/
run_sequence_labeling_and_text_classification.py
979 lines (812 loc) · 45 KB
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run_sequence_labeling_and_text_classification.py
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#! usr/bin/env python3
# -*- coding:utf-8 -*-
"""
@Author:yuanxiao
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import csv
import os
from bert import modeling
from bert import optimization
from bert import tokenization
import tensorflow as tf
import pickle
import shutil
import calculate_model_score as tf_metrics
flags = tf.flags
FLAGS = flags.FLAGS
## Required parameters
flags.DEFINE_string(
"data_dir", None,
"The input data dir. Should contain the .tsv files (or other data files) "
"for the task.")
flags.DEFINE_string(
"bert_config_file", None,
"The config json file corresponding to the pre-trained BERT model. "
"This specifies the model architecture.")
flags.DEFINE_string("task_name", None, "The name of the task to train.")
flags.DEFINE_string("vocab_file", None,
"The vocabulary file that the BERT model was trained on.")
flags.DEFINE_string(
"output_dir", None,
"The output directory where the model checkpoints will be written.")
## Other parameters
flags.DEFINE_bool(
"calculate_model_score", True, "calculate_model_score_on_test_data")
flags.DEFINE_string(
"init_checkpoint", None,
"Initial checkpoint (usually from a pre-trained BERT model).")
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_integer(
"max_seq_length", 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_train", False, "Whether to run training.")
flags.DEFINE_bool("do_eval", False, "Whether to run eval on the dev set.")
flags.DEFINE_bool(
"do_predict", False,
"Whether to run the model in inference mode on the test set.")
flags.DEFINE_integer("train_batch_size", 32, "Total batch size for training.")
flags.DEFINE_integer("eval_batch_size", 8, "Total batch size for eval.")
flags.DEFINE_integer("predict_batch_size", 8, "Total batch size for predict.")
flags.DEFINE_float("learning_rate", 5e-5, "The initial learning rate for Adam.")
flags.DEFINE_float("num_train_epochs", 3.0,
"Total number of training epochs to perform.")
flags.DEFINE_float(
"warmup_proportion", 0.1,
"Proportion of training to perform linear learning rate warmup for. "
"E.g., 0.1 = 10% of training.")
flags.DEFINE_integer("save_checkpoints_steps", 1000,
"How often to save the model checkpoint.")
flags.DEFINE_integer("iterations_per_loop", 1000,
"How many steps to make in each estimator call.")
flags.DEFINE_bool("use_tpu", False, "Whether to use TPU or GPU/CPU.")
tf.flags.DEFINE_string(
"tpu_name", None,
"The Cloud TPU to use for training. This should be either the name "
"used when creating the Cloud TPU, or a grpc://ip.address.of.tpu:8470 "
"url.")
tf.flags.DEFINE_string(
"tpu_zone", None,
"[Optional] GCE zone where the Cloud TPU is located in. If not "
"specified, we will attempt to automatically detect the GCE project from "
"metadata.")
tf.flags.DEFINE_string(
"gcp_project", None,
"[Optional] Project name for the Cloud TPU-enabled project. If not "
"specified, we will attempt to automatically detect the GCE project from "
"metadata.")
tf.flags.DEFINE_string("master", None, "[Optional] TensorFlow master URL.")
flags.DEFINE_integer(
"num_tpu_cores", 8,
"Only used if `use_tpu` is True. Total number of TPU cores to use.")
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 input sequence. For single
sequence tasks, only this sequence must be specified.
text_b: (Optional) string. The untokenized text of the labeling sequence(Slot Filling).
specified for train and dev examples, but not for test examples.
label: (Optional) string. The label(Intent Prediction) 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,
slot_ids,
input_mask,
segment_ids,
label_id,
is_real_example=True):
self.input_ids = input_ids
self.slot_ids = slot_ids
self.input_mask = input_mask
self.segment_ids = segment_ids
self.label_id = label_id
self.is_real_example = is_real_example
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 Atis_Slot_Filling_and_Intent_Detection_Processor(DataProcessor):
def get_examples(self, data_dir):
path_seq_in = os.path.join(data_dir, "seq.in")
path_seq_out = os.path.join(data_dir, "seq.out")
path_label = os.path.join(data_dir, "label")
seq_in_list, seq_out_list, label_list = [], [], []
with open(path_seq_in) as seq_in_f:
with open(path_seq_out) as seq_out_f:
with open(path_label) as label_f:
for seqin, seqout, label in zip(seq_in_f.readlines(), seq_out_f.readlines(), label_f.readlines()):
seqin_words = [word for word in seqin.split() if len(word) > 0]
seqout_words = [word for word in seqout.split() if len(word) > 0]
label_list.append(label.replace("\n", ""))
assert len(seqin_words) == len(seqout_words)
seq_in_list.append(" ".join(seqin_words))
seq_out_list.append(" ".join(seqout_words))
lines = list(zip(seq_in_list, seq_out_list, label_list))
return lines
def get_train_examples(self, data_dir):
return self._create_example(self.get_examples(os.path.join(data_dir, "train")), "train")
def get_dev_examples(self, data_dir):
return self._create_example(self.get_examples(os.path.join(data_dir, "valid")), "valid")
def get_test_examples(self, data_dir):
return self._create_example(self.get_examples(os.path.join(data_dir, "test")), "test")
def get_slot_labels_from_files(self, data_dir):
label_set = set()
for f_type in ["train", "valid", "test"]:
seq_out_dir = os.path.join(os.path.join(data_dir, f_type), "seq.out")
with open(seq_out_dir) as data_f:
seq_sentence_list = [seq.split() for seq in data_f.readlines()]
seq_word_list = [word for seq in seq_sentence_list for word in seq]
label_set = label_set | set(seq_word_list)
label_list = list(label_set)
label_list.sort()
return ["[Padding]", "[##WordPiece]", "[CLS]", "[SEP]"] + label_list
def get_slot_labels(self):
return ['[Padding]', '[##WordPiece]', '[CLS]', '[SEP]', 'B-aircraft_code', 'B-airline_code', 'B-airline_name',
'B-airport_code', 'B-airport_name', 'B-arrive_date.date_relative', 'B-arrive_date.day_name',
'B-arrive_date.day_number', 'B-arrive_date.month_name', 'B-arrive_date.today_relative',
'B-arrive_time.end_time', 'B-arrive_time.period_mod', 'B-arrive_time.period_of_day',
'B-arrive_time.start_time', 'B-arrive_time.time', 'B-arrive_time.time_relative', 'B-booking_class',
'B-city_name', 'B-class_type', 'B-compartment', 'B-connect', 'B-cost_relative', 'B-day_name',
'B-day_number', 'B-days_code', 'B-depart_date.date_relative', 'B-depart_date.day_name',
'B-depart_date.day_number', 'B-depart_date.month_name', 'B-depart_date.today_relative',
'B-depart_date.year', 'B-depart_time.end_time', 'B-depart_time.period_mod',
'B-depart_time.period_of_day', 'B-depart_time.start_time', 'B-depart_time.time',
'B-depart_time.time_relative', 'B-economy', 'B-fare_amount', 'B-fare_basis_code', 'B-flight',
'B-flight_days', 'B-flight_mod', 'B-flight_number', 'B-flight_stop', 'B-flight_time',
'B-fromloc.airport_code', 'B-fromloc.airport_name', 'B-fromloc.city_name', 'B-fromloc.state_code',
'B-fromloc.state_name', 'B-meal', 'B-meal_code', 'B-meal_description', 'B-mod', 'B-month_name', 'B-or',
'B-period_of_day', 'B-restriction_code', 'B-return_date.date_relative', 'B-return_date.day_name',
'B-return_date.day_number', 'B-return_date.month_name', 'B-return_date.today_relative',
'B-return_time.period_mod', 'B-return_time.period_of_day', 'B-round_trip', 'B-state_code',
'B-state_name', 'B-stoploc.airport_code', 'B-stoploc.airport_name', 'B-stoploc.city_name',
'B-stoploc.state_code', 'B-time', 'B-time_relative', 'B-today_relative', 'B-toloc.airport_code',
'B-toloc.airport_name', 'B-toloc.city_name', 'B-toloc.country_name', 'B-toloc.state_code',
'B-toloc.state_name', 'B-transport_type', 'I-airline_name', 'I-airport_name',
'I-arrive_date.day_number', 'I-arrive_time.end_time', 'I-arrive_time.period_of_day',
'I-arrive_time.start_time', 'I-arrive_time.time', 'I-arrive_time.time_relative', 'I-city_name',
'I-class_type', 'I-cost_relative', 'I-depart_date.day_number', 'I-depart_date.today_relative',
'I-depart_time.end_time', 'I-depart_time.period_of_day', 'I-depart_time.start_time',
'I-depart_time.time', 'I-depart_time.time_relative', 'I-economy', 'I-fare_amount', 'I-fare_basis_code',
'I-flight_mod', 'I-flight_number', 'I-flight_stop', 'I-flight_time', 'I-fromloc.airport_name',
'I-fromloc.city_name', 'I-fromloc.state_name', 'I-meal_code', 'I-meal_description',
'I-restriction_code', 'I-return_date.date_relative', 'I-return_date.day_number',
'I-return_date.today_relative', 'I-round_trip', 'I-state_name', 'I-stoploc.city_name', 'I-time',
'I-today_relative', 'I-toloc.airport_name', 'I-toloc.city_name', 'I-toloc.state_name',
'I-transport_type', 'O']
def get_intent_labels(self):
return ['atis_abbreviation', 'atis_aircraft', 'atis_aircraft#atis_flight#atis_flight_no',
'atis_airfare', 'atis_airfare#atis_flight', 'atis_airfare#atis_flight_time',
'atis_airline', 'atis_airline#atis_flight_no', 'atis_airport', 'atis_capacity',
'atis_cheapest', 'atis_city', 'atis_day_name', 'atis_distance', 'atis_flight',
'atis_flight#atis_airfare', 'atis_flight#atis_airline', 'atis_flight_no',
'atis_flight_no#atis_airline', 'atis_flight_time', 'atis_ground_fare',
'atis_ground_service', 'atis_ground_service#atis_ground_fare', 'atis_meal',
'atis_quantity', 'atis_restriction']
def _create_example(self, lines, set_type):
examples = []
for (i, line) in enumerate(lines):
guid = "%s-%s" % (set_type, i)
text_a = tokenization.convert_to_unicode(line[0])
text_b = tokenization.convert_to_unicode(line[1])
label = tokenization.convert_to_unicode(line[2])
examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
return examples
class Snips_Slot_Filling_and_Intent_Detection_Processor(DataProcessor):
def get_examples(self, data_dir):
path_seq_in = os.path.join(data_dir, "seq.in")
path_seq_out = os.path.join(data_dir, "seq.out")
path_label = os.path.join(data_dir, "label")
seq_in_list, seq_out_list, label_list = [], [], []
with open(path_seq_in) as seq_in_f:
with open(path_seq_out) as seq_out_f:
with open(path_label) as label_f:
for seqin, seqout, label in zip(seq_in_f.readlines(), seq_out_f.readlines(), label_f.readlines()):
seqin_words = [word for word in seqin.split() if len(word) > 0]
seqout_words = [word for word in seqout.split() if len(word) > 0]
label_list.append(label.replace("\n", ""))
assert len(seqin_words) == len(seqout_words)
seq_in_list.append(" ".join(seqin_words))
seq_out_list.append(" ".join(seqout_words))
lines = list(zip(seq_in_list, seq_out_list, label_list))
return lines
def get_train_examples(self, data_dir):
return self._create_example(self.get_examples(os.path.join(data_dir, "train")), "train")
def get_dev_examples(self, data_dir):
return self._create_example(self.get_examples(os.path.join(data_dir, "valid")), "valid")
def get_test_examples(self, data_dir):
return self._create_example(self.get_examples(os.path.join(data_dir, "test")), "test")
def get_slot_labels_from_files(self, data_dir):
label_set = set()
for f_type in ["train", "valid", "test"]:
seq_out_dir = os.path.join(os.path.join(data_dir, f_type), "seq.out")
with open(seq_out_dir) as data_f:
seq_sentence_list = [seq.split() for seq in data_f.readlines()]
seq_word_list = [word for seq in seq_sentence_list for word in seq]
label_set = label_set | set(seq_word_list)
label_list = list(label_set)
label_list.sort()
return ["[Padding]", "[##WordPiece]", "[CLS]", "[SEP]"] + label_list
def get_slot_labels(self):
return ['[Padding]', '[##WordPiece]', '[CLS]', '[SEP]', 'B-album', 'B-artist', 'B-best_rating', 'B-city', 'B-condition_description', 'B-condition_temperature', 'B-country', 'B-cuisine', 'B-current_location', 'B-entity_name', 'B-facility', 'B-genre', 'B-geographic_poi', 'B-location_name', 'B-movie_name', 'B-movie_type', 'B-music_item', 'B-object_location_type', 'B-object_name', 'B-object_part_of_series_type', 'B-object_select', 'B-object_type', 'B-party_size_description', 'B-party_size_number', 'B-playlist', 'B-playlist_owner', 'B-poi', 'B-rating_unit', 'B-rating_value', 'B-restaurant_name', 'B-restaurant_type', 'B-served_dish', 'B-service', 'B-sort', 'B-spatial_relation', 'B-state', 'B-timeRange', 'B-track', 'B-year', 'I-album', 'I-artist', 'I-city', 'I-country', 'I-cuisine', 'I-current_location', 'I-entity_name', 'I-facility', 'I-genre', 'I-geographic_poi', 'I-location_name', 'I-movie_name', 'I-movie_type', 'I-music_item', 'I-object_location_type', 'I-object_name', 'I-object_part_of_series_type', 'I-object_select', 'I-object_type', 'I-party_size_description', 'I-playlist', 'I-playlist_owner', 'I-poi', 'I-restaurant_name', 'I-restaurant_type', 'I-served_dish', 'I-service', 'I-sort', 'I-spatial_relation', 'I-state', 'I-timeRange', 'I-track', 'O']
def get_intent_labels(self):
return ['AddToPlaylist', 'BookRestaurant', 'GetWeather', 'PlayMusic',
'RateBook', 'SearchCreativeWork', 'SearchScreeningEvent']
def _create_example(self, lines, set_type):
examples = []
for (i, line) in enumerate(lines):
guid = "%s-%s" % (set_type, i)
text_a = tokenization.convert_to_unicode(line[0])
text_b = tokenization.convert_to_unicode(line[1])
label = 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, slot_label_list, intent_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,
slot_ids=[0] * max_seq_length,
input_mask=[0] * max_seq_length,
segment_ids=[0] * max_seq_length,
label_id=0,
is_real_example=False)
slot_label_map = {}
for (i, label) in enumerate(slot_label_list):
slot_label_map[label] = i
with open(os.path.join(FLAGS.output_dir, "slot_label2id.pkl"), 'wb') as w:
pickle.dump(slot_label_map, w)
intent_label_map = {}
for (i, label) in enumerate(intent_label_list):
intent_label_map[label] = i
with open(os.path.join(FLAGS.output_dir, "intent_label2id.pkl"), 'wb') as w:
pickle.dump(intent_label_map, w)
text_a_list = example.text_a.split(" ")
text_b_list = example.text_b.split(" ")
tokens_a = []
slots_b = []
for i, word in enumerate(text_a_list):
token_a = tokenizer.tokenize(word)
tokens_a.extend(token_a)
slot_i = text_b_list[i]
for m in range(len(token_a)):
if m == 0:
slots_b.append(slot_i)
else:
slots_b.append("[##WordPiece]")
# Account for [CLS] and [SEP] with "- 2"
if len(tokens_a) > max_seq_length - 2:
tokens_a = tokens_a[0:(max_seq_length - 2)]
slots_b = slots_b[0:(max_seq_length - 2)]
tokens = []
slot_ids = []
segment_ids = []
tokens.append("[CLS]")
slot_ids.append(slot_label_map["[CLS]"])
segment_ids.append(0)
for i, token in enumerate(tokens_a):
tokens.append(token)
segment_ids.append(0)
slot_ids.append(slot_label_map[slots_b[i]])
tokens.append("[SEP]")
segment_ids.append(0)
# append("O") or append("[SEP]") not sure!
slot_ids.append(slot_label_map["[SEP]"])
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)
# we don't concerned about it!
slot_ids.append(0)
tokens.append("[Padding]")
assert len(input_ids) == max_seq_length
assert len(input_mask) == max_seq_length
assert len(segment_ids) == max_seq_length
assert len(slot_ids) == max_seq_length
label_id = intent_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("slots_ids: %s" % " ".join([str(x) for x in slot_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,
slot_ids=slot_ids,
input_mask=input_mask,
segment_ids=segment_ids,
label_id=label_id,
is_real_example=True)
return feature
def file_based_convert_examples_to_features(
examples, slot_label_list, intent_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 = convert_single_example(ex_index, example, slot_label_list, intent_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["slot_ids"] = create_int_feature(feature.slot_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])
features["is_real_example"] = create_int_feature([int(feature.is_real_example)])
tf_example = tf.train.Example(features=tf.train.Features(feature=features))
writer.write(tf_example.SerializeToString())
writer.close()
def file_based_input_fn_builder(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),
"slot_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),
"is_real_example": 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 create_model(bert_config, is_training, input_ids, input_mask, segment_ids,
slot_label_ids, intent_label_ids, num_slot_labels, num_intent_labels,
use_one_hot_embeddings):
"""Creates a sequence labeling and 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)
# We "pool" the model by simply taking the hidden state corresponding
# to the first token. float Tensor of shape [batch_size, hidden_size]
intent_output_layer = model.get_pooled_output()
intent_hidden_size = intent_output_layer.shape[-1].value
intent_output_weights = tf.get_variable(
"intent_output_weights", [num_intent_labels, intent_hidden_size],
initializer=tf.truncated_normal_initializer(stddev=0.02))
intent_output_bias = tf.get_variable(
"intent_output_bias", [num_intent_labels], initializer=tf.zeros_initializer())
with tf.variable_scope("intent_loss"):
if is_training:
# I.e., 0.1 dropout
intent_output_layer = tf.nn.dropout(intent_output_layer, keep_prob=0.9)
intent_logits = tf.matmul(intent_output_layer, intent_output_weights, transpose_b=True)
intent_logits = tf.nn.bias_add(intent_logits, intent_output_bias)
intent_probabilities = tf.nn.softmax(intent_logits, axis=-1)
intent_log_probs = tf.nn.log_softmax(intent_logits, axis=-1)
intent_predictions = tf.argmax(intent_logits, axis=-1)
intent_one_hot_labels = tf.one_hot(intent_label_ids, depth=num_intent_labels, dtype=tf.float32)
intent_per_example_loss = -tf.reduce_sum(intent_one_hot_labels * intent_log_probs, axis=-1)
intent_loss = tf.reduce_mean(intent_per_example_loss)
#return (intent_loss, intent_per_example_loss, intent_logits, intent_probabilities, intent_predictions)
# """Gets final hidden layer of encoder.
#
# Returns:
# float Tensor of shape [batch_size, seq_length, hidden_size] corresponding
# to the final hidden of the transformer encoder.
# """
slot_output_layer = model.get_sequence_output()
slot_hidden_size = slot_output_layer.shape[-1].value
slot_output_weight = tf.get_variable(
"slot_output_weights", [num_slot_labels, slot_hidden_size],
initializer=tf.truncated_normal_initializer(stddev=0.02)
)
slot_output_bias = tf.get_variable(
"slot_output_bias", [num_slot_labels], initializer=tf.zeros_initializer()
)
with tf.variable_scope("slot_loss"):
if is_training:
slot_output_layer = tf.nn.dropout(slot_output_layer, keep_prob=0.9)
slot_output_layer = tf.reshape(slot_output_layer, [-1, slot_hidden_size])
slot_logits = tf.matmul(slot_output_layer, slot_output_weight, transpose_b=True)
slot_logits = tf.nn.bias_add(slot_logits, slot_output_bias)
slot_logits = tf.reshape(slot_logits, [-1, FLAGS.max_seq_length, num_slot_labels])
slot_log_probs = tf.nn.log_softmax(slot_logits, axis=-1)
slot_one_hot_labels = tf.one_hot(slot_label_ids, depth=num_slot_labels, dtype=tf.float32)
slot_per_example_loss = -tf.reduce_sum(slot_one_hot_labels * slot_log_probs, axis=-1)
slot_loss = tf.reduce_sum(slot_per_example_loss)
slot_probabilities = tf.nn.softmax(slot_logits, axis=-1)
slot_predictions = tf.argmax(slot_probabilities, axis=-1)
#return (slot_loss, slot_per_example_loss, slot_logits, slot_predict)
loss = intent_loss + slot_loss
return (loss,
intent_loss, intent_per_example_loss, intent_logits, intent_predictions,
slot_loss, slot_per_example_loss, slot_logits, slot_predictions)
def model_fn_builder(bert_config, num_slot_labels, num_intent_labels, init_checkpoint,
learning_rate, num_train_steps, num_warmup_steps, use_tpu,
use_one_hot_embeddings):
"""Returns `model_fn` closure for TPUEstimator."""
def model_fn(features, labels, mode, params): # pylint: disable=unused-argument
"""The `model_fn` for TPUEstimator."""
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"]
slot_label_ids = features["slot_ids"]
input_mask = features["input_mask"]
segment_ids = features["segment_ids"]
intent_label_ids = features["label_ids"]
is_real_example = None
if "is_real_example" in features:
is_real_example = tf.cast(features["is_real_example"], dtype=tf.float32)
else:
is_real_example = tf.ones(tf.shape(intent_label_ids), dtype=tf.float32)
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
(total_loss,
intent_loss, intent_per_example_loss, intent_logits, intent_predictions,
slot_loss, slot_per_example_loss, slot_logits, slot_predictions) = create_model(
bert_config, is_training, input_ids, input_mask, segment_ids,
slot_label_ids, intent_label_ids, num_slot_labels, num_intent_labels, use_one_hot_embeddings)
tvars = tf.trainable_variables()
initialized_variable_names = {}
scaffold_fn = None
if init_checkpoint:
(assignment_map, initialized_variable_names
) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)
if use_tpu:
def tpu_scaffold():
tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
return tf.train.Scaffold()
scaffold_fn = tpu_scaffold
else:
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 = None
if mode == tf.estimator.ModeKeys.TRAIN:
train_op = optimization.create_optimizer(
total_loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu)
output_spec = tf.contrib.tpu.TPUEstimatorSpec(
mode=mode,
loss=total_loss,
train_op=train_op,
scaffold_fn=scaffold_fn)
elif mode == tf.estimator.ModeKeys.EVAL:
def metric_fn(intent_per_example_loss, intent_label_ids, intent_logits,
slot_per_example_loss, slot_label_ids, slot_logits, is_real_example):
intent_predictions = tf.argmax(intent_logits, axis=-1, output_type=tf.int32)
intent_accuracy = tf.metrics.accuracy(
labels=intent_label_ids, predictions=intent_predictions, weights=is_real_example)
intent_loss = tf.metrics.mean(values=intent_per_example_loss, weights=is_real_example)
slot_predictions = tf.argmax(slot_logits, axis=-1, output_type=tf.int32)
slot_pos_indices_list = list(range(num_slot_labels))[4:] # ["[Padding]","[##WordPiece]", "[CLS]", "[SEP]"] + seq_out_set
pos_indices_list = slot_pos_indices_list[:-1] # do not care "O"
slot_precision_macro = tf_metrics.precision(slot_label_ids, slot_predictions, num_slot_labels,
slot_pos_indices_list, average="macro")
slot_recall_macro = tf_metrics.recall(slot_label_ids, slot_predictions, num_slot_labels,
slot_pos_indices_list, average="macro")
slot_f_macro = tf_metrics.f1(slot_label_ids, slot_predictions, num_slot_labels, slot_pos_indices_list,
average="macro")
slot_precision_micro = tf_metrics.precision(slot_label_ids, slot_predictions, num_slot_labels,
slot_pos_indices_list, average="micro")
slot_recall_micro = tf_metrics.recall(slot_label_ids, slot_predictions, num_slot_labels,
slot_pos_indices_list, average="micro")
slot_f_micro = tf_metrics.f1(slot_label_ids, slot_predictions, num_slot_labels, slot_pos_indices_list,
average="micro")
slot_loss = tf.metrics.mean(values=slot_per_example_loss, weights=is_real_example)
return {
"eval_intent_accuracy": intent_accuracy,
"eval_intent_loss": intent_loss,
"eval_slot_precision(macro)": slot_precision_macro,
"eval_slot_recall(macro)": slot_recall_macro,
"eval_slot_f(macro)": slot_f_macro,
"eval_slot_precision(micro)": slot_precision_micro,
"eval_slot_recall(micro)": slot_recall_micro,
"eval_slot_f(micro)": slot_f_micro,
"eval_slot_loss": slot_loss,
}
eval_metrics = (metric_fn,
[intent_per_example_loss, intent_label_ids, intent_logits,
slot_per_example_loss, slot_label_ids, slot_logits, is_real_example])
output_spec = tf.contrib.tpu.TPUEstimatorSpec(
mode=mode,
loss=total_loss,
eval_metrics=eval_metrics,
scaffold_fn=scaffold_fn)
else:
output_spec = tf.contrib.tpu.TPUEstimatorSpec(
mode=mode,
predictions={"intent_predictions": intent_predictions,
"slot_predictions": slot_predictions},
scaffold_fn=scaffold_fn)
return output_spec
return model_fn
def main(_):
# ----------------for code test------------------
if FLAGS.do_train:
if os.path.exists(FLAGS.output_dir):
try:
os.removedirs(FLAGS.output_dir)
os.makedirs(FLAGS.output_dir)
except:
tf.logging.info("***** Running evaluation *****")
tf.logging.warning(FLAGS.output_dir + " is not empty, here use shutil.rmtree(FLAGS.output_dir)!")
shutil.rmtree(FLAGS.output_dir)
os.makedirs(FLAGS.output_dir)
else:
os.makedirs(FLAGS.output_dir)
# ----------------for code test------------------
tf.logging.set_verbosity(tf.logging.INFO)
processors = {
"atis": Atis_Slot_Filling_and_Intent_Detection_Processor,
"snips": Snips_Slot_Filling_and_Intent_Detection_Processor,
}
tokenization.validate_case_matches_checkpoint(FLAGS.do_lower_case,
FLAGS.init_checkpoint)
if not FLAGS.do_train and not FLAGS.do_eval and not FLAGS.do_predict:
raise ValueError(
"At least one of `do_train`, `do_eval` or `do_predict' must be True.")
bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)
if FLAGS.max_seq_length > bert_config.max_position_embeddings:
raise ValueError(
"Cannot use sequence length %d because the BERT model "
"was only trained up to sequence length %d" %
(FLAGS.max_seq_length, bert_config.max_position_embeddings))
tf.gfile.MakeDirs(FLAGS.output_dir)
task_name = FLAGS.task_name.lower()
if task_name not in processors:
raise ValueError("Task not found: %s" % (task_name))
processor = processors[task_name]()
intent_label_list = processor.get_intent_labels()
slot_label_list = processor.get_slot_labels()
intent_id2label = {}
for (i, label) in enumerate(intent_label_list):
intent_id2label[i] = label
slot_id2label = {}
for (i, label) in enumerate(slot_label_list):
slot_id2label[i] = label
tokenizer = tokenization.FullTokenizer(
vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case)
tpu_cluster_resolver = None
if FLAGS.use_tpu and FLAGS.tpu_name:
tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver(
FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)
is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2
run_config = tf.contrib.tpu.RunConfig(
cluster=tpu_cluster_resolver,
master=FLAGS.master,
model_dir=FLAGS.output_dir,
save_checkpoints_steps=FLAGS.save_checkpoints_steps,
tpu_config=tf.contrib.tpu.TPUConfig(
iterations_per_loop=FLAGS.iterations_per_loop,
num_shards=FLAGS.num_tpu_cores,
per_host_input_for_training=is_per_host))
train_examples = None
num_train_steps = None
num_warmup_steps = None
if FLAGS.do_train:
train_examples = processor.get_train_examples(FLAGS.data_dir)
num_train_steps = int(
len(train_examples) / FLAGS.train_batch_size * FLAGS.num_train_epochs)
num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion)
model_fn = model_fn_builder(
bert_config=bert_config,
num_slot_labels=len(slot_label_list),
num_intent_labels=len(intent_label_list),
init_checkpoint=FLAGS.init_checkpoint,
learning_rate=FLAGS.learning_rate,
num_train_steps=num_train_steps,
num_warmup_steps=num_warmup_steps,
use_tpu=FLAGS.use_tpu,
use_one_hot_embeddings=FLAGS.use_tpu)
# If TPU is not available, this will fall back to normal Estimator on CPU
# or GPU.
estimator = tf.contrib.tpu.TPUEstimator(
use_tpu=FLAGS.use_tpu,
model_fn=model_fn,
config=run_config,
train_batch_size=FLAGS.train_batch_size,
eval_batch_size=FLAGS.eval_batch_size,
predict_batch_size=FLAGS.predict_batch_size)
if FLAGS.do_train:
train_file = os.path.join(FLAGS.output_dir, "train.tf_record")
file_based_convert_examples_to_features(
train_examples, slot_label_list, intent_label_list, FLAGS.max_seq_length, tokenizer, train_file)
tf.logging.info("***** Running training *****")
tf.logging.info(" Num examples = %d", len(train_examples))
tf.logging.info(" Batch size = %d", FLAGS.train_batch_size)
tf.logging.info(" Num steps = %d", num_train_steps)
train_input_fn = file_based_input_fn_builder(
input_file=train_file,
seq_length=FLAGS.max_seq_length,
is_training=True,
drop_remainder=True)
estimator.train(input_fn=train_input_fn, max_steps=num_train_steps)
if FLAGS.do_eval:
eval_examples = processor.get_dev_examples(FLAGS.data_dir)
num_actual_eval_examples = len(eval_examples)
if FLAGS.use_tpu:
# TPU requires a fixed batch size for all batches, therefore the number
# of examples must be a multiple of the batch size, or else examples
# will get dropped. So we pad with fake examples which are ignored
# later on. These do NOT count towards the metric (all tf.metrics
# support a per-instance weight, and these get a weight of 0.0).
while len(eval_examples) % FLAGS.eval_batch_size != 0:
eval_examples.append(PaddingInputExample())
eval_file = os.path.join(FLAGS.output_dir, "eval.tf_record")
file_based_convert_examples_to_features(
eval_examples, slot_label_list, intent_label_list, FLAGS.max_seq_length, tokenizer, eval_file)
tf.logging.info("***** Running evaluation *****")
tf.logging.info(" Num examples = %d (%d actual, %d padding)",
len(eval_examples), num_actual_eval_examples,
len(eval_examples) - num_actual_eval_examples)
tf.logging.info(" Batch size = %d", FLAGS.eval_batch_size)
# This tells the estimator to run through the entire set.
eval_steps = None
# However, if running eval on the TPU, you will need to specify the
# number of steps.
if FLAGS.use_tpu:
assert len(eval_examples) % FLAGS.eval_batch_size == 0
eval_steps = int(len(eval_examples) // FLAGS.eval_batch_size)
eval_drop_remainder = True if FLAGS.use_tpu else False
eval_input_fn = file_based_input_fn_builder(
input_file=eval_file,
seq_length=FLAGS.max_seq_length,
is_training=False,
drop_remainder=eval_drop_remainder)
result = estimator.evaluate(input_fn=eval_input_fn, steps=eval_steps)
output_eval_file = os.path.join(FLAGS.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])))
if FLAGS.do_predict:
predict_examples = processor.get_test_examples(FLAGS.data_dir)
num_actual_predict_examples = len(predict_examples)
if FLAGS.use_tpu:
# TPU requires a fixed batch size for all batches, therefore the number
# of examples must be a multiple of the batch size, or else examples
# will get dropped. So we pad with fake examples which are ignored
# later on.
while len(predict_examples) % FLAGS.predict_batch_size != 0:
predict_examples.append(PaddingInputExample())
predict_file = os.path.join(FLAGS.output_dir, "predict.tf_record")
file_based_convert_examples_to_features(predict_examples, slot_label_list, intent_label_list,
FLAGS.max_seq_length, tokenizer,
predict_file)
tf.logging.info("***** Running prediction*****")
tf.logging.info(" Num examples = %d (%d actual, %d padding)",
len(predict_examples), num_actual_predict_examples,
len(predict_examples) - num_actual_predict_examples)
tf.logging.info(" Batch size = %d", FLAGS.predict_batch_size)
predict_drop_remainder = True if FLAGS.use_tpu else False
predict_input_fn = file_based_input_fn_builder(
input_file=predict_file,
seq_length=FLAGS.max_seq_length,
is_training=False,
drop_remainder=predict_drop_remainder)
result = estimator.predict(input_fn=predict_input_fn)
#result_list = list(result)
#with open(os.path.join(FLAGS.output_dir, "all_test_results.pkl"), "wb") as result_f:
# pickle.dump(result_list, result_f)
intent_output_predict_file = os.path.join(FLAGS.output_dir, "intent_prediction_test_results.txt")
slot_output_predict_file = os.path.join(FLAGS.output_dir, "slot_filling_test_results.txt")
with tf.gfile.GFile(intent_output_predict_file, "w") as intent_writer:
with tf.gfile.GFile(slot_output_predict_file, "w") as slot_writer:
num_written_lines = 0
tf.logging.info("***** Intent Predict and Slot Filling results *****")
for (i, prediction) in enumerate(result):
intent_prediction = prediction["intent_predictions"]
slot_predictions = prediction["slot_predictions"]
if i >= num_actual_predict_examples:
break
intent_output_line = str(intent_id2label[intent_prediction]) + "\n"
intent_writer.write(intent_output_line)
slot_output_line = " ".join(
slot_id2label[id] for id in slot_predictions if id != 0) + "\n" # 0--->"[Padding]"
slot_writer.write(slot_output_line)
num_written_lines += 1
assert num_written_lines == num_actual_predict_examples
if FLAGS.calculate_model_score:
path_to_label_file = os.path.join(FLAGS.data_dir, "test")
path_to_predict_label_file = FLAGS.output_dir
log_out_file = path_to_predict_label_file
if FLAGS.task_name.lower() == "snips":
intent_slot_reports = tf_metrics.Snips_Slot_Filling_and_Intent_Detection_Calculate(
path_to_label_file, path_to_predict_label_file, log_out_file)
elif FLAGS.task_name.lower() == "atis":
intent_slot_reports = tf_metrics.Atis_Slot_Filling_and_Intent_Detection_Calculate(
path_to_label_file, path_to_predict_label_file, log_out_file)
else:
raise ValueError("Not this calculate_model_score")
intent_slot_reports.show_intent_prediction_report(store_report=True)
intent_slot_reports.show_slot_filling_report(store_report=True)
if __name__ == "__main__":
flags.mark_flag_as_required("data_dir")
flags.mark_flag_as_required("task_name")
flags.mark_flag_as_required("vocab_file")
flags.mark_flag_as_required("bert_config_file")
flags.mark_flag_as_required("output_dir")
tf.app.run()