train_test.py

# 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.

import json
import os
import random

from absl import flags
from absl import logging
from absl.testing import flagsaver
import tensorflow as tf
from official.projects.assemblenet 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=(36, 36, 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_run(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 = 'assemblenet50_kinetics600'
    logging.info('Test pipeline correctness.')
    num_frames = 4

    params_override = json.dumps({
        'runtime': {
            'mixed_precision_dtype': 'float32',
        },
        'trainer': {
            'train_steps': 1,
            'validation_steps': 1,
        },
        'task': {
            'model': {
                'backbone': {
                    'assemblenet': {
                        'model_id': '26',
                        'num_frames': num_frames,
                    },
                },
            },
            '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')

    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()