        "input_mask": [feature.input_mask],
        "segment_ids": [feature.segment_ids],
        "label_ids": [feature.label_id],
    })
    probabilities = prediction["probabilities"]
    print(probabilities)
    label = label_list[probabilities.argmax()]
    probabilities_list = probabilities.tolist()
    probabilities_list = probabilities_list[0]
    simi = probabilities_list[int(label)]
    print(label)
    result = {'result': {'label': label, 'simi':simi, 'prob_marix': probabilities_list}}
    end = time.time()
    print('time:{}seconds'.format(end-start))
    return json.dumps(result, ensure_ascii=False)


flaskAPP.run(host='192.168.9.23', port=5009, debug=True)