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# 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.""" | |
from __future__ import absolute_import | |
from __future__ import division | |
from __future__ import print_function | |
import csv | |
import os | |
import modeling | |
import optimization | |
import tokenization | |
import tensorflow as tf | |
import numpy as np | |
# NLPR original | |
from sklearn.metrics import roc_auc_score | |
def get_confusion_matrix(predicted, answer): | |
assert(len(predicted) == len(answer)) | |
d = {} | |
#d.setdefault(0) | |
for i in range(3): | |
for j in range(3): | |
d[(i,j)] = 0 | |
for _ in range(len(answer)): | |
key_pair = (np.argmax(predicted[_]), np.argmax(answer[_])) | |
x = d.get(key_pair, 0 ) + 1 | |
d[key_pair] = x | |
return d | |
def get_roc(mat): | |
Label0 = mat[(0,0)] + mat[(1,0)] + mat[(2,0)] | |
Label1 = mat[(0,1)] + mat[(1,1)] + mat[(2,1)] | |
Label2 = mat[(0,2)] + mat[(1,2)] + mat[(2,2)] | |
Guess0 = mat[(0,0)] + mat[(0,1)] + mat[(0,2)] | |
Guess1 = mat[(1,0)] + mat[(1,1)] + mat[(1,2)] | |
Guess2 = mat[(2,0)] + mat[(2,1)] + mat[(2,2)] | |
Prec0 = mat[(0,0)] / Guess0 | |
Recall0 = mat[0,0] / Label0 | |
Prec1 = mat[(1,1)] / Guess1 | |
Recall1 = mat[1,1] / Label1 | |
Prec2 = mat[(2,2)] / Guess2 | |
Recall2 = mat[2,2] / Label2 | |
Prec_macro = ( Prec0 + Prec1 + Prec2 ) / 3.0 | |
Prec_micro = (mat[(0,0)] + mat[(1,1)] + mat[(2,2)]) / (Guess0 + Guess1 + Guess2) | |
print(Prec_macro, Prec_micro) | |
print(Prec0, Recall0) | |
print(Prec1, Recall1) | |
print(Prec2, Recall2) | |
# NLPR original ending | |
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_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_test", False, "Whether to run test on the test set.") # ADDED | |
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("test_batch_size", 8, "Total batch size for eval.") | |
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 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() | |
# ADDED | |
def get_test_examples(self, data_dir): | |
"""Gets a collection of `InputExample`s for the dev set.""" | |
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 | |
### NOT FROM THE OFFICIAL REPO | |
class SnliProcessor(DataProcessor): | |
"""Processor for the SNLI data set (GLUE version).""" | |
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") | |
# ADDED | |
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 ["contradiction", "entailment", "neutral"] | |
def _create_examples(self, lines, set_type): | |
"""Creates examples for the training and dev sets.""" | |
examples = [] | |
if len(lines) > 2000: | |
lines = lines[:2000] | |
for (i, line) in enumerate(lines): | |
if i == 0: | |
continue | |
guid = "%s-%s" % (set_type, tokenization.convert_to_unicode(line[0])) | |
text_a = tokenization.convert_to_unicode(line[7]) | |
text_b = tokenization.convert_to_unicode(line[8]) | |
label = tokenization.convert_to_unicode(line[-1]) | |
examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) | |
return examples | |
### | |
class MnliProcessor(DataProcessor): | |
"""Processor for the MultiNLI data set (GLUE version).""" | |
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_matched.tsv")), | |
"dev_matched") | |
def get_labels(self): | |
"""See base class.""" | |
return ["contradiction", "entailment", "neutral"] | |
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, tokenization.convert_to_unicode(line[0])) | |
text_a = tokenization.convert_to_unicode(line[8]) | |
text_b = tokenization.convert_to_unicode(line[9]) | |
label = tokenization.convert_to_unicode(line[-1]) | |
examples.append( | |
InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) | |
return examples | |
class MrpcProcessor(DataProcessor): | |
"""Processor for the MRPC data set (GLUE version).""" | |
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_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) | |
text_a = tokenization.convert_to_unicode(line[3]) | |
text_b = tokenization.convert_to_unicode(line[4]) | |
label = tokenization.convert_to_unicode(line[0]) | |
examples.append( | |
InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) | |
return examples | |
class ColaProcessor(DataProcessor): | |
"""Processor for the CoLA data set (GLUE version).""" | |
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_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): | |
guid = "%s-%s" % (set_type, i) | |
text_a = tokenization.convert_to_unicode(line[3]) | |
label = tokenization.convert_to_unicode(line[1]) | |
examples.append( | |
InputExample(guid=guid, text_a=text_a, text_b=None, label=label)) | |
return examples | |
def convert_examples_to_features(examples, label_list, max_seq_length, | |
tokenizer): | |
"""Loads a data file into a list of `InputBatch`s.""" | |
label_map = {} | |
for (i, label) in enumerate(label_list): | |
label_map[label] = i | |
features = [] | |
for (ex_index, example) in enumerate(examples): | |
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)) | |
features.append( | |
InputFeatures( | |
input_ids=input_ids, | |
input_mask=input_mask, | |
segment_ids=segment_ids, | |
label_id=label_id)) | |
return features | |
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_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) | |
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) | |
def model_fn_builder(bert_config, num_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"] | |
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) = create_model( | |
bert_config, is_training, input_ids, input_mask, segment_ids, label_ids, | |
num_labels, use_one_hot_embeddings) | |
tvars = tf.trainable_variables() | |
scaffold_fn = None | |
if init_checkpoint: | |
(assignment_map, | |
initialized_variable_names) = modeling.get_assigment_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(per_example_loss, label_ids, logits): | |
predictions = tf.argmax(logits, axis=-1, output_type=tf.int32) | |
indices = tf.squeeze(tf.where(tf.less_equal(label_ids, 3 - 1)), 1) | |
labels1 = tf.cast(tf.gather(label_ids, indices), tf.int32) | |
predictions1 = tf.gather(predictions, indices) | |
#the_auc = tf.metrics.auc(labels1, predictions1) # not from original repo | |
#print("the auc:", auc) it's a fn callback not a | |
the_prec = tf.metrics.precision(labels1, predictions1) | |
the_rec = tf.metrics.recall(labels1, predictions1) | |
accuracy = tf.metrics.accuracy(label_ids, predictions) | |
loss = tf.metrics.mean(per_example_loss) | |
return { | |
"eval_accuracy": accuracy, | |
"eval_loss": loss, | |
#"eval_auc" : the_auc, | |
"eval_prec" : the_prec, | |
"eval_rec" : the_rec, | |
} | |
eval_metrics = (metric_fn, [per_example_loss, label_ids, logits]) | |
output_spec = tf.contrib.tpu.TPUEstimatorSpec( | |
mode=mode, | |
loss=total_loss, | |
eval_metrics=eval_metrics, | |
scaffold_fn=scaffold_fn) | |
else: | |
raise ValueError("Only TRAIN and EVAL modes are supported: %s" % (mode)) | |
return output_spec | |
return model_fn | |
def input_fn_builder(features, seq_length, is_training, drop_remainder): | |
"""Creates an `input_fn` closure to be passed to TPUEstimator.""" | |
all_input_ids = [] | |
all_input_mask = [] | |
all_segment_ids = [] | |
all_label_ids = [] | |
for feature in features: | |
all_input_ids.append(feature.input_ids) | |
all_input_mask.append(feature.input_mask) | |
all_segment_ids.append(feature.segment_ids) | |
all_label_ids.append(feature.label_id) | |
def input_fn(params): | |
"""The actual input function.""" | |
batch_size = params["batch_size"] | |
num_examples = len(features) | |
# This is for demo purposes and does NOT scale to large data sets. We do | |
# not use Dataset.from_generator() because that uses tf.py_func which is | |
# not TPU compatible. The right way to load data is with TFRecordReader. | |
d = tf.data.Dataset.from_tensor_slices({ | |
"input_ids": | |
tf.constant( | |
all_input_ids, shape=[num_examples, seq_length], | |
dtype=tf.int32), | |
"input_mask": | |
tf.constant( | |
all_input_mask, | |
shape=[num_examples, seq_length], | |
dtype=tf.int32), | |
"segment_ids": | |
tf.constant( | |
all_segment_ids, | |
shape=[num_examples, seq_length], | |
dtype=tf.int32), | |
"label_ids": | |
tf.constant(all_label_ids, shape=[num_examples], dtype=tf.int32), | |
}) | |
if is_training: | |
d = d.repeat() | |
d = d.shuffle(buffer_size=100) | |
d = d.batch(batch_size=batch_size, drop_remainder=drop_remainder) | |
return d | |
return input_fn | |
def main(_): | |
tf.logging.set_verbosity(tf.logging.INFO) | |
processors = { | |
"cola": ColaProcessor, | |
"mnli": MnliProcessor, | |
"mrpc": MrpcProcessor, | |
"snli": SnliProcessor | |
} | |
if not FLAGS.do_train and not FLAGS.do_eval: | |
raise ValueError("At least one of `do_train` or `do_eval` 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]() | |
label_list = processor.get_labels() | |
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_labels=len(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) | |
if FLAGS.do_train: | |
train_features = convert_examples_to_features( | |
train_examples, label_list, FLAGS.max_seq_length, tokenizer) | |
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 = input_fn_builder( | |
features=train_features, | |
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) | |
eval_features = convert_examples_to_features( | |
eval_examples, label_list, FLAGS.max_seq_length, tokenizer) | |
tf.logging.info("***** Running evaluation *****") | |
tf.logging.info(" Num examples = %d", len(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: | |
# Eval will be slightly WRONG on the TPU because it will truncate | |
# the last batch. | |
eval_steps = int(len(eval_examples) / FLAGS.eval_batch_size) | |
eval_drop_remainder = True if FLAGS.use_tpu else False | |
eval_input_fn = input_fn_builder( | |
features=eval_features, | |
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_test: | |
test_examples = processor.get_test_examples(FLAGS.data_dir) | |
test_features = convert_examples_to_features( | |
test_examples, label_list, FLAGS.max_seq_length, tokenizer) | |
tf.logging.info("***** Running test *****") | |
tf.logging.info(" Num examples = %d", len(test_examples)) | |
tf.logging.info(" Batch size = %d", FLAGS.test_batch_size) | |
# This tells the estimator to run through the entire set. | |
test_steps = None | |
# However, if running eval on the TPU, you will need to specify the | |
# number of steps. | |
if FLAGS.use_tpu: | |
# Eval will be slightly WRONG on the TPU because it will truncate | |
# the last batch. | |
test_steps = int(len(test_examples) / FLAGS.test_batch_size) | |
test_drop_remainder = True if FLAGS.use_tpu else False | |
test_input_fn = input_fn_builder( | |
features=test_features, | |
seq_length=FLAGS.max_seq_length, | |
is_training=False, | |
drop_remainder=test_drop_remainder) | |
result = estimator.evaluate(input_fn=test_input_fn, steps=test_steps) | |
output_test_file = os.path.join(FLAGS.output_dir, "test_results.txt") | |
with tf.gfile.GFile(output_test_file, "w") as writer: | |
tf.logging.info("***** Test 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 __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() |
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