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research/object_detection/utils/learning_schedules_test.py
183 строки
6 KB
Zhichao Lu
Add an exponential decay learning rate schedule with warmup.
30 ноя 2020, 07:45
30 ноя 2020, 07:45
3a6079c
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# Copyright 2017 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 object_detection.utils.learning_schedules.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np from six.moves import range import tensorflow.compat.v1 as tf from object_detection.utils import learning_schedules from object_detection.utils import test_case class LearningSchedulesTest(test_case.TestCase): def testExponentialDecayWithBurnin(self): def graph_fn(global_step): learning_rate_base = 1.0 learning_rate_decay_steps = 3 learning_rate_decay_factor = .1 burnin_learning_rate = .5 burnin_steps = 2 min_learning_rate = .05 learning_rate = learning_schedules.exponential_decay_with_burnin( global_step, learning_rate_base, learning_rate_decay_steps, learning_rate_decay_factor, burnin_learning_rate, burnin_steps, min_learning_rate) assert learning_rate.op.name.endswith('learning_rate') return (learning_rate,) output_rates = [ self.execute(graph_fn, [np.array(i).astype(np.int64)]) for i in range(9) ] exp_rates = [.5, .5, 1, 1, 1, .1, .1, .1, .05] self.assertAllClose(output_rates, exp_rates, rtol=1e-4) def testExponentialDecayWithWarmup(self): def graph_fn(global_step): learning_rate_base = 1.0 learning_rate_decay_steps = 3 learning_rate_decay_factor = .1 warmup_learning_rate = .5 warmup_steps = 2 min_learning_rate = .05 learning_rate = learning_schedules.exponential_decay_with_warmup( global_step, learning_rate_base, learning_rate_decay_steps, learning_rate_decay_factor, warmup_learning_rate, warmup_steps, min_learning_rate) assert learning_rate.op.name.endswith('learning_rate') return (learning_rate,) output_rates = [ self.execute(graph_fn, [np.array(i).astype(np.int64)]) for i in range(9) ] exp_rates = [.5, .75, 1, 1, 1, .1, .1, .1, .05] self.assertAllClose(output_rates, exp_rates, rtol=1e-4) def testCosineDecayWithWarmup(self): def graph_fn(global_step): learning_rate_base = 1.0 total_steps = 100 warmup_learning_rate = 0.1 warmup_steps = 9 learning_rate = learning_schedules.cosine_decay_with_warmup( global_step, learning_rate_base, total_steps, warmup_learning_rate, warmup_steps) assert learning_rate.op.name.endswith('learning_rate') return (learning_rate,) exp_rates = [0.1, 0.5, 0.9, 1.0, 0] input_global_steps = [0, 4, 8, 9, 100] output_rates = [ self.execute(graph_fn, [np.array(step).astype(np.int64)]) for step in input_global_steps ] self.assertAllClose(output_rates, exp_rates) def testCosineDecayAfterTotalSteps(self): def graph_fn(global_step): learning_rate_base = 1.0 total_steps = 100 warmup_learning_rate = 0.1 warmup_steps = 9 learning_rate = learning_schedules.cosine_decay_with_warmup( global_step, learning_rate_base, total_steps, warmup_learning_rate, warmup_steps) assert learning_rate.op.name.endswith('learning_rate') return (learning_rate,) exp_rates = [0] input_global_steps = [101] output_rates = [ self.execute(graph_fn, [np.array(step).astype(np.int64)]) for step in input_global_steps ] self.assertAllClose(output_rates, exp_rates) def testCosineDecayWithHoldBaseLearningRateSteps(self): def graph_fn(global_step): learning_rate_base = 1.0 total_steps = 120 warmup_learning_rate = 0.1 warmup_steps = 9 hold_base_rate_steps = 20 learning_rate = learning_schedules.cosine_decay_with_warmup( global_step, learning_rate_base, total_steps, warmup_learning_rate, warmup_steps, hold_base_rate_steps) assert learning_rate.op.name.endswith('learning_rate') return (learning_rate,) exp_rates = [0.1, 0.5, 0.9, 1.0, 1.0, 1.0, 0.999702, 0.874255, 0.577365, 0.0] input_global_steps = [0, 4, 8, 9, 10, 29, 30, 50, 70, 120] output_rates = [ self.execute(graph_fn, [np.array(step).astype(np.int64)]) for step in input_global_steps ] self.assertAllClose(output_rates, exp_rates) def testManualStepping(self): def graph_fn(global_step): boundaries = [2, 3, 7] rates = [1.0, 2.0, 3.0, 4.0] learning_rate = learning_schedules.manual_stepping( global_step, boundaries, rates) assert learning_rate.op.name.endswith('learning_rate') return (learning_rate,) output_rates = [ self.execute(graph_fn, [np.array(i).astype(np.int64)]) for i in range(10) ] exp_rates = [1.0, 1.0, 2.0, 3.0, 3.0, 3.0, 3.0, 4.0, 4.0, 4.0] self.assertAllClose(output_rates, exp_rates) def testManualSteppingWithWarmup(self): def graph_fn(global_step): boundaries = [4, 6, 8] rates = [0.02, 0.10, 0.01, 0.001] learning_rate = learning_schedules.manual_stepping( global_step, boundaries, rates, warmup=True) assert learning_rate.op.name.endswith('learning_rate') return (learning_rate,) output_rates = [ self.execute(graph_fn, [np.array(i).astype(np.int64)]) for i in range(9) ] exp_rates = [0.02, 0.04, 0.06, 0.08, 0.10, 0.10, 0.01, 0.01, 0.001] self.assertAllClose(output_rates, exp_rates) def testManualSteppingWithZeroBoundaries(self): def graph_fn(global_step): boundaries = [] rates = [0.01] learning_rate = learning_schedules.manual_stepping( global_step, boundaries, rates) return (learning_rate,) output_rates = [ self.execute(graph_fn, [np.array(i).astype(np.int64)]) for i in range(4) ] exp_rates = [0.01] * 4 self.assertAllClose(output_rates, exp_rates) if __name__ == '__main__': tf.test.main()