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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): | |
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): | |
def test_generated_docstring(self, class_name): | |
self.assertNotEmpty(class_name.__init__.__doc__) | |
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() | |