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# Copyright 2023 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.
"""Unit tests for task."""
from absl.testing import parameterized
import tensorflow as tf, tf_keras
from official.core import exp_factory
from official.recommendation.ranking import task
from official.recommendation.ranking.data import data_pipeline
class TaskTest(parameterized.TestCase, tf.test.TestCase):
@parameterized.parameters(('dlrm_criteo', True),
('dlrm_criteo', False),
('dcn_criteo', True),
('dcn_criteo', False))
def test_task(self, config_name, is_training):
params = exp_factory.get_exp_config(config_name)
params.task.train_data.global_batch_size = 16
params.task.validation_data.global_batch_size = 16
params.task.model.vocab_sizes = [40, 12, 11, 13, 2, 5]
params.task.model.embedding_dim = 8
params.task.model.bottom_mlp = [64, 32, 8]
params.task.use_synthetic_data = True
params.task.model.num_dense_features = 5
ranking_task = task.RankingTask(params.task,
params.trainer)
if is_training:
dataset = data_pipeline.train_input_fn(params.task)
else:
dataset = data_pipeline.eval_input_fn(params.task)
iterator = iter(dataset(ctx=None))
model = ranking_task.build_model()
if is_training:
ranking_task.train_step(next(iterator), model, model.optimizer,
metrics=model.metrics)
else:
ranking_task.validation_step(next(iterator), model, metrics=model.metrics)
if __name__ == '__main__':
tf.test.main()
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