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# Copyright 2019 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 learning_rate."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from official.vision.image_classification import learning_rate
class LearningRateTests(tf.test.TestCase):
def test_warmup_decay(self):
"""Basic computational test for warmup decay."""
initial_lr = 0.01
decay_steps = 100
decay_rate = 0.01
warmup_steps = 10
base_lr = tf.keras.optimizers.schedules.ExponentialDecay(
initial_learning_rate=initial_lr,
decay_steps=decay_steps,
decay_rate=decay_rate)
lr = learning_rate.WarmupDecaySchedule(
lr_schedule=base_lr,
warmup_steps=warmup_steps)
for step in range(warmup_steps - 1):
config = lr.get_config()
self.assertEqual(config['warmup_steps'], warmup_steps)
self.assertAllClose(self.evaluate(lr(step)),
step / warmup_steps * initial_lr)
def test_piecewise_constant_decay_with_warmup(self):
"""Basic computational test for piecewise constant decay with warmup."""
boundaries = [1, 2, 3]
warmup_epochs = boundaries[0]
learning_rate_multipliers = [1.0, 0.1, 0.001]
expected_keys = [
'rescaled_lr', 'step_boundaries', 'lr_values', 'warmup_steps',
]
expected_lrs = [0.0, 0.1, 0.1]
lr = learning_rate.PiecewiseConstantDecayWithWarmup(
batch_size=256,
epoch_size=256,
warmup_epochs=warmup_epochs,
boundaries=boundaries[1:],
multipliers=learning_rate_multipliers)
step = 0
config = lr.get_config()
self.assertAllInSet(list(config.keys()), expected_keys)
for boundary, expected_lr in zip(boundaries, expected_lrs):
for _ in range(step, boundary):
self.assertAllClose(self.evaluate(lr(step)), expected_lr)
step += 1
def test_piecewise_constant_decay_invalid_boundaries(self):
with self.assertRaisesRegex(ValueError,
'The length of boundaries must be 1 less '):
learning_rate.PiecewiseConstantDecayWithWarmup(
batch_size=256,
epoch_size=256,
warmup_epochs=1,
boundaries=[1, 2],
multipliers=[1, 2])
def test_cosine_decay_with_warmup(self):
"""Basic computational test for cosine decay with warmup."""
expected_lrs = [0.0, 0.1, 0.05, 0.0]
lr = learning_rate.CosineDecayWithWarmup(
batch_size=256, total_steps=3, warmup_steps=1)
for step in [0, 1, 2, 3]:
self.assertAllClose(lr(step), expected_lrs[step])
if __name__ == '__main__':
tf.test.main()
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