similarity.py

"""
进行文本相似度预测的示例。可以直接运行进行预测。
参考了项目：https://github.com/chdd/bert-utils

"""


import tensorflow as tf
import args
import tokenization
import modeling
from run_classifier import InputFeatures, InputExample, DataProcessor, create_model, convert_examples_to_features


# os.environ['CUDA_VISIBLE_DEVICES'] = '1'


class SimProcessor(DataProcessor):
    def get_sentence_examples(self, questions):
        examples = []
        for index, data in enumerate(questions):
            guid = 'test-%d' % index
            text_a = tokenization.convert_to_unicode(str(data[0]))
            text_b = tokenization.convert_to_unicode(str(data[1]))
            label = str(0)
            examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
        return examples

    def get_labels(self):
        return ['0', '1']


"""
模型类，负责载入checkpoint初始化模型
"""
class BertSim:
    def __init__(self, batch_size=args.batch_size):
        self.mode = None
        self.max_seq_length = args.max_seq_len
        self.tokenizer = tokenization.FullTokenizer(vocab_file=args.vocab_file, do_lower_case=True)
        self.batch_size = batch_size
        self.estimator = None
        self.processor = SimProcessor()
        tf.logging.set_verbosity(tf.logging.INFO)



    #载入estimator,构造模型
    def start_model(self):
        self.estimator = self.get_estimator()


    def model_fn_builder(self, bert_config, num_labels, init_checkpoint, learning_rate,
                         num_train_steps, num_warmup_steps,
                         use_one_hot_embeddings):
        """Returns `model_fn` closurimport_tfe for TPUEstimator."""

        def model_fn(features, labels, mode, params):  # pylint: disable=unused-argument
            from tensorflow.python.estimator.model_fn import EstimatorSpec

            tf.logging.info("*** Features ***")
            for name in sorted(features.keys()):
                tf.logging.info("  name = %s, shape = %s" % (name, features[name].shape))

            input_ids = features["input_ids"]
            input_mask = features["input_mask"]
            segment_ids = features["segment_ids"]
            label_ids = features["label_ids"]

            is_training = (mode == tf.estimator.ModeKeys.TRAIN)

            (total_loss, per_example_loss, logits, probabilities) = create_model(
                bert_config, is_training, input_ids, input_mask, segment_ids, label_ids,
                num_labels, use_one_hot_embeddings)

            tvars = tf.trainable_variables()
            initialized_variable_names = {}

            if init_checkpoint:
                (assignment_map, initialized_variable_names) \
                    = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)
                tf.train.init_from_checkpoint(init_checkpoint, assignment_map)

            tf.logging.info("**** Trainable Variables ****")
            for var in tvars:
                init_string = ""
                if var.name in initialized_variable_names:
                    init_string = ", *INIT_FROM_CKPT*"
                tf.logging.info("  name = %s, shape = %s%s", var.name, var.shape,
                                init_string)
            output_spec = EstimatorSpec(mode=mode, predictions=probabilities)

            return output_spec

        return model_fn

    def get_estimator(self):

        from tensorflow.python.estimator.estimator import Estimator
        from tensorflow.python.estimator.run_config import RunConfig

        bert_config = modeling.BertConfig.from_json_file(args.config_name)
        label_list = self.processor.get_labels()
        if self.mode == tf.estimator.ModeKeys.TRAIN:
            init_checkpoint = args.ckpt_name
        else:
            init_checkpoint = args.output_dir

        model_fn = self.model_fn_builder(
            bert_config=bert_config,
            num_labels=len(label_list),
            init_checkpoint=init_checkpoint,
            learning_rate=args.learning_rate,
            num_train_steps=None,
            num_warmup_steps=None,
            use_one_hot_embeddings=False)

        config = tf.ConfigProto()
        config.gpu_options.allow_growth = True
        config.gpu_options.per_process_gpu_memory_fraction = args.gpu_memory_fraction
        config.log_device_placement = False

        return Estimator(model_fn=model_fn, config=RunConfig(session_config=config), model_dir=args.output_dir,
                         params={'batch_size': self.batch_size})

    def predict_sentences(self,sentences):
        results= self.estimator.predict(input_fn=input_fn_builder(self,sentences), yield_single_examples=False)
        #打印预测结果
        for i in results:
            print(i)

    def _truncate_seq_pair(self, 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(self, 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"
            self._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 input_fn_builder(bertSim,sentences):
    def predict_input_fn():
        return (tf.data.Dataset.from_generator(
            generate_from_input,
            output_types={
                'input_ids': tf.int32,
                'input_mask': tf.int32,
                'segment_ids': tf.int32,
                'label_ids': tf.int32},
            output_shapes={
                'input_ids': (None, bertSim.max_seq_length),
                'input_mask': (None, bertSim.max_seq_length),
                'segment_ids': (None, bertSim.max_seq_length),
                'label_ids': (1,)}).prefetch(10))

    def generate_from_input():
        processor = bertSim.processor
        predict_examples = processor.get_sentence_examples(sentences)
        features = convert_examples_to_features(predict_examples, processor.get_labels(), args.max_seq_len,
                                                bertSim.tokenizer)
        yield {
            'input_ids': [f.input_ids for f in features],
            'input_mask': [f.input_mask for f in features],
            'segment_ids': [f.segment_ids for f in features],
            'label_ids': [f.label_id for f in features]
        }

    return predict_input_fn


if __name__ == '__main__':
    sim = BertSim()
    sim.start_model()
    sim.predict_sentences([("我喜欢妈妈做的汤", "妈妈做的汤我很喜欢喝")])