File size: 2,575 Bytes
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#
# 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 nlp.projects.example.classification_example."""
import tensorflow as tf, tf_keras
from official.core import config_definitions as cfg
from official.nlp.configs import encoders
from official.projects.text_classification_example import classification_data_loader
from official.projects.text_classification_example import classification_example
class ClassificationExampleTest(tf.test.TestCase):
def get_model_config(self):
return classification_example.ModelConfig(
encoder=encoders.EncoderConfig(
bert=encoders.BertEncoderConfig(vocab_size=30522, num_layers=2)))
def get_dummy_dataset(self, params: cfg.DataConfig):
def dummy_data(_):
dummy_ids = tf.zeros((1, params.seq_length), dtype=tf.int32)
x = dict(
input_word_ids=dummy_ids,
input_mask=dummy_ids,
input_type_ids=dummy_ids)
y = tf.zeros((1, 1), dtype=tf.int32)
return (x, y)
dataset = tf.data.Dataset.range(1)
dataset = dataset.repeat()
dataset = dataset.map(
dummy_data, num_parallel_calls=tf.data.experimental.AUTOTUNE)
return dataset
def test_task_with_dummy_data(self):
train_data_config = (
classification_data_loader.ClassificationExampleDataConfig(
input_path='dummy', seq_length=128, global_batch_size=1))
task_config = classification_example.ClassificationExampleConfig(
model=self.get_model_config(),)
task = classification_example.ClassificationExampleTask(task_config)
task.build_inputs = self.get_dummy_dataset
model = task.build_model()
metrics = task.build_metrics()
dataset = task.build_inputs(train_data_config)
iterator = iter(dataset)
optimizer = tf_keras.optimizers.SGD(lr=0.1)
task.initialize(model)
task.train_step(next(iterator), model, optimizer, metrics=metrics)
if __name__ == '__main__':
tf.test.main()
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