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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 lr_schedule."""
from absl.testing import parameterized
import tensorflow as tf, tf_keras
from official.modeling.optimization import lr_schedule
class PowerAndLinearDecayTest(tf.test.TestCase, parameterized.TestCase):
@parameterized.named_parameters(
dict(
testcase_name='power_only',
init_lr=1.0,
power=-1.0,
linear_decay_fraction=0.0,
total_decay_steps=100,
offset=0,
expected=[[0, 1.0], [1, 1.0], [40, 1. / 40.], [60, 1. / 60],
[100, 1. / 100]]),
dict(
testcase_name='linear_only',
init_lr=1.0,
power=0.0,
linear_decay_fraction=1.0,
total_decay_steps=100,
offset=0,
expected=[[0, 1.0], [1, 0.99], [40, 0.6], [60, 0.4], [100, 0.0]]),
dict(
testcase_name='general',
init_lr=1.0,
power=-1.0,
linear_decay_fraction=0.5,
total_decay_steps=100,
offset=0,
expected=[[0, 1.0], [1, 1.0], [40, 1. / 40.],
[60, 1. / 60. * 0.8], [100, 0.0]]),
dict(
testcase_name='offset',
init_lr=1.0,
power=-1.0,
linear_decay_fraction=0.5,
total_decay_steps=100,
offset=90,
expected=[[0, 1.0], [90, 1.0], [91, 1.0], [130, 1. / 40.],
[150, 1. / 60. * 0.8], [190, 0.0], [200, 0.0]]),
)
def test_power_linear_lr_schedule(self, init_lr, power, linear_decay_fraction,
total_decay_steps, offset, expected):
lr = lr_schedule.PowerAndLinearDecay(
initial_learning_rate=init_lr,
power=power,
linear_decay_fraction=linear_decay_fraction,
total_decay_steps=total_decay_steps,
offset=offset)
for step, value in expected:
self.assertAlmostEqual(lr(step).numpy(), value)
class OffsetLearningRateTest(tf.test.TestCase, parameterized.TestCase):
@parameterized.parameters(
dict(class_name=lr_schedule.PiecewiseConstantDecayWithOffset),
dict(class_name=lr_schedule.PolynomialDecayWithOffset),
dict(class_name=lr_schedule.ExponentialDecayWithOffset),
dict(class_name=lr_schedule.CosineDecayWithOffset),
)
def test_generated_docstring(self, class_name):
self.assertNotEmpty(class_name.__init__.__doc__)
@parameterized.parameters(
dict(
class_name=lr_schedule.PiecewiseConstantDecayWithOffset,
kwarg=dict(boundaries=[50, 80], values=[1.0, 0.5, 0.1])),
dict(
class_name=lr_schedule.PolynomialDecayWithOffset,
kwarg=dict(initial_learning_rate=1.0, decay_steps=100)),
dict(
class_name=lr_schedule.ExponentialDecayWithOffset,
kwarg=dict(
initial_learning_rate=1.0, decay_steps=100, decay_rate=0.5)),
dict(
class_name=lr_schedule.CosineDecayWithOffset,
kwarg=dict(initial_learning_rate=1.0, decay_steps=100)),
)
def test_offset(self, class_name, kwarg):
offset = 10
offset_lr = class_name(offset=offset, **kwarg)
base_lr = class_name.base_lr_class(**kwarg)
self.assertIsInstance(offset_lr, class_name)
for step in range(10, 101, 10):
self.assertEqual(offset_lr(step), base_lr(step - offset))
if __name__ == '__main__':
tf.test.main()