train_test.py

# Copyright 2024 The Flax Authors.
#
# 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 flax.examples.mnist.mnist_lib."""

import pathlib
import tempfile

from absl.testing import absltest
import jax
from jax import numpy as jnp
import numpy as np
import tensorflow as tf
import tensorflow_datasets as tfds

from configs import default
import train


CNN_PARAMS = 825_034


class TrainTest(absltest.TestCase):
  """Test cases for train."""

  def setUp(self):
    super().setUp()
    # Make sure tf does not allocate gpu memory.
    tf.config.experimental.set_visible_devices([], "GPU")

  def test_cnn(self):
    """Tests CNN module used as the trainable model."""
    rng = jax.random.key(0)
    inputs = jnp.ones((1, 28, 28, 3), jnp.float32)
    output, variables = train.CNN().init_with_output(rng, inputs)

    self.assertEqual((1, 10), output.shape)
    self.assertEqual(
        CNN_PARAMS,
        sum(
            np.prod(arr.shape)
            for arr in jax.tree_util.tree_leaves(variables["params"])
        ),
    )

  def test_train_and_evaluate(self):
    """Tests training and evaluation code by running a single step."""
    # Create a temporary directory where tensorboard metrics are written.
    workdir = tempfile.mkdtemp()

    # Go two directories up to the root of the flax directory.
    flax_root_dir = pathlib.Path(__file__).parents[2]
    data_dir = str(flax_root_dir) + "/.tfds/metadata"  # pylint: disable=unused-variable

    # Define training configuration.
    config = default.get_config()
    config.num_epochs = 1
    config.batch_size = 8

    with tfds.testing.mock_data(num_examples=8, data_dir=data_dir):
      train.train_and_evaluate(config=config, workdir=workdir)


if __name__ == "__main__":
  absltest.main()