!239 Add dynamic learning rate decay and review optimizer code
Merge pull request !239 from fanglei/masterpull/239/MERGE
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5 years ago
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# Copyright 2020 Huawei Technologies Co., Ltd
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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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""" Test Dynamic Learning Rate """
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import pytest
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import mindspore
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from mindspore.nn import dynamic_lr as dr
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milestone = [10, 20, 30]
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learning_rates = [0.1, 0.05, 0.01]
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learning_rate = 0.1
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end_learning_rate = 0.01
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decay_rate = 0.9
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total_step = 30
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step_per_epoch = 3
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decay_epoch = 2
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min_lr = 0.01
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max_lr = 0.1
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power = 0.5
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class TestInputs:
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def test_milestone1(self):
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milestone1 = 1
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with pytest.raises(ValueError):
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dr.piecewise_constant_lr(milestone1, learning_rates)
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def test_milestone2(self):
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milestone1 = [20, 10, 1]
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with pytest.raises(ValueError):
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dr.piecewise_constant_lr(milestone1, learning_rates)
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milestone2 = [1.0, 2.0, True]
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with pytest.raises(ValueError):
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dr.piecewise_constant_lr(milestone2, learning_rates)
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def test_learning_rates1(self):
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lr = True
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with pytest.raises(ValueError):
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dr.piecewise_constant_lr(milestone, lr)
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def test_learning_rates2(self):
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lr = [1, 2, 1]
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with pytest.raises(ValueError):
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dr.piecewise_constant_lr(milestone, lr)
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def test_learning_rate_type(self):
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lr = True
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with pytest.raises(TypeError):
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dr.exponential_decay_lr(lr, decay_rate, total_step, step_per_epoch, decay_epoch)
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with pytest.raises(TypeError):
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dr.polynomial_decay_lr(lr, end_learning_rate, total_step, step_per_epoch, decay_epoch, power)
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def test_learning_rate_value(self):
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lr = -1.0
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with pytest.raises(ValueError):
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dr.exponential_decay_lr(lr, decay_rate, total_step, step_per_epoch, decay_epoch)
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with pytest.raises(ValueError):
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dr.polynomial_decay_lr(lr, end_learning_rate, total_step, step_per_epoch, decay_epoch, power)
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def test_end_learning_rate_type(self):
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lr = True
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with pytest.raises(TypeError):
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dr.polynomial_decay_lr(learning_rate, lr, total_step, step_per_epoch, decay_epoch, power)
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def test_end_learning_rate_value(self):
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lr = -1.0
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with pytest.raises(ValueError):
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dr.polynomial_decay_lr(learning_rate, lr, total_step, step_per_epoch, decay_epoch, power)
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def test_decay_rate_type(self):
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rate = 'a'
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with pytest.raises(TypeError):
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dr.exponential_decay_lr(learning_rate, rate, total_step, step_per_epoch, decay_epoch)
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def test_decay_rate_value(self):
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rate = -1.0
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with pytest.raises(ValueError):
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dr.exponential_decay_lr(learning_rate, rate, total_step, step_per_epoch, decay_epoch)
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def test_total_step1(self):
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total_step1 = 2.0
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with pytest.raises(ValueError):
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dr.exponential_decay_lr(learning_rate, decay_rate, total_step1, step_per_epoch, decay_epoch)
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with pytest.raises(ValueError):
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dr.cosine_decay_lr(min_lr, max_lr, total_step1, step_per_epoch, decay_epoch)
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with pytest.raises(ValueError):
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dr.polynomial_decay_lr(learning_rate, end_learning_rate, total_step1, step_per_epoch, decay_epoch, power)
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def test_total_step2(self):
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total_step1 = -1
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with pytest.raises(ValueError):
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dr.exponential_decay_lr(learning_rate, decay_rate, total_step1, step_per_epoch, decay_epoch)
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with pytest.raises(ValueError):
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dr.cosine_decay_lr(min_lr, max_lr, total_step1, step_per_epoch, decay_epoch)
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with pytest.raises(ValueError):
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dr.polynomial_decay_lr(learning_rate, end_learning_rate, total_step1, step_per_epoch, decay_epoch, power)
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def test_step_per_epoch1(self):
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step_per_epoch1 = True
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with pytest.raises(ValueError):
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dr.exponential_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch1, decay_epoch)
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with pytest.raises(ValueError):
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dr.cosine_decay_lr(min_lr, max_lr, total_step, step_per_epoch1, decay_epoch)
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with pytest.raises(ValueError):
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dr.polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_epoch1, decay_epoch, power)
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def test_step_per_epoch2(self):
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step_per_epoch1 = -1
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with pytest.raises(ValueError):
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dr.exponential_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch1, decay_epoch)
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with pytest.raises(ValueError):
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dr.cosine_decay_lr(min_lr, max_lr, total_step, step_per_epoch1, decay_epoch)
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with pytest.raises(ValueError):
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dr.polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_epoch1, decay_epoch, power)
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def test_decay_epoch1(self):
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decay_epoch1 = 'm'
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with pytest.raises(ValueError):
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dr.exponential_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch1)
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with pytest.raises(ValueError):
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dr.cosine_decay_lr(min_lr, max_lr, total_step, step_per_epoch, decay_epoch1)
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with pytest.raises(ValueError):
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dr.polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_epoch, decay_epoch1, power)
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def test_decay_epoch2(self):
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decay_epoch1 = -1
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with pytest.raises(ValueError):
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dr.exponential_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch1)
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with pytest.raises(ValueError):
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dr.cosine_decay_lr(min_lr, max_lr, total_step, step_per_epoch, decay_epoch1)
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with pytest.raises(ValueError):
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dr.polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_epoch, decay_epoch1, power)
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def test_is_stair(self):
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is_stair = 1
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with pytest.raises(ValueError):
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dr.exponential_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, is_stair)
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def test_min_lr_type(self):
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min_lr1 = True
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with pytest.raises(TypeError):
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dr.cosine_decay_lr(min_lr1, max_lr, total_step, step_per_epoch, decay_epoch)
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def test_min_lr_value(self):
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min_lr1 = -1.0
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with pytest.raises(ValueError):
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dr.cosine_decay_lr(min_lr1, max_lr, total_step, step_per_epoch, decay_epoch)
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def test_max_lr_type(self):
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max_lr1 = 'a'
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with pytest.raises(TypeError):
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dr.cosine_decay_lr(min_lr, max_lr1, total_step, step_per_epoch, decay_epoch)
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def test_max_lr_value(self):
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max_lr1 = -1.0
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with pytest.raises(ValueError):
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dr.cosine_decay_lr(min_lr, max_lr1, total_step, step_per_epoch, decay_epoch)
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def test_power(self):
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power1 = True
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with pytest.raises(ValueError):
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dr.polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_epoch, decay_epoch, power1)
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def test_update_decay_epoch(self):
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update_decay_epoch = 1
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with pytest.raises(ValueError):
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dr.polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_epoch, decay_epoch,
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power, update_decay_epoch)
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def test_learning_rate():
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lr = dr.piecewise_constant_lr(milestone, learning_rates)
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assert len(lr) == milestone[-1]
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def test_exponential_decay():
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lr1 = dr.exponential_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch)
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assert len(lr1) == total_step
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lr2 = dr.exponential_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, True)
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assert len(lr2) == total_step
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def test_enatural_exp_decay():
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lr1 = dr.natural_exp_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch)
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assert len(lr1) == total_step
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lr2 = dr.natural_exp_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, True)
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assert len(lr2) == total_step
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def test_inverse_decay():
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lr1 = dr.inverse_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch)
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assert len(lr1) == total_step
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lr2 = dr.inverse_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, True)
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assert len(lr2) == total_step
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def test_cosine_decay():
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lr = dr.cosine_decay_lr(min_lr, max_lr, total_step, step_per_epoch, decay_epoch)
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assert len(lr) == total_step
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def test_polynomial_decay():
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lr1 = dr.polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_epoch, decay_epoch, power)
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assert len(lr1) == total_step
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lr2 = dr.polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_epoch, decay_epoch, power,
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True)
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assert len(lr2) == total_step
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