run_classifier_pb.py

# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""BERT finetuning runner of classification for online prediction. input is a list. output is a label."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import csv
import os

import numpy as np
import tensorflow as tf

import modeling
import tokenization

flags = tf.flags

FLAGS = flags.FLAGS
#
# ## Required parameters
BERT_BASE_DIR = "F:\python_work\\bert\chinese_L-12_H-768_A-12\\"
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", "F:\python_work\\bert\chinese_L-12_H-768_A-12\\bert_model.ckpt.meta",
                    "Initial checkpoint (usually from a pre-trained BERT model).")

flags.DEFINE_integer("max_seq_length", 30,
                     "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.")


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

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


class DataProcessor(object):
    """Base class for data converters for sequence classification data sets."""

    def get_train_examples(self, data_dir):
        """Gets a collection of `InputExample`s for the train set."""
        raise NotImplementedError()

    def get_dev_examples(self, data_dir):
        """Gets a collection of `InputExample`s for the dev set."""
        raise NotImplementedError()

    def get_test_examples(self, data_dir):
        """Gets a collection of `InputExample`s for prediction."""
        raise NotImplementedError()

    def get_labels(self):
        """Gets the list of labels for this data set."""
        raise NotImplementedError()

    @classmethod
    def _read_tsv(cls, input_file, quotechar=None):
        """Reads a tab separated value file."""
        with tf.gfile.Open(input_file, "r") as f:
            reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
            lines = []
            for line in reader:
                lines.append(line)
            return lines


class SentencePairClassificationProcessor(DataProcessor):
    """Processor for the internal data set. sentence pair classification"""

    def __init__(self):
        self.language = "zh"

    def get_train_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")

    def get_dev_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")

    def get_test_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")

    def get_labels(self):
        """See base class."""
        return ["0", "1"]

    def _create_examples(self, lines, set_type):
        """Creates examples for the training and dev sets."""
        examples = []
        for (i, line) in enumerate(lines):
            if i == 0:
                continue
            guid = "%s-%s" % (set_type, i)
            label = tokenization.convert_to_unicode(line[0])
            text_a = tokenization.convert_to_unicode(line[1])
            text_b = tokenization.convert_to_unicode(line[2])
            examples.append(
                InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
        return examples


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

    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)
    return feature


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 create_int_feature(values):
    f = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values)))
    return f


def create_model(bert_config, is_training, input_ids, input_mask, segment_ids,
                 labels, num_labels, use_one_hot_embeddings):
    """Creates a classification model."""
    model = modeling.BertModel(
        config=bert_config,
        is_training=is_training,
        input_ids=input_ids,
        input_mask=input_mask,
        token_type_ids=segment_ids,
        use_one_hot_embeddings=use_one_hot_embeddings)

    # In the demo, we are doing a simple classification task on the entire
    # segment.
    #
    # If you want to use the token-level output, use model.get_sequence_output()
    # instead.
    output_layer = model.get_pooled_output()
    print(output_layer)

    hidden_size = output_layer.shape[-1].value

    output_weights = tf.get_variable(
        "output_weights", [num_labels, hidden_size],
        initializer=tf.truncated_normal_initializer(stddev=0.02))

    output_bias = tf.get_variable(
        "output_bias", [num_labels], initializer=tf.zeros_initializer())

    with tf.variable_scope("loss"):
        if is_training:
            # I.e., 0.1 dropout
            output_layer = tf.nn.dropout(output_layer, keep_prob=0.9)

        logits = tf.matmul(output_layer, output_weights, transpose_b=True)
        logits = tf.nn.bias_add(logits, output_bias)
        probabilities = tf.nn.softmax(logits, axis=-1)
        log_probs = tf.nn.log_softmax(logits, axis=-1)

        one_hot_labels = tf.one_hot(labels, depth=num_labels, dtype=tf.float32)

        per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1)
        loss = tf.reduce_mean(per_example_loss)

        return (loss, per_example_loss, logits, probabilities)


# tf.logging.set_verbosity(tf.logging.INFO)
processors = {
    "sentence_pair": SentencePairClassificationProcessor,
}
bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)
task_name = FLAGS.task_name.lower()
# print("task_name:", task_name)
processor = processors[task_name]()
label_list = processor.get_labels()
# lines_dev = processor.get_dev_examples("./TEXT_DIR")
index2label = {i: label_list[i] for i in range(len(label_list))}
tokenizer = tokenization.FullTokenizer(vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case)


def main(_):
    pass


# init mode and session
# move something codes outside of function, so that this code will run only once during online prediction when predict_online is invoked.
is_training = False
use_one_hot_embeddings = False
batch_size = 1
num_labels = len(label_list)
gpu_config = tf.ConfigProto()
gpu_config.gpu_options.allow_growth = True
sess = tf.Session(config=gpu_config)
model = None
input_ids_p, input_mask_p, label_ids_p, segment_ids_p = None, None, None, None

input_ids_p = tf.placeholder(tf.int32, [batch_size, FLAGS.max_seq_length], name="input_ids")
input_mask_p = tf.placeholder(tf.int32, [batch_size, FLAGS.max_seq_length], name="input_mask")
label_ids_p = tf.placeholder(tf.int32, [batch_size], name="label_ids")
segment_ids_p = tf.placeholder(tf.int32, [FLAGS.max_seq_length], name="segment_ids")
total_loss, per_example_loss, logits, probabilities = create_model(
    bert_config, is_training, input_ids_p, input_mask_p, segment_ids_p,
    label_ids_p, num_labels, use_one_hot_embeddings)


def predict_online(line):
    """
    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:
    """
    with tf.gfile.GFile('F:\python_work\\bert\selfsim_output\\bert_model.pb', 'rb') as f:  # 加载模型
        graph = tf.GraphDef()
        graph.ParseFromString(f.read())
        sess.graph.as_default()
        tf.import_graph_def(graph, name='')  # 导入计算图
    print(sess.graph.get_operations())
    label = tokenization.convert_to_unicode(line[0])  # this should compatible with format you defined in processor.
    text_a = tokenization.convert_to_unicode(line[1])
    text_b = tokenization.convert_to_unicode(line[2])
    example = InputExample(guid=0, text_a=text_a, text_b=text_b, label=label)
    feature = convert_single_example(0, example, label_list, 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], (FLAGS.max_seq_length))
    label_ids = [feature.label_id]
    sess.run(tf.global_variables_initializer())
    sess.run(tf.local_variables_initializer())
    feed_dict = {input_ids_p: input_ids, input_mask_p: input_mask, segment_ids_p: segment_ids, label_ids_p: label_ids}
    possibility = sess.run([probabilities], feed_dict)
    possibility = possibility[0][0]  # get first label
    label_index = np.argmax(possibility)
    label_predict = index2label[label_index]
    print("label_predict:", label_predict, ";possibility:", possibility)
    return label_predict, possibility


if __name__ == "__main__":
    example = ["1", "统帅是什么", "统帅怎么样"]
    predict_online(example)