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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.
"""Tests for train.py."""
import json
import os
import random
from absl import flags
from absl import logging
from absl.testing import flagsaver
import tensorflow as tf, tf_keras
from official.projects.movinet import train as train_lib
from official.vision.dataloaders import tfexample_utils
FLAGS = flags.FLAGS
class TrainTest(tf.test.TestCase):
def setUp(self):
super(TrainTest, self).setUp()
self._model_dir = os.path.join(self.get_temp_dir(), 'model_dir')
tf.io.gfile.makedirs(self._model_dir)
data_dir = os.path.join(self.get_temp_dir(), 'data')
tf.io.gfile.makedirs(data_dir)
self._data_path = os.path.join(data_dir, 'data.tfrecord')
# pylint: disable=g-complex-comprehension
examples = [
tfexample_utils.make_video_test_example(
image_shape=(32, 32, 3),
audio_shape=(20, 128),
label=random.randint(0, 100)) for _ in range(2)
]
# pylint: enable=g-complex-comprehension
tfexample_utils.dump_to_tfrecord(self._data_path, tf_examples=examples)
def test_train_and_evaluation_pipeline_runs(self):
saved_flag_values = flagsaver.save_flag_values()
train_lib.tfm_flags.define_flags()
FLAGS.mode = 'train'
FLAGS.model_dir = self._model_dir
FLAGS.experiment = 'movinet_kinetics600'
logging.info('Test pipeline correctness.')
num_frames = 4
# Test model training pipeline runs.
params_override = json.dumps({
'runtime': {
'distribution_strategy': 'mirrored',
'mixed_precision_dtype': 'float32',
},
'trainer': {
'train_steps': 2,
'validation_steps': 2,
},
'task': {
'train_data': {
'input_path': self._data_path,
'file_type': 'tfrecord',
'feature_shape': [num_frames, 32, 32, 3],
'global_batch_size': 2,
},
'validation_data': {
'input_path': self._data_path,
'file_type': 'tfrecord',
'global_batch_size': 2,
'feature_shape': [num_frames * 2, 32, 32, 3],
}
}
})
FLAGS.params_override = params_override
train_lib.main('unused_args')
# Test model evaluation pipeline runs on newly produced checkpoint.
FLAGS.mode = 'eval'
with train_lib.gin.unlock_config():
train_lib.main('unused_args')
flagsaver.restore_flag_values(saved_flag_values)
if __name__ == '__main__':
tf.test.main()
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