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prepare_data.py
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prepare_data.py
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# Copyright 2019 The Texar Authors. All Rights Reserved.
#
# 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.
"""Produces TFRecord files and modifies data configuration file
"""
import os
import tensorflow as tf
import texar.tf as tx
# pylint: disable=no-name-in-module
from utils import data_utils
# pylint: disable=invalid-name, too-many-locals, too-many-statements
flags = tf.flags
FLAGS = flags.FLAGS
flags.DEFINE_string(
"task", "MRPC",
"The task to run experiment on. One of "
"{'COLA', 'MNLI', 'MRPC', 'XNLI', 'SST'}.")
flags.DEFINE_string(
"pretrained_model_name", 'bert-base-uncased',
"The name of pre-trained BERT model. See the doc of "
"`texar.tf.modules.PretrainedBERTMixin for all supported models.`")
flags.DEFINE_integer(
"max_seq_length", 128,
"The maximum length of sequence, longer sequence will be trimmed.")
flags.DEFINE_string(
"tfrecord_output_dir", None,
"The output directory where the TFRecord files will be generated. "
"By default it will be set to 'data/{task}'. E.g.: if "
"task is 'MRPC', it will be set as 'data/MRPC'")
tf.logging.set_verbosity(tf.logging.INFO)
def _modify_config_data(max_seq_length, num_train_data, num_classes):
# Modify the data configuration file
config_data_exists = os.path.isfile('./config_data.py')
if config_data_exists:
with open("./config_data.py", 'r') as file:
filedata = file.read()
filedata_lines = filedata.split('\n')
idx = 0
while True:
if idx >= len(filedata_lines):
break
line = filedata_lines[idx]
if (line.startswith('num_classes =') or
line.startswith('num_train_data =') or
line.startswith('max_seq_length =')):
filedata_lines.pop(idx)
idx -= 1
idx += 1
if len(filedata_lines) > 0:
insert_idx = 1
else:
insert_idx = 0
filedata_lines.insert(
insert_idx, '{} = {}'.format(
"num_train_data", num_train_data))
filedata_lines.insert(
insert_idx, '{} = {}'.format(
"num_classes", num_classes))
filedata_lines.insert(
insert_idx, '{} = {}'.format(
"max_seq_length", max_seq_length))
with open("./config_data.py", 'w') as file:
file.write('\n'.join(filedata_lines))
tf.logging.info("config_data.py has been updated")
else:
tf.logging.info("config_data.py cannot be found")
tf.logging.info("Data preparation finished")
def main():
"""Prepares data.
"""
# Loads data
tf.logging.info("Loading data")
task_datasets_rename = {
"COLA": "CoLA",
"SST": "SST-2",
}
data_dir = 'data/{}'.format(FLAGS.task)
if FLAGS.task.upper() in task_datasets_rename:
data_dir = 'data/{}'.format(
task_datasets_rename[FLAGS.task])
if FLAGS.tfrecord_output_dir is None:
tfrecord_output_dir = data_dir
else:
tfrecord_output_dir = FLAGS.tfrecord_output_dir
tx.utils.maybe_create_dir(tfrecord_output_dir)
processors = {
"COLA": data_utils.ColaProcessor,
"MNLI": data_utils.MnliProcessor,
"MRPC": data_utils.MrpcProcessor,
"XNLI": data_utils.XnliProcessor,
'SST': data_utils.SSTProcessor
}
processor = processors[FLAGS.task]()
num_classes = len(processor.get_labels())
num_train_data = len(processor.get_train_examples(data_dir))
tf.logging.info(
'num_classes:%d; num_train_data:%d' % (num_classes, num_train_data))
tokenizer = tx.data.BERTTokenizer(
pretrained_model_name=FLAGS.pretrained_model_name)
# Produces TFRecord files
data_utils.prepare_TFRecord_data(
processor=processor,
tokenizer=tokenizer,
data_dir=data_dir,
max_seq_length=FLAGS.max_seq_length,
output_dir=tfrecord_output_dir)
_modify_config_data(FLAGS.max_seq_length, num_train_data, num_classes)
if __name__ == "__main__":
main()