parameters_test.py

# Copyright 2018 Google LLC. 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 of parameters."""

from absl.testing import parameterized
import tensorflow as tf
from tensorflow_compression.python.layers import parameters


class ParameterTest:

  def test_initial_value_is_reproduced(self):
    initial_value = tf.random.uniform(self.shape, dtype=tf.float32)
    parameter = self.cls(initial_value, **self.kwargs)
    self.assertAllClose(initial_value, parameter(), atol=1e-6, rtol=0)

  def test_name_and_value_are_reproduced_after_serialization(self):
    initial_value = tf.random.uniform(self.shape, dtype=tf.float32)
    parameter = self.cls(initial_value, **self.kwargs)
    name_before = parameter.name
    value_before = parameter()
    json = tf.keras.utils.legacy.serialize_keras_object(parameter)
    weights = parameter.get_weights()
    parameter = tf.keras.utils.legacy.deserialize_keras_object(json)
    self.assertIsInstance(parameter, self.cls)
    self.assertEqual(name_before, parameter.name)
    parameter.set_weights(weights)
    value_after = parameter()
    self.assertAllEqual(value_before, value_after)
    self.assertEqual(value_before.dtype.name, value_after.dtype.name)

  def test_converts_to_tensor(self):
    initial_value = tf.random.uniform(self.shape, dtype=tf.float32)
    parameter = self.cls(initial_value, **self.kwargs)
    value = parameter()
    converted = tf.convert_to_tensor(parameter)
    self.assertAllEqual(value, converted)
    self.assertEqual(value.dtype.name, converted.dtype.name)


class RDFTParameterTest(ParameterTest, tf.test.TestCase,
                        parameterized.TestCase):

  cls = parameters.RDFTParameter
  kwargs = dict(name="rdft_kernel")
  shape = (3, 3, 1, 2)

  # TODO(jonycgn): Find out why 3D RFFT gradients are not implemented in TF.
  @parameterized.parameters((7, 3, 2), (5, 3, 1, 2))
  def test_gradients_propagate(self, *shape):
    initial_value = tf.random.uniform(shape, dtype=tf.float32)
    parameter = self.cls(initial_value, **self.kwargs)
    rand = tf.random.uniform(shape)
    with tf.GradientTape() as tape:
      loss = tf.reduce_sum(rand * parameter())
    gradients = tape.gradient(loss, parameter.variables)
    self.assertLen(gradients, 2)
    self.assertNotAllClose(
        tf.zeros_like(gradients[0]), gradients[0], atol=1e-1, rtol=0)
    self.assertNotAllClose(
        tf.zeros_like(gradients[1]), gradients[1], atol=1e-1, rtol=0)


class GDNParameterTest(ParameterTest, tf.test.TestCase):

  cls = parameters.GDNParameter
  kwargs = dict(name="gdn_parameter")
  shape = (2, 1, 3)

  def test_initial_value_is_reproduced_with_minimum(self):
    initial_value = tf.random.uniform(self.shape, dtype=tf.float32)
    parameter = self.cls(initial_value, minimum=.5)
    expected_value = tf.maximum(initial_value, .5)
    self.assertAllClose(expected_value, parameter(), atol=1e-6, rtol=0)


if __name__ == "__main__":
  tf.test.main()