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156 lines
5.5 KiB
156 lines
5.5 KiB
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Tests for object_detection.utils.learning_schedules."""
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import numpy as np
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import tensorflow as tf
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from object_detection.utils import learning_schedules
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from object_detection.utils import test_case
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class LearningSchedulesTest(test_case.TestCase):
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def testExponentialDecayWithBurnin(self):
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def graph_fn(global_step):
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learning_rate_base = 1.0
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learning_rate_decay_steps = 3
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learning_rate_decay_factor = .1
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burnin_learning_rate = .5
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burnin_steps = 2
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min_learning_rate = .05
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learning_rate = learning_schedules.exponential_decay_with_burnin(
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global_step, learning_rate_base, learning_rate_decay_steps,
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learning_rate_decay_factor, burnin_learning_rate, burnin_steps,
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min_learning_rate)
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assert learning_rate.op.name.endswith('learning_rate')
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return (learning_rate,)
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output_rates = [
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self.execute(graph_fn, [np.array(i).astype(np.int64)]) for i in range(9)
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]
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exp_rates = [.5, .5, 1, 1, 1, .1, .1, .1, .05]
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self.assertAllClose(output_rates, exp_rates, rtol=1e-4)
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def testCosineDecayWithWarmup(self):
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def graph_fn(global_step):
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learning_rate_base = 1.0
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total_steps = 100
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warmup_learning_rate = 0.1
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warmup_steps = 9
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learning_rate = learning_schedules.cosine_decay_with_warmup(
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global_step, learning_rate_base, total_steps,
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warmup_learning_rate, warmup_steps)
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assert learning_rate.op.name.endswith('learning_rate')
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return (learning_rate,)
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exp_rates = [0.1, 0.5, 0.9, 1.0, 0]
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input_global_steps = [0, 4, 8, 9, 100]
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output_rates = [
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self.execute(graph_fn, [np.array(step).astype(np.int64)])
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for step in input_global_steps
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]
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self.assertAllClose(output_rates, exp_rates)
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def testCosineDecayAfterTotalSteps(self):
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def graph_fn(global_step):
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learning_rate_base = 1.0
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total_steps = 100
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warmup_learning_rate = 0.1
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warmup_steps = 9
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learning_rate = learning_schedules.cosine_decay_with_warmup(
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global_step, learning_rate_base, total_steps,
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warmup_learning_rate, warmup_steps)
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assert learning_rate.op.name.endswith('learning_rate')
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return (learning_rate,)
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exp_rates = [0]
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input_global_steps = [101]
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output_rates = [
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self.execute(graph_fn, [np.array(step).astype(np.int64)])
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for step in input_global_steps
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]
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self.assertAllClose(output_rates, exp_rates)
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def testCosineDecayWithHoldBaseLearningRateSteps(self):
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def graph_fn(global_step):
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learning_rate_base = 1.0
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total_steps = 120
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warmup_learning_rate = 0.1
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warmup_steps = 9
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hold_base_rate_steps = 20
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learning_rate = learning_schedules.cosine_decay_with_warmup(
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global_step, learning_rate_base, total_steps,
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warmup_learning_rate, warmup_steps, hold_base_rate_steps)
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assert learning_rate.op.name.endswith('learning_rate')
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return (learning_rate,)
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exp_rates = [0.1, 0.5, 0.9, 1.0, 1.0, 1.0, 0.999702, 0.874255, 0.577365,
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0.0]
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input_global_steps = [0, 4, 8, 9, 10, 29, 30, 50, 70, 120]
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output_rates = [
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self.execute(graph_fn, [np.array(step).astype(np.int64)])
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for step in input_global_steps
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]
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self.assertAllClose(output_rates, exp_rates)
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def testManualStepping(self):
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def graph_fn(global_step):
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boundaries = [2, 3, 7]
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rates = [1.0, 2.0, 3.0, 4.0]
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learning_rate = learning_schedules.manual_stepping(
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global_step, boundaries, rates)
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assert learning_rate.op.name.endswith('learning_rate')
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return (learning_rate,)
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output_rates = [
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self.execute(graph_fn, [np.array(i).astype(np.int64)])
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for i in range(10)
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]
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exp_rates = [1.0, 1.0, 2.0, 3.0, 3.0, 3.0, 3.0, 4.0, 4.0, 4.0]
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self.assertAllClose(output_rates, exp_rates)
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def testManualSteppingWithWarmup(self):
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def graph_fn(global_step):
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boundaries = [4, 6, 8]
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rates = [0.02, 0.10, 0.01, 0.001]
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learning_rate = learning_schedules.manual_stepping(
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global_step, boundaries, rates, warmup=True)
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assert learning_rate.op.name.endswith('learning_rate')
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return (learning_rate,)
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output_rates = [
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self.execute(graph_fn, [np.array(i).astype(np.int64)])
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for i in range(9)
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]
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exp_rates = [0.02, 0.04, 0.06, 0.08, 0.10, 0.10, 0.01, 0.01, 0.001]
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self.assertAllClose(output_rates, exp_rates)
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def testManualSteppingWithZeroBoundaries(self):
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def graph_fn(global_step):
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boundaries = []
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rates = [0.01]
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learning_rate = learning_schedules.manual_stepping(
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global_step, boundaries, rates)
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return (learning_rate,)
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output_rates = [
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self.execute(graph_fn, [np.array(i).astype(np.int64)])
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for i in range(4)
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]
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exp_rates = [0.01] * 4
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self.assertAllClose(output_rates, exp_rates)
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if __name__ == '__main__':
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tf.test.main()
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