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# Copyright 2024 The TensorFlow 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.
"""Tests for official.nlp.data.question_answering_dataloader."""
import os
import numpy as np
import tensorflow as tf, tf_keras
from official.nlp.data import question_answering_dataloader
def _create_fake_dataset(output_path, seq_length):
"""Creates a fake dataset."""
writer = tf.io.TFRecordWriter(output_path)
def create_int_feature(values):
f = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values)))
return f
for _ in range(100):
features = {}
input_ids = np.random.randint(100, size=(seq_length))
features['input_ids'] = create_int_feature(input_ids)
features['input_mask'] = create_int_feature(np.ones_like(input_ids))
features['segment_ids'] = create_int_feature(np.ones_like(input_ids))
features['start_positions'] = create_int_feature(np.array([0]))
features['end_positions'] = create_int_feature(np.array([10]))
tf_example = tf.train.Example(features=tf.train.Features(feature=features))
writer.write(tf_example.SerializeToString())
writer.close()
class QuestionAnsweringDataTest(tf.test.TestCase):
def test_load_dataset(self):
seq_length = 128
batch_size = 10
input_path = os.path.join(self.get_temp_dir(), 'train.tf_record')
_create_fake_dataset(input_path, seq_length)
data_config = question_answering_dataloader.QADataConfig(
is_training=True,
input_path=input_path,
seq_length=seq_length,
global_batch_size=batch_size)
dataset = question_answering_dataloader.QuestionAnsweringDataLoader(
data_config).load()
features, labels = next(iter(dataset))
self.assertCountEqual(['input_word_ids', 'input_mask', 'input_type_ids'],
features.keys())
self.assertEqual(features['input_word_ids'].shape, (batch_size, seq_length))
self.assertEqual(features['input_mask'].shape, (batch_size, seq_length))
self.assertEqual(features['input_type_ids'].shape, (batch_size, seq_length))
self.assertCountEqual(['start_positions', 'end_positions'], labels.keys())
self.assertEqual(labels['start_positions'].shape, (batch_size,))
self.assertEqual(labels['end_positions'].shape, (batch_size,))
if __name__ == '__main__':
tf.test.main()
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