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Guy Jacob authored
* summary_graph.py: * Change ONNX op.uniqueName() to op.debugName() * Removed scope-naming workaround which isn't needed in PyTorch 1.3 * Tests: * Naming of trace entries changed in 1.3. Fixed SummaryGraph unit test that checked that * Adjusted expected values in full_flow_tests * Adjusted tolerance in test_sim_bn_fold * Filter some new warningsGuy Jacob authored* summary_graph.py: * Change ONNX op.uniqueName() to op.debugName() * Removed scope-naming workaround which isn't needed in PyTorch 1.3 * Tests: * Naming of trace entries changed in 1.3. Fixed SummaryGraph unit test that checked that * Adjusted expected values in full_flow_tests * Adjusted tolerance in test_sim_bn_fold * Filter some new warnings
test_learning_rate.py 2.36 KiB
#
# Copyright (c) 2018 Intel Corporation
#
# 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.
#
import pytest
import torch
from torch.optim import Optimizer
from distiller.learning_rate import MultiStepMultiGammaLR
@pytest.mark.filterwarnings('ignore:Detected call of')
def test_multi_step_multi_gamma_lr():
dummy_tensor = torch.zeros(3, 3, 3, requires_grad=True)
dummy_optimizer = Optimizer([dummy_tensor], {'lr': 0.1})
# Test input checks
with pytest.raises(ValueError):
lr_sched = MultiStepMultiGammaLR(dummy_optimizer, milestones=[60, 30, 80], gammas=[0.1, 0.1, 0.2])
with pytest.raises(ValueError):
lr_sched = MultiStepMultiGammaLR(dummy_optimizer, milestones=[30, 60], gammas=[0.1, 0.1, 0.2])
with pytest.raises(ValueError):
lr_sched = MultiStepMultiGammaLR(dummy_optimizer, milestones=[30, 60, 80], gammas=[0.1, 0.1])
# Test functionality
lr_sched = MultiStepMultiGammaLR(dummy_optimizer, milestones=[30, 60, 80], gammas=[0.1, 0.1, 0.2])
expected_gammas = [1, 1 * 0.1, 1 * 0.1 * 0.1, 1 * 0.1 * 0.1 * 0.2]
expected_lrs = [0.1 * gamma for gamma in expected_gammas]
assert lr_sched.multiplicative_gammas == expected_gammas
lr_sched.step(0)
assert dummy_optimizer.param_groups[0]['lr'] == expected_lrs[0]
lr_sched.step(15)
assert dummy_optimizer.param_groups[0]['lr'] == expected_lrs[0]
lr_sched.step(30)
assert dummy_optimizer.param_groups[0]['lr'] == expected_lrs[1]
lr_sched.step(33)
assert dummy_optimizer.param_groups[0]['lr'] == expected_lrs[1]
lr_sched.step(60)
assert dummy_optimizer.param_groups[0]['lr'] == expected_lrs[2]
lr_sched.step(79)
assert dummy_optimizer.param_groups[0]['lr'] == expected_lrs[2]
lr_sched.step(80)
assert dummy_optimizer.param_groups[0]['lr'] == expected_lrs[3]
lr_sched.step(100)
assert dummy_optimizer.param_groups[0]['lr'] == expected_lrs[3]