import os from queue import Queue from threading import Thread import pandas as pd import tensorflow as tf import collections import args import tokenization import modeling import optimization # os.environ['CUDA_VISIBLE_DEVICES'] = '1' 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() class SimProcessor(DataProcessor): def get_train_examples(self, data_dir): file_path = os.path.join(data_dir, 'train.csv') train_df = pd.read_csv(file_path, encoding='utf-8') train_data = [] for index, train in enumerate(train_df.values): guid = 'train-%d' % index text_a = tokenization.convert_to_unicode(str(train[0])) text_b = tokenization.convert_to_unicode(str(train[1])) label = str(train[2]) train_data.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) return train_data def get_dev_examples(self, data_dir): file_path = os.path.join(data_dir, 'dev.csv') dev_df = pd.read_csv(file_path, encoding='utf-8') dev_data = [] for index, dev in enumerate(dev_df.values): guid = 'test-%d' % index text_a = tokenization.convert_to_unicode(str(dev[0])) text_b = tokenization.convert_to_unicode(str(dev[1])) label = str(dev[2]) dev_data.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) return dev_data def get_test_examples(self, data_dir): file_path = os.path.join(data_dir, 'test.csv') test_df = pd.read_csv(file_path, encoding='utf-8') test_data = [] for index, test in enumerate(test_df.values): guid = 'test-%d' % index text_a = tokenization.convert_to_unicode(str(test[0])) text_b = tokenization.convert_to_unicode(str(test[1])) label = str(test[2]) test_data.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) return test_data def get_sentence_examples(self, questions): 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) yield InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label) def get_labels(self): return ['0', '1'] 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) def set_mode(self, mode): self.mode = mode self.estimator = self.get_estimator() if mode == tf.estimator.ModeKeys.PREDICT: self.input_queue = Queue(maxsize=1) self.output_queue = Queue(maxsize=1) self.predict_thread = Thread(target=self.predict_from_queue, daemon=True) self.predict_thread.start() 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() 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) 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) = BertSim.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) if mode == tf.estimator.ModeKeys.TRAIN: train_op = optimization.create_optimizer( total_loss, learning_rate, num_train_steps, num_warmup_steps, False) output_spec = EstimatorSpec( mode=mode, loss=total_loss, train_op=train_op) elif mode == tf.estimator.ModeKeys.EVAL: def metric_fn(per_example_loss, label_ids, logits): predictions = tf.argmax(logits, axis=-1, output_type=tf.int32) accuracy = tf.metrics.accuracy(label_ids, predictions) auc = tf.metrics.auc(label_ids, predictions) loss = tf.metrics.mean(per_example_loss) return { "eval_accuracy": accuracy, "eval_auc": auc, "eval_loss": loss, } eval_metrics = metric_fn(per_example_loss, label_ids, logits) output_spec = EstimatorSpec( mode=mode, loss=total_loss, eval_metric_ops=eval_metrics) else: 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() train_examples = self.processor.get_train_examples(args.data_dir) num_train_steps = int( len(train_examples) / self.batch_size * args.num_train_epochs) num_warmup_steps = int(num_train_steps * 0.1) 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=num_train_steps, num_warmup_steps=num_warmup_steps, 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_from_queue(self): for i in self.estimator.predict(input_fn=self.queue_predict_input_fn, yield_single_examples=False): self.output_queue.put(i) def queue_predict_input_fn(self): return (tf.data.Dataset.from_generator( self.generate_from_queue, output_types={ 'input_ids': tf.int32, 'input_mask': tf.int32, 'segment_ids': tf.int32, 'label_ids': tf.int32}, output_shapes={ 'input_ids': (None, self.max_seq_length), 'input_mask': (None, self.max_seq_length), 'segment_ids': (None, self.max_seq_length), 'label_ids': (1,)}).prefetch(10)) def convert_examples_to_features(self, examples, label_list, max_seq_length, tokenizer): """Convert a set of `InputExample`s to a list of `InputFeatures`.""" for (ex_index, example) in enumerate(examples): 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) yield feature def generate_from_queue(self): while True: predict_examples = self.processor.get_sentence_examples(self.input_queue.get()) features = list(self.convert_examples_to_features(predict_examples, self.processor.get_labels(), args.max_seq_len, self.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] } 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 file_based_convert_examples_to_features(self, examples, label_list, max_seq_length, tokenizer, output_file): """Convert a set of `InputExample`s to a TFRecord file.""" writer = tf.python_io.TFRecordWriter(output_file) for (ex_index, example) in enumerate(examples): if ex_index % 10000 == 0: tf.logging.info("Writing example %d of %d" % (ex_index, len(examples))) feature = self.convert_single_example(ex_index, example, label_list, max_seq_length, tokenizer) def create_int_feature(values): f = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values))) return f features = collections.OrderedDict() features["input_ids"] = create_int_feature(feature.input_ids) features["input_mask"] = create_int_feature(feature.input_mask) features["segment_ids"] = create_int_feature(feature.segment_ids) features["label_ids"] = create_int_feature([feature.label_id]) tf_example = tf.train.Example(features=tf.train.Features(feature=features)) writer.write(tf_example.SerializeToString()) def file_based_input_fn_builder(self, input_file, seq_length, is_training, drop_remainder): """Creates an `input_fn` closure to be passed to TPUEstimator.""" name_to_features = { "input_ids": tf.FixedLenFeature([seq_length], tf.int64), "input_mask": tf.FixedLenFeature([seq_length], tf.int64), "segment_ids": tf.FixedLenFeature([seq_length], tf.int64), "label_ids": tf.FixedLenFeature([], tf.int64), } def _decode_record(record, name_to_features): """Decodes a record to a TensorFlow example.""" example = tf.parse_single_example(record, name_to_features) # tf.Example only supports tf.int64, but the TPU only supports tf.int32. # So cast all int64 to int32. for name in list(example.keys()): t = example[name] if t.dtype == tf.int64: t = tf.to_int32(t) example[name] = t return example def input_fn(params): """The actual input function.""" batch_size = params["batch_size"] # For training, we want a lot of parallel reading and shuffling. # For eval, we want no shuffling and parallel reading doesn't matter. d = tf.data.TFRecordDataset(input_file) if is_training: d = d.repeat() d = d.shuffle(buffer_size=100) d = d.apply( tf.contrib.data.map_and_batch( lambda record: _decode_record(record, name_to_features), batch_size=batch_size, drop_remainder=drop_remainder)) return d return input_fn def train(self): if self.mode is None: raise ValueError("Please set the 'mode' parameter") bert_config = modeling.BertConfig.from_json_file(args.config_name) if args.max_seq_len > bert_config.max_position_embeddings: raise ValueError( "Cannot use sequence length %d because the BERT model " "was only trained up to sequence length %d" % (args.max_seq_len, bert_config.max_position_embeddings)) tf.gfile.MakeDirs(args.output_dir) label_list = self.processor.get_labels() train_examples = self.processor.get_train_examples(args.data_dir) num_train_steps = int(len(train_examples) / args.batch_size * args.num_train_epochs) estimator = self.get_estimator() train_file = os.path.join(args.output_dir, "train.tf_record") self.file_based_convert_examples_to_features(train_examples, label_list, args.max_seq_len, self.tokenizer, train_file) tf.logging.info("***** Running training *****") tf.logging.info(" Num examples = %d", len(train_examples)) tf.logging.info(" Batch size = %d", args.batch_size) tf.logging.info(" Num steps = %d", num_train_steps) train_input_fn = self.file_based_input_fn_builder(input_file=train_file, seq_length=args.max_seq_len, is_training=True, drop_remainder=True) # early_stopping = tf.contrib.estimator.stop_if_no_decrease_hook( # estimator, # metric_name='loss', # max_steps_without_decrease=10, # min_steps=num_train_steps) # estimator.train(input_fn=train_input_fn, hooks=[early_stopping]) estimator.train(input_fn=train_input_fn, max_steps=num_train_steps) def eval(self): if self.mode is None: raise ValueError("Please set the 'mode' parameter") eval_examples = self.processor.get_dev_examples(args.data_dir) eval_file = os.path.join(args.output_dir, "eval.tf_record") label_list = self.processor.get_labels() self.file_based_convert_examples_to_features( eval_examples, label_list, args.max_seq_len, self.tokenizer, eval_file) tf.logging.info("***** Running evaluation *****") tf.logging.info(" Num examples = %d", len(eval_examples)) tf.logging.info(" Batch size = %d", self.batch_size) eval_input_fn = self.file_based_input_fn_builder( input_file=eval_file, seq_length=args.max_seq_len, is_training=False, drop_remainder=False) estimator = self.get_estimator() result = estimator.evaluate(input_fn=eval_input_fn, steps=None) output_eval_file = os.path.join(args.output_dir, "eval_results.txt") with tf.gfile.GFile(output_eval_file, "w") as writer: tf.logging.info("***** Eval results *****") for key in sorted(result.keys()): tf.logging.info(" %s = %s", key, str(result[key])) writer.write("%s = %s\n" % (key, str(result[key]))) def predict(self, sentence1, sentence2): if self.mode is None: raise ValueError("Please set the 'mode' parameter") self.input_queue.put([(sentence1, sentence2)]) prediction = self.output_queue.get() return prediction if __name__ == '__main__': sim = BertSim() sim.set_mode(tf.estimator.ModeKeys.TRAIN) sim.train() sim.set_mode(tf.estimator.ModeKeys.EVAL) sim.eval() # sim.set_mode(tf.estimator.ModeKeys.PREDICT) # while True: # sentence1 = input('sentence1: ') # sentence2 = input('sentence2: ') # predict = sim.predict(sentence1, sentence2) # print(f'similarity：{predict[0][1]}')