# Copyright (c) OpenMMLab. All rights reserved.
import math
import os.path as osp
import tempfile
from unittest import TestCase
import torch
import torch.nn.functional as F
import torch.optim as optim
from mmengine.optim import OptimWrapper
# yapf: disable
from mmengine.optim.scheduler import (ConstantParamScheduler,
CosineAnnealingParamScheduler,
CosineRestartParamScheduler,
ExponentialParamScheduler,
LinearParamScheduler,
MultiStepParamScheduler,
OneCycleParamScheduler,
PolyParamScheduler,
ReduceOnPlateauParamScheduler,
StepParamScheduler, _ParamScheduler)
# yapf: enable
from mmengine.testing import assert_allclose
class ToyModel(torch.nn.Module):
def __init__(self):
super().__init__()
self.conv1 = torch.nn.Conv2d(1, 1, 1)
self.conv2 = torch.nn.Conv2d(1, 1, 1)
def forward(self, x):
return self.conv2(F.relu(self.conv1(x)))
class TestParameterScheduler(TestCase):
def setUp(self):
"""Setup the model and optimizer which are used in every test method.
TestCase calls functions in this order: setUp() -> testMethod() ->
tearDown() -> cleanUp()
"""
self.model = ToyModel()
self.layer2_mult = 10
lr = 0.05
momentum = 0.01
weight_decay = 5e-4
self.optimizer = optim.SGD(
[{
'params': self.model.conv1.parameters()
}, {
'params': self.model.conv2.parameters(),
'lr': lr * self.layer2_mult,
'momentum': momentum * self.layer2_mult,
'weight_decay': weight_decay * self.layer2_mult
}],
lr=lr,
momentum=momentum,
weight_decay=weight_decay)
self.temp_dir = tempfile.TemporaryDirectory()
def test_base_scheduler_step(self):
with self.assertRaises(NotImplementedError):
_ParamScheduler(self.optimizer, param_name='lr')
def test_invalid_optimizer(self):
with self.assertRaisesRegex(TypeError, 'should be an Optimizer'):
StepParamScheduler(
'invalid_optimizer', step_size=1, param_name='lr')
def test_overwrite_optimzer_step(self):
# raise warning if the counter in optimizer.step() is overwritten
scheduler = ExponentialParamScheduler(
self.optimizer, param_name='lr', gamma=0.9)
def overwrite_fun():
pass
self.optimizer.step = overwrite_fun
self.optimizer.step()
self.assertWarnsRegex(UserWarning, r'how-to-adjust-learning-rate',
scheduler.step)
def test_resume(self):
# test invalid case: optimizer and scheduler are not both resumed
with self.assertRaisesRegex(KeyError,
"param 'initial_lr' is not specified"):
StepParamScheduler(
self.optimizer,
param_name='lr',
gamma=0.1,
step_size=3,
last_step=10)
# test manually resume with ``last_step`` instead of load_state_dict
epochs = 10
targets = [0.05 * (0.9**x) for x in range(epochs)]
scheduler = ExponentialParamScheduler(
self.optimizer, param_name='lr', gamma=0.9)
results = []
for epoch in range(5):
results.append(self.optimizer.param_groups[0]['lr'])
# The order should be
# train_epoch() -> save_checkpoint() -> scheduler.step().
# Break at here to simulate the checkpoint is saved before
# the scheduler.step().
if epoch == 4:
break
scheduler.step()
scheduler2 = ExponentialParamScheduler(
self.optimizer, param_name='lr', gamma=0.9, last_step=4)
for epoch in range(6):
results.append(self.optimizer.param_groups[0]['lr'])
scheduler2.step()
for epoch in range(epochs):
assert_allclose(
targets[epoch],
results[epoch],
msg='lr is wrong in epoch {}: expected {}, got {}'.format(
epoch, targets[epoch], results[epoch]),
atol=1e-5,
rtol=0)
def test_scheduler_before_optim_warning(self):
"""warns if scheduler is used before optimizer."""
def call_sch_before_optim():
scheduler = StepParamScheduler(
self.optimizer, param_name='lr', gamma=0.1, step_size=3)
scheduler.step()
self.optimizer.step()
# check warning doc link
self.assertWarnsRegex(UserWarning, r'how-to-adjust-learning-rate',
call_sch_before_optim)
# check warning when resume
for i, group in enumerate(self.optimizer.param_groups):
group['initial_lr'] = 0.01
def call_sch_before_optim_resume():
scheduler = StepParamScheduler(
self.optimizer,
param_name='lr',
gamma=0.1,
step_size=3,
last_step=10)
scheduler.step()
self.optimizer.step()
# check warning doc link
self.assertWarnsRegex(UserWarning, r'how-to-adjust-learning-rate',
call_sch_before_optim_resume)
def test_get_last_value(self):
epochs = 10
single_targets = [0.05] * 3 + [0.005] * 3 + [0.0005] * 3 + [0.00005]
targets = [
single_targets, [x * self.layer2_mult for x in single_targets]
]
scheduler = StepParamScheduler(
self.optimizer, param_name='lr', step_size=3, gamma=0.1)
for epoch in range(epochs):
result = scheduler.get_last_value()
self.optimizer.step()
scheduler.step()
target = [t[epoch] for t in targets]
for t, r in zip(target, result):
assert_allclose(
target,
result,
msg='LR is wrong in epoch {}: expected {}, got {}'.format(
epoch, t, r),
atol=1e-5,
rtol=0)
def test_scheduler_step_count(self):
iteration = 10
scheduler = StepParamScheduler(
self.optimizer, param_name='lr', gamma=0.1, step_size=3)
self.assertEqual(scheduler.last_step, 0)
target = [i + 1 for i in range(iteration)]
step_counts = []
for i in range(iteration):
self.optimizer.step()
scheduler.step()
step_counts.append(scheduler.last_step)
self.assertEqual(step_counts, target)
def test_effective_interval(self):
# check invalid begin end
with self.assertRaisesRegex(ValueError,
'end should be larger than begin'):
StepParamScheduler(
self.optimizer,
param_name='lr',
gamma=0.1,
step_size=3,
begin=10,
end=5)
# lr = 0.05 if epoch == 0
# lr = 0.025 if epoch == 1
# lr = 0.03125 if epoch == 2
# lr = 0.0375 if epoch == 3
# lr = 0.04375 if epoch == 4
# lr = 0.005 if epoch > 4
begin = 1
epochs = 10
start_factor = 1.0 / 2
iters = 4
interpolation = [
start_factor + i * (1 - start_factor) / iters for i in range(iters)
]
single_targets = [0.05] * begin + [x * 0.05
for x in interpolation] + [0.05] * (
epochs - iters - begin)
targets = [
single_targets, [x * self.layer2_mult for x in single_targets]
]
scheduler = LinearParamScheduler(
self.optimizer,
param_name='lr',
start_factor=start_factor,
begin=begin,
end=begin + iters + 1)
self._test_scheduler_value(scheduler, targets, epochs)
def test_param_name(self):
with self.assertRaises(KeyError):
StepParamScheduler(
self.optimizer, param_name='invalid_name', step_size=10)
def _test_scheduler_value(self,
schedulers,
targets,
epochs=10,
param_name='lr',
step_kwargs=None):
if isinstance(schedulers, _ParamScheduler):
schedulers = [schedulers]
if step_kwargs is None:
step_kwarg = [{} for _ in range(len(schedulers))]
step_kwargs = [step_kwarg for _ in range(epochs)]
else: # step_kwargs is not None
assert len(step_kwargs) == epochs
assert len(step_kwargs[0]) == len(schedulers)
for epoch in range(epochs):
for param_group, target in zip(self.optimizer.param_groups,
targets):
assert_allclose(
target[epoch],
param_group[param_name],
msg='{} is wrong in epoch {}: expected {}, got {}'.format(
param_name, epoch, target[epoch],
param_group[param_name]),
atol=1e-5,
rtol=0)
[
scheduler.step(**step_kwargs[epoch][i])
for i, scheduler in enumerate(schedulers)
]
def test_step_scheduler(self):
# lr = 0.05 if epoch < 3
# lr = 0.005 if 3 <= epoch < 6
# lr = 0.0005 if 6 <= epoch < 9
# lr = 0.00005 if epoch >=9
epochs = 10
single_targets = [0.05] * 3 + [0.005] * 3 + [0.0005] * 3 + [0.00005
] * 3
targets = [
single_targets, [x * self.layer2_mult for x in single_targets]
]
scheduler = StepParamScheduler(
self.optimizer,
param_name='lr',
gamma=0.1,
step_size=3,
verbose=True)
self._test_scheduler_value(scheduler, targets, epochs)
# momentum = 0.01 if epoch < 2
# momentum = 0.001 if 2 <= epoch < 4
epochs = 4
single_targets = [0.01] * 2 + [0.001] * 2
targets = [
single_targets, [x * self.layer2_mult for x in single_targets]
]
scheduler = StepParamScheduler(
self.optimizer, param_name='momentum', gamma=0.1, step_size=2)
self._test_scheduler_value(
scheduler, targets, epochs, param_name='momentum')
def test_multi_step_scheduler(self):
# lr = 0.05 if epoch < 2
# lr = 0.005 if 2 <= epoch < 5
# lr = 0.0005 if 5 <= epoch < 9
# lr = 0.00005 if epoch >= 9
epochs = 10
single_targets = [0.05] * 2 + [0.005] * 3 + [0.0005] * 4 + [0.00005
] * 3
targets = [
single_targets, [x * self.layer2_mult for x in single_targets]
]
scheduler = MultiStepParamScheduler(
self.optimizer, param_name='lr', gamma=0.1, milestones=[2, 5, 9])
self._test_scheduler_value(scheduler, targets, epochs)
def test_constant_scheduler(self):
# factor should between 0~1
with self.assertRaises(ValueError):
ConstantParamScheduler(self.optimizer, param_name='lr', factor=99)
# lr = 0.025 if epoch < 5
# lr = 0.005 if 5 <= epoch
epochs = 10
single_targets = [0.025] * 4 + [0.05] * 6
targets = [
single_targets, [x * self.layer2_mult for x in single_targets]
]
scheduler = ConstantParamScheduler(
self.optimizer, param_name='lr', factor=1.0 / 2, end=5)
self._test_scheduler_value(scheduler, targets, epochs)
def test_linear_scheduler(self):
with self.assertRaises(ValueError):
LinearParamScheduler(
self.optimizer, param_name='lr', start_factor=10, end=900)
with self.assertRaises(ValueError):
LinearParamScheduler(
self.optimizer, param_name='lr', start_factor=-1, end=900)
with self.assertRaises(ValueError):
LinearParamScheduler(
self.optimizer, param_name='lr', end_factor=1.001, end=900)
with self.assertRaises(ValueError):
LinearParamScheduler(
self.optimizer, param_name='lr', end_factor=-0.00001, end=900)
# lr = 0.025 if epoch == 0
# lr = 0.03125 if epoch == 1
# lr = 0.0375 if epoch == 2
# lr = 0.04375 if epoch == 3
# lr = 0.005 if epoch >= 4
epochs = 10
start_factor = 1.0 / 2
iters = 4
interpolation = [
start_factor + i * (1 - start_factor) / iters for i in range(iters)
]
single_targets = [x * 0.05 for x in interpolation] + [0.05] * (
epochs - iters)
targets = [
single_targets, [x * self.layer2_mult for x in single_targets]
]
scheduler = LinearParamScheduler(
self.optimizer,
param_name='lr',
start_factor=start_factor,
end=iters + 1)
self._test_scheduler_value(scheduler, targets, epochs)
def test_exp_scheduler(self):
epochs = 10
single_targets = [0.05 * (0.9**x) for x in range(epochs)]
targets = [
single_targets, [x * self.layer2_mult for x in single_targets]
]
scheduler = ExponentialParamScheduler(
self.optimizer, param_name='lr', gamma=0.9)
self._test_scheduler_value(scheduler, targets, epochs)
def test_cos_anneal_scheduler(self):
with self.assertRaises(AssertionError):
CosineAnnealingParamScheduler(
self.optimizer,
param_name='lr',
T_max=10,
eta_min=0,
eta_min_ratio=0.1)
epochs = 12
t = 10
eta_min = 5e-3
targets1 = [
eta_min + (0.05 - eta_min) * (1 + math.cos(math.pi * x / t)) / 2
for x in range(epochs)
]
targets2 = [
eta_min + (0.5 - eta_min) * (1 + math.cos(math.pi * x / t)) / 2
for x in range(epochs)
]
targets = [targets1, targets2]
scheduler = CosineAnnealingParamScheduler(
self.optimizer, param_name='lr', T_max=t, eta_min=eta_min)
self._test_scheduler_value(scheduler, targets, epochs)
# Test `eta_min_ratio`
self.setUp()
eta_min_ratio = 1e-3
targets1 = [
0.05 * eta_min_ratio + (0.05 - 0.05 * eta_min_ratio) *
(1 + math.cos(math.pi * x / t)) / 2 for x in range(epochs)
]
targets2 = [
0.5 * eta_min_ratio + (0.5 - 0.5 * eta_min_ratio) *
(1 + math.cos(math.pi * x / t)) / 2 for x in range(epochs)
]
targets = [targets1, targets2]
scheduler = CosineAnnealingParamScheduler(
self.optimizer,
param_name='lr',
T_max=t,
eta_min_ratio=eta_min_ratio)
self._test_scheduler_value(scheduler, targets, epochs)
# Test default `T_max`
scheduler = CosineAnnealingParamScheduler(
self.optimizer, param_name='lr', begin=5, end=100, eta_min=eta_min)
self.assertEqual(scheduler.T_max, 100 - 5)
def test_poly_scheduler(self):
epochs = 10
power = 0.9
min_lr = 0.001
iters = 4
targets_layer1 = [
min_lr + (0.05 - min_lr) * (1 - i / iters)**power
for i in range(iters)
] + [min_lr] * (
epochs - iters)
targets_layer2 = [
min_lr + (0.05 * self.layer2_mult - min_lr) *
(1 - i / iters)**power for i in range(iters)
] + [min_lr] * (
epochs - iters)
targets = [targets_layer1, targets_layer2]
scheduler = PolyParamScheduler(
self.optimizer,
param_name='lr',
power=power,
eta_min=min_lr,
end=iters + 1)
self._test_scheduler_value(scheduler, targets, epochs=10)
def test_cosine_restart_scheduler(self):
with self.assertRaises(AssertionError):
CosineRestartParamScheduler(
self.optimizer,
param_name='lr',
periods=[4, 5],
restart_weights=[1, 0.5],
eta_min=0,
eta_min_ratio=0.1)
with self.assertRaises(AssertionError):
CosineRestartParamScheduler(
self.optimizer,
param_name='lr',
periods=[4, 5],
restart_weights=[1, 0.5, 0.0],
eta_min=0)
single_targets = [
0.05, 0.0426776, 0.025, 0.00732233, 0.025, 0.022612712, 0.01636271,
0.0086372, 0.0023872, 0.0023872
]
targets = [
single_targets, [t * self.layer2_mult for t in single_targets]
]
# Test with non-zero eta-min.
scheduler = CosineRestartParamScheduler(
self.optimizer,
param_name='lr',
periods=[4, 5],
restart_weights=[1, 0.5],
eta_min=0)
self._test_scheduler_value(scheduler, targets, epochs=10)
epochs = 10
t = 10
eta_min = 5e-3
targets1 = [
eta_min + (0.05 - eta_min) * (1 + math.cos(math.pi * x / t)) / 2
for x in range(epochs)
]
targets2 = [
eta_min + (0.5 - eta_min) * (1 + math.cos(math.pi * x / t)) / 2
for x in range(epochs)
]
targets = [targets1, targets2]
scheduler = CosineRestartParamScheduler(
self.optimizer,
param_name='lr',
periods=[t],
restart_weights=[1],
eta_min=eta_min)
self._test_scheduler_value(scheduler, targets, epochs=10)
def test_reduce_on_plateau_scheduler(self):
# inherit _ParamScheduler but not call super().__init__(),
# so some codes need to be retested
# Test error in __init__ method
with self.assertRaises(TypeError):
ReduceOnPlateauParamScheduler('invalid_optimizer', param_name='lr')
with self.assertRaises(ValueError):
ReduceOnPlateauParamScheduler(
self.optimizer, 'lr', begin=10, end=5)
with self.assertRaises(AssertionError):
ReduceOnPlateauParamScheduler(self.optimizer, 'lr', by_epoch=False)
for last_step in (1.5, -2):
with self.assertRaises(AssertionError):
ReduceOnPlateauParamScheduler(
self.optimizer, 'lr', last_step=last_step)
with self.assertRaises(ValueError):
ReduceOnPlateauParamScheduler(self.optimizer, 'lr', factor=2.0)
ReduceOnPlateauParamScheduler(
self.optimizer, 'lr', min_value=[0.1, 0.1])
with self.assertRaises(ValueError):
ReduceOnPlateauParamScheduler(
self.optimizer, 'lr', min_value=[0.1, 0.1, 0.1, 0.1])
with self.assertRaises(ValueError):
ReduceOnPlateauParamScheduler(self.optimizer, 'lr', threshold=-1.0)
with self.assertRaises(ValueError):
ReduceOnPlateauParamScheduler(self.optimizer, 'lr', rule='foo')
with self.assertRaises(ValueError):
ReduceOnPlateauParamScheduler(
self.optimizer, 'lr', threshold_rule='foo')
# Test error in step method
scheduler = ReduceOnPlateauParamScheduler(
self.optimizer, param_name='lr', monitor='loss')
assert scheduler.step() is None
with self.assertRaises(TypeError):
scheduler.step(('foo', 1.0))
metrics = dict(loss_foo=1.0)
with self.assertRaises(KeyError):
scheduler.step(metrics)
# Test scheduler value
def _test_value(epochs, targets, metrics_list, monitor, rule, factor,
patience, threshold, threshold_rule, cooldown,
min_value):
lr = 0.05
momentum = 0.01
weight_decay = 5e-4
scheduler = ReduceOnPlateauParamScheduler(
self.optimizer,
param_name='lr',
monitor=monitor,
rule=rule,
factor=factor,
patience=patience,
threshold=threshold,
threshold_rule=threshold_rule,
cooldown=cooldown,
min_value=min_value,
)
self._test_scheduler_value(
scheduler, targets, epochs=epochs, step_kwargs=metrics_list)
# reset the state of optimizers
self.optimizer = optim.SGD(
[{
'params': self.model.conv1.parameters()
}, {
'params': self.model.conv2.parameters(),
'lr': lr * self.layer2_mult,
'momentum': momentum * self.layer2_mult,
'weight_decay': weight_decay * self.layer2_mult
}],
lr=lr,
momentum=momentum,
weight_decay=weight_decay)
epochs = 10
factor = 0.1
cooldown = 1
patience = 2
# rule(less) and threshold_rule(rel)
rule, threshold_rule = 'less', 'rel'
threshold = 0.01
monitor = 'loss'
metric_values = [10., 9., 8., 7., 6., 6., 6., 6., 6., 6.]
metrics_list = [[dict(metrics={monitor: v})] for v in metric_values]
single_targets = [
0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.005, 0.005
]
targets = [
single_targets, [t * self.layer2_mult for t in single_targets]
]
_test_value(epochs, targets, metrics_list, monitor, rule, factor,
patience, threshold, threshold_rule, cooldown, 0.0)
# rule(less) and threshold_rule(abs)
rule, threshold_rule = 'less', 'abs'
threshold = 0.9
monitor = 'loss'
metric_values = [10., 9., 8., 7., 6., 6., 6., 6., 6., 6.]
metrics_list = [[dict(metrics={monitor: v})] for v in metric_values]
single_targets = [
0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.005, 0.005
]
targets = [
single_targets, [t * self.layer2_mult for t in single_targets]
]
_test_value(epochs, targets, metrics_list, monitor, rule, factor,
patience, threshold, threshold_rule, cooldown, 0.0)
# rule(greater) and threshold_rule(rel)
rule, threshold_rule = 'greater', 'rel'
threshold = 0.01
monitor = 'bbox_mAP'
metric_values = [1., 2., 3., 4., 5., 5., 5., 5., 5., 5.]
metrics_list = [[dict(metrics={monitor: v})] for v in metric_values]
single_targets = [
0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.005, 0.005
]
targets = [
single_targets, [t * self.layer2_mult for t in single_targets]
]
_test_value(epochs, targets, metrics_list, monitor, rule, factor,
patience, threshold, threshold_rule, cooldown, 0.0)
# rule(greater) and threshold_rule(abs)
rule, threshold_rule = 'greater', 'abs'
threshold = 0.9
monitor = 'bbox_mAP'
metric_values = [1., 2., 3., 4., 5., 5., 5., 5., 5., 5.]
metrics_list = [[dict(metrics={monitor: v})] for v in metric_values]
single_targets = [
0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.005, 0.005
]
targets = [
single_targets, [t * self.layer2_mult for t in single_targets]
]
_test_value(epochs, targets, metrics_list, monitor, rule, factor,
patience, threshold, threshold_rule, cooldown, 0.0)
# change min_value
min_value = 0.01
rule, threshold_rule = 'less', 'rel'
threshold = 0.01
monitor = 'loss'
metric_values = [10., 9., 8., 7., 6., 6., 6., 6., 6., 6.]
metrics_list = [[dict(metrics={monitor: v})] for v in metric_values]
single_targets_1 = [
0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, min_value,
min_value
]
single_targets_2 = [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.05, 0.05]
targets = [single_targets_1, single_targets_2]
_test_value(epochs, targets, metrics_list, monitor, rule, factor,
patience, threshold, threshold_rule, cooldown, min_value)
def _check_scheduler_state_dict(self,
construct,
construct2,
epochs=10,
step_kwargs=None):
if step_kwargs is None:
step_kwargs = [{} for _ in range(epochs)]
else: # step_kwargs is not None
assert len(step_kwargs) == epochs
scheduler = construct()
for epoch in range(epochs):
scheduler.optimizer.step()
scheduler.step(**step_kwargs[epoch])
scheduler_copy = construct2()
torch.save(scheduler.state_dict(),
osp.join(self.temp_dir.name, 'tmp.pth'))
state_dict = torch.load(osp.join(self.temp_dir.name, 'tmp.pth'))
scheduler_copy.load_state_dict(state_dict)
for key in scheduler.__dict__.keys():
if key != 'optimizer':
self.assertEqual(scheduler.__dict__[key],
scheduler_copy.__dict__[key])
self.assertEqual(scheduler.get_last_value(),
scheduler_copy.get_last_value())
def test_step_scheduler_state_dict(self):
self._check_scheduler_state_dict(
lambda: StepParamScheduler(
self.optimizer, param_name='lr', gamma=0.1, step_size=3),
lambda: StepParamScheduler(
self.optimizer, param_name='lr', gamma=0.01 / 2, step_size=1))
def test_multi_step_scheduler_state_dict(self):
self._check_scheduler_state_dict(
lambda: MultiStepParamScheduler(
self.optimizer,
param_name='lr',
gamma=0.1,
milestones=[2, 5, 9]), lambda: MultiStepParamScheduler(
self.optimizer,
param_name='lr',
gamma=0.01,
milestones=[1, 4, 6]))
def test_exp_scheduler_state_dict(self):
self._check_scheduler_state_dict(
lambda: ExponentialParamScheduler(
self.optimizer, param_name='lr', gamma=0.1),
lambda: ExponentialParamScheduler(
self.optimizer, param_name='lr', gamma=0.01))
def test_cosine_scheduler_state_dict(self):
epochs = 10
eta_min = 1e-10
self._check_scheduler_state_dict(
lambda: CosineAnnealingParamScheduler(
self.optimizer, param_name='lr', T_max=epochs, eta_min=eta_min
),
lambda: CosineAnnealingParamScheduler(
self.optimizer,
param_name='lr',
T_max=epochs // 2,
eta_min=eta_min / 2),
epochs=epochs)
def test_linear_scheduler_state_dict(self):
epochs = 10
self._check_scheduler_state_dict(
lambda: LinearParamScheduler(
self.optimizer, param_name='lr', start_factor=1 / 3),
lambda: LinearParamScheduler(
self.optimizer,
param_name='lr',
start_factor=0,
end_factor=0.3),
epochs=epochs)
def test_poly_scheduler_state_dict(self):
self._check_scheduler_state_dict(
lambda: PolyParamScheduler(
self.optimizer, param_name='lr', power=0.5, eta_min=0.001),
lambda: PolyParamScheduler(
self.optimizer, param_name='lr', power=0.8, eta_min=0.002),
epochs=10)
def test_cosine_restart_scheduler_state_dict(self):
self._check_scheduler_state_dict(
lambda: CosineRestartParamScheduler(
self.optimizer,
param_name='lr',
periods=[4, 5],
restart_weights=[1, 0.5],
eta_min=0),
lambda: CosineRestartParamScheduler(
self.optimizer,
param_name='lr',
periods=[4, 6],
restart_weights=[1, 0.5],
eta_min=0),
epochs=10)
def test_reduce_on_plateau_scheduler_state_dict(self):
epochs = 10
metrics_list = [dict(metrics=dict(loss=1.0)) for _ in range(epochs)]
self._check_scheduler_state_dict(
lambda: ReduceOnPlateauParamScheduler(
self.optimizer,
param_name='lr',
monitor='loss',
rule='less',
factor=0.01,
patience=5,
threshold=1e-4,
threshold_rule='rel',
cooldown=0,
min_value=0.0,
eps=1e-8),
lambda: ReduceOnPlateauParamScheduler(
self.optimizer,
param_name='lr',
monitor='loss_foo',
rule='greater',
factor=0.05,
patience=10,
threshold=1e-5,
threshold_rule='abs',
cooldown=5,
min_value=0.1,
eps=1e-9),
epochs=epochs,
step_kwargs=metrics_list)
def test_step_scheduler_convert_iterbased(self):
# invalid epoch_length
with self.assertRaises(AssertionError):
scheduler = StepParamScheduler.build_iter_from_epoch(
self.optimizer,
param_name='momentum',
gamma=0.1,
step_size=2,
epoch_length=-1)
# momentum = 0.01 if epoch < 2
# momentum = 0.001 if 2 <= epoch < 4
epochs = 4
epoch_length = 7
single_targets = [0.01] * 2 * epoch_length + [0.001] * 2 * epoch_length
targets = [
single_targets, [x * self.layer2_mult for x in single_targets]
]
scheduler = StepParamScheduler.build_iter_from_epoch(
self.optimizer,
param_name='momentum',
gamma=0.1,
step_size=2,
epoch_length=epoch_length)
self._test_scheduler_value(
scheduler, targets, epochs * epoch_length, param_name='momentum')
def test_multi_step_scheduler_convert_iterbased(self):
# lr = 0.05 if epoch < 2
# lr = 0.005 if 2 <= epoch < 5
# lr = 0.0005 if 5 <= epoch < 9
# lr = 0.00005 if epoch >= 9
epochs = 10
epoch_length = 7
single_targets = [0.05
] * 2 * epoch_length + [0.005] * 3 * epoch_length + [
0.0005
] * 4 * epoch_length + [0.00005] * 3 * epoch_length
targets = [
single_targets, [x * self.layer2_mult for x in single_targets]
]
scheduler = MultiStepParamScheduler.build_iter_from_epoch(
self.optimizer,
param_name='lr',
gamma=0.1,
milestones=[2, 5, 9],
epoch_length=epoch_length)
self._test_scheduler_value(scheduler, targets, epochs * epoch_length)
def test_constant_scheduler_convert_iterbased(self):
# lr = 0.025 if epoch < 5
# lr = 0.005 if 5 <= epoch
epochs = 10
epoch_length = 7
single_targets = [0.025] * (5 * epoch_length -
1) + [0.05] * (5 * epoch_length + 1)
targets = [
single_targets, [x * self.layer2_mult for x in single_targets]
]
scheduler = ConstantParamScheduler.build_iter_from_epoch(
self.optimizer,
param_name='lr',
factor=1.0 / 2,
end=5,
epoch_length=epoch_length)
self._test_scheduler_value(scheduler, targets, epochs * epoch_length)
def test_linear_scheduler_convert_iterbased(self):
epochs = 10
start_factor = 1.0 / 2
end = 5
epoch_length = 11
iters = end * epoch_length - 1
interpolation = [
start_factor + i * (1 - start_factor) / iters for i in range(iters)
]
single_targets = [x * 0.05 for x in interpolation] + [0.05] * (
epochs * epoch_length - iters)
targets = [
single_targets, [x * self.layer2_mult for x in single_targets]
]
scheduler = LinearParamScheduler.build_iter_from_epoch(
self.optimizer,
param_name='lr',
start_factor=start_factor,
end=end,
epoch_length=epoch_length)
self._test_scheduler_value(scheduler, targets, epochs)
def test_exp_scheduler_convert_iterbased(self):
epochs = 10
epoch_length = 7
single_targets = [
0.05 * (0.9**x) for x in range(epochs * epoch_length)
]
targets = [
single_targets, [x * self.layer2_mult for x in single_targets]
]
scheduler = ExponentialParamScheduler.build_iter_from_epoch(
self.optimizer,
param_name='lr',
gamma=0.9,
epoch_length=epoch_length)
self._test_scheduler_value(scheduler, targets, epochs * epoch_length)
def test_cos_anneal_scheduler_convert_iterbased(self):
epochs = 12
t = 10
eta_min = 1e-10
epoch_length = 11
single_targets = [
eta_min + (0.05 - eta_min) *
(1 + math.cos(math.pi * x / t / epoch_length)) / 2
for x in range(epochs * epoch_length)
]
targets = [
single_targets, [x * self.layer2_mult for x in single_targets]
]
scheduler = CosineAnnealingParamScheduler.build_iter_from_epoch(
self.optimizer,
param_name='lr',
T_max=t,
eta_min=eta_min,
epoch_length=epoch_length)
self._test_scheduler_value(scheduler, targets, epochs)
def test_poly_scheduler_convert_iterbased(self):
epochs = 10
power = 0.9
min_lr = 0.001
end = 5
epoch_length = 11
iters = end * epoch_length - 1
targets_layer1 = [
min_lr + (0.05 - min_lr) * (1 - i / iters)**power
for i in range(iters)
] + [min_lr] * (
epochs - iters)
targets_layer2 = [
min_lr + (0.05 * self.layer2_mult - min_lr) *
(1 - i / iters)**power for i in range(iters)
] + [min_lr] * (
epochs - iters)
targets = [targets_layer1, targets_layer2]
scheduler = PolyParamScheduler.build_iter_from_epoch(
self.optimizer,
param_name='lr',
power=power,
eta_min=min_lr,
end=end,
epoch_length=epoch_length)
self._test_scheduler_value(scheduler, targets, epochs=10)
def test_multi_scheduler_without_overlap_linear_multi_step(self):
# use Linear in the first 5 epochs and then use MultiStep
epochs = 12
single_targets = [0.025, 0.03125, 0.0375, 0.04375
] + [0.05] * 4 + [0.005] * 3 + [0.0005] * 1
targets = [
single_targets, [x * self.layer2_mult for x in single_targets]
]
scheduler1 = LinearParamScheduler(
self.optimizer,
param_name='lr',
start_factor=1 / 2,
begin=0,
end=5)
scheduler2 = MultiStepParamScheduler(
self.optimizer,
param_name='lr',
gamma=0.1,
milestones=[3, 6],
begin=5,
end=12)
self._test_scheduler_value([scheduler1, scheduler2], targets, epochs)
def test_multi_scheduler_without_overlap_exp_cosine(self):
# use Exp in the first 5 epochs and then use Cosine
epochs = 10
single_targets1 = [0.05 * (0.9**x) for x in range(5)]
scheduler1 = ExponentialParamScheduler(
self.optimizer, param_name='lr', gamma=0.9, begin=0, end=5)
eta_min = 1e-10
single_targets2 = [
eta_min + (single_targets1[-1] - eta_min) *
(1 + math.cos(math.pi * x / 5)) / 2 for x in range(5)
]
single_targets = single_targets1 + single_targets2
targets = [
single_targets, [x * self.layer2_mult for x in single_targets]
]
scheduler2 = CosineAnnealingParamScheduler(
self.optimizer,
param_name='lr',
T_max=5,
eta_min=eta_min,
begin=5,
end=10)
self._test_scheduler_value([scheduler1, scheduler2], targets, epochs)
def test_multi_scheduler_with_overlap(self):
# use Linear at first 5 epochs together with MultiStep
epochs = 10
single_targets = [0.025, 0.03125, 0.0375, 0.004375
] + [0.005] * 2 + [0.0005] * 3 + [0.00005] * 1
targets = [
single_targets, [x * self.layer2_mult for x in single_targets]
]
scheduler1 = LinearParamScheduler(
self.optimizer,
param_name='lr',
start_factor=1 / 2,
begin=0,
end=5)
scheduler2 = MultiStepParamScheduler(
self.optimizer, param_name='lr', gamma=0.1, milestones=[3, 6, 9])
self._test_scheduler_value([scheduler1, scheduler2], targets, epochs)
def test_multi_scheduler_with_gap(self):
# use Exp in the first 5 epochs and the last 5 epochs use Cosine
# no scheduler in the middle 5 epochs
epochs = 15
single_targets1 = [0.05 * (0.9**x) for x in range(5)]
scheduler1 = ExponentialParamScheduler(
self.optimizer, param_name='lr', gamma=0.9, begin=0, end=5)
eta_min = 1e-10
single_targets2 = [
eta_min + (single_targets1[-1] - eta_min) *
(1 + math.cos(math.pi * x / 5)) / 2 for x in range(5)
]
single_targets = single_targets1 + [single_targets1[-1]
] * 5 + single_targets2
targets = [
single_targets, [x * self.layer2_mult for x in single_targets]
]
scheduler2 = CosineAnnealingParamScheduler(
self.optimizer,
param_name='lr',
T_max=5,
eta_min=eta_min,
begin=10,
end=15)
self._test_scheduler_value([scheduler1, scheduler2], targets, epochs)
def test_onecycle_scheduler(self):
# test invalid total steps
with self.assertRaises(ValueError):
OneCycleParamScheduler(
self.optimizer, param_name='lr', total_steps=-1)
# test invalid pct_start
with self.assertRaises(ValueError):
OneCycleParamScheduler(
self.optimizer, param_name='lr', total_steps=10, pct_start=-1)
# test invalid anneal_strategy
with self.assertRaises(ValueError):
OneCycleParamScheduler(
self.optimizer,
param_name='lr',
total_steps=10,
anneal_strategy='a')
class TestParameterSchedulerOptimWrapper(TestParameterScheduler):
def setUp(self):
super().setUp()
self.optimizer = OptimWrapper(optimizer=self.optimizer)