/
test_lr_scheduler.py
860 lines (770 loc) · 33.3 KB
/
test_lr_scheduler.py
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# Copyright (c) 2016 PaddlePaddle 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.
from __future__ import print_function
import copy
import math
import numpy as np
import unittest
import paddle
import paddle.fluid as fluid
import paddle.fluid.layers as layers
import paddle.fluid.framework as framework
import paddle.fluid.core as core
def reduce_lr_on_plateau(decay_rate, threshold, cooldown, patience, m, n, loss,
var_list):
def is_better(current, best, m, n):
if m == 'min' and n == 'rel':
return current < best - best * threshold
elif m == 'min' and n == 'abs':
return current < best - threshold
elif m == 'max' and n == 'rel':
return current > best + best * threshold
else: # mode == 'max' and epsilon_mode == 'abs':
return current > best + threshold
if var_list[2] > 0:
var_list[2] -= 1
return var_list[1]
if is_better(loss, var_list[0], m, n):
var_list[0] = loss
var_list[3] = 0
else:
var_list[3] += 1
if var_list[3] > patience:
var_list[2] = cooldown
var_list[3] = 0
new_lr = var_list[1] * decay_rate
var_list[1] = new_lr if var_list[1] - new_lr > 1e-8 else var_list[1]
return var_list[1]
class TestReduceOnPlateauDecay(object):
def test_ReduceLR(self):
# the decay rate must be less than 1.0
with self.assertRaises(ValueError):
paddle.optimizer.lr.ReduceOnPlateau(learning_rate=1.0, factor=2.0)
# the mode must be "min" or "max"
with self.assertRaises(ValueError):
paddle.optimizer.lr.ReduceOnPlateau(learning_rate=1.0, mode="test")
# the threshold_mode must be "rel" or "abs"
with self.assertRaises(ValueError):
paddle.optimizer.lr.ReduceOnPlateau(learning_rate=1.0,
threshold_mode="test")
with self.assertRaises(TypeError):
paddle.optimizer.lr.ReduceOnPlateau(learning_rate="test")
with self.assertRaises(TypeError):
paddle.optimizer.lr.ReduceOnPlateau(learning_rate=0.5).step("test")
places = [paddle.CPUPlace()]
if core.is_compiled_with_cuda():
places.append(paddle.CUDAPlace(0))
for place in places:
for m, n in zip(['min', 'max', 'min', 'max'],
['rel', 'rel', 'abs', 'abs']):
kwargs = {
'learning_rate': 1.0,
'mode': m,
'factor': 0.5,
'patience': 3,
'threshold': 1e-4,
'threshold_mode': n,
'cooldown': 1,
'min_lr': 0,
'epsilon': 1e-8,
'verbose': False,
}
paddle.enable_static()
self._test_static(place, kwargs)
paddle.disable_static(place)
self._test_dygraph(place, kwargs)
paddle.enable_static()
def _test_static(self, place, kwargs):
paddle.enable_static()
best = float("-10000") if kwargs['mode'] == "max" else float("10000")
current_lr = 1.0
cooldown_counter = 0
num_bad_epochs = 0
var_list = [best, current_lr, cooldown_counter, num_bad_epochs]
main_prog = paddle.static.Program()
start_prog = paddle.static.Program()
with paddle.static.program_guard(main_prog, start_prog):
x = fluid.layers.create_global_var([1],
1,
'float32',
persistable=True)
paddle.increment(x)
loss = paddle.sin(x)
scheduler = paddle.optimizer.lr.ReduceOnPlateau(**kwargs)
adam = paddle.optimizer.Adam(learning_rate=scheduler)
adam.minimize(loss)
lr_var = adam._global_learning_rate()
test_prog = main_prog.clone()
exe = paddle.static.Executor(place)
exe.run(start_prog)
for epoch in range(20):
for batch_id in range(1):
out, actual_lr = exe.run(main_prog,
fetch_list=[loss.name, lr_var.name])
expected_lr = reduce_lr_on_plateau(
kwargs['factor'], kwargs['threshold'], kwargs['cooldown'],
kwargs['patience'], kwargs['mode'],
kwargs['threshold_mode'], out[0], var_list)
scheduler.step(out[0])
actual_lr = scheduler()
self.assertEqual(actual_lr, np.array(expected_lr))
for epoch in range(10):
for batch_id in range(1):
out, actual_lr = exe.run(test_prog,
fetch_list=[loss.name, lr_var.name])
expected_lr = reduce_lr_on_plateau(
kwargs['factor'], kwargs['threshold'], kwargs['cooldown'],
kwargs['patience'], kwargs['mode'],
kwargs['threshold_mode'], out[0], var_list)
scheduler.step(out[0])
actual_lr = scheduler()
self.assertEqual(actual_lr, np.array(expected_lr))
def _test_dygraph(self, place, kwargs):
paddle.disable_static(place)
best = float("-10000") if kwargs['mode'] == "max" else float("10000")
current_lr = 1.0
cooldown_counter = 0
num_bad_epochs = 0
var_list = [best, current_lr, cooldown_counter, num_bad_epochs]
linear = paddle.nn.Linear(10, 10)
scheduler = paddle.optimizer.lr.ReduceOnPlateau(**kwargs)
adam = paddle.optimizer.Adam(learning_rate=scheduler,
parameters=linear.parameters())
for epoch in range(20):
for batch_id in range(1):
x = paddle.to_tensor(epoch).astype('float32')
loss = paddle.sin(x)
loss.backward()
adam.step()
adam.clear_grad()
scheduler.step(loss)
# get lr from paddle
current_lr = adam.get_lr()
# get lr form python
expected_lr = reduce_lr_on_plateau(
kwargs['factor'], kwargs['threshold'], kwargs['cooldown'],
kwargs['patience'], kwargs['mode'], kwargs['threshold_mode'],
loss, var_list)
self.assertEqual(current_lr, expected_lr)
state_dict = adam.state_dict()
scheduler1 = paddle.optimizer.lr.ReduceOnPlateau(**kwargs)
adam1 = paddle.optimizer.Adam(learning_rate=scheduler1,
parameters=linear.parameters())
adam1.set_state_dict(state_dict)
self.assertEqual(scheduler.cooldown_counter,
scheduler1.cooldown_counter)
self.assertEqual(scheduler.best.numpy()[0], scheduler1.best)
self.assertEqual(scheduler.num_bad_epochs, scheduler1.num_bad_epochs)
self.assertEqual(scheduler.last_epoch, scheduler1.last_epoch)
self.assertEqual(scheduler.last_lr, scheduler1.last_lr)
def noam_lr(epoch_num, d_model, warmup_steps, learning_rate=1.0, verbose=False):
if epoch_num == 0:
a = 1
else:
a = math.pow(epoch_num, -0.5)
b = math.pow(warmup_steps, -1.5) * epoch_num
return learning_rate * math.pow(d_model, -0.5) * min(a, b)
def lambda_lr(epoch_num, learning_rate, lr_lambda, verbose=False):
return learning_rate * lr_lambda(epoch_num)
def multiplicative_lr(epoch_num, learning_rate, lr_lambda, verbose=False):
latest_lr = learning_rate
for i in range(epoch_num):
latest_lr = latest_lr * lr_lambda(i + 1)
return latest_lr
def piecewise_lr(epoch_num, boundaries, values, verbose=False):
assert len(boundaries) + 1 == len(values)
for i in range(len(boundaries)):
if epoch_num < boundaries[i]:
return values[i]
return values[len(values) - 1]
def exponential_lr(epoch_num, learning_rate, gamma, verbose=False):
return learning_rate * gamma**epoch_num
def natural_exp_lr(epoch_num, learning_rate, gamma, verbose=False):
return learning_rate * math.exp(-1 * gamma * epoch_num)
def inverse_time_lr(epoch_num, learning_rate, gamma, verbose=False):
return learning_rate / (1 + gamma * epoch_num)
def polynomial_lr(epoch_num,
learning_rate,
decay_steps,
end_lr=0.0001,
power=1.0,
cycle=False,
verbose=False):
if cycle:
div = math.ceil(epoch_num / float(decay_steps))
if epoch_num == 0:
div = 1
decay_steps = decay_steps * div
else:
epoch_num = min(epoch_num, decay_steps)
return (learning_rate - end_lr) * (
(1 - float(epoch_num) / float(decay_steps))**power) + end_lr
def get_lr(self):
if self.last_epoch == 0:
return self.base_lr
elif (self.last_epoch - 1 - self.T_max) % (2 * self.T_max) == 0:
return self.last_lr + (self.base_lr - self.eta_min) * (
1 - math.cos(math.pi / self.T_max)) / 2
return (1 + math.cos(math.pi * self.last_epoch / self.T_max)) / (
1 + math.cos(math.pi * (self.last_epoch - 1) / self.T_max)) * (
self.last_lr - self.eta_min) + self.eta_min
cosine_annealing_lr_current = None
def cosine_annealing_lr(epoch_num,
learning_rate,
T_max,
eta_min=0,
verbose=False):
global cosine_annealing_lr_current
if epoch_num == 0:
cosine_annealing_lr_current = learning_rate
elif (epoch_num - 1 - T_max) % (2 * T_max) == 0:
cosine_annealing_lr_current = cosine_annealing_lr_current + (
learning_rate - eta_min) * (1 -
math.cos(math.pi / float(T_max))) / 2
else:
cosine_annealing_lr_current = (
1 + math.cos(math.pi * epoch_num / float(T_max))) / (
1 + math.cos(math.pi * (epoch_num - 1) / float(T_max))) * (
cosine_annealing_lr_current - eta_min) + eta_min
return cosine_annealing_lr_current
def linear_warmup_lr(epoch_num,
learning_rate,
warmup_steps,
start_lr,
end_lr,
verbose=False):
tmp = epoch_num - warmup_steps
if tmp < 0:
return start_lr + (end_lr - start_lr) * (float(epoch_num) /
float(warmup_steps))
elif paddle.in_dynamic_mode():
if tmp < 3:
return 0.5
elif tmp < 6:
return 0.2
else:
return 0.1
else:
return 0.5
def multi_step_lr(epoch_num,
learning_rate,
milestones,
gamma=0.1,
verbose=False):
for i in range(len(milestones)):
if epoch_num < milestones[i]:
return learning_rate * (gamma**i)
return learning_rate * (gamma**len(milestones))
def step_lr(epoch_num, learning_rate, step_size, gamma=0.1, verbose=False):
return learning_rate * math.pow(gamma, epoch_num // step_size)
def one_cycle_lr(epoch_num,
max_learning_rate,
total_steps,
divide_factor=25,
end_learning_rate=0.0001,
phase_pct=0.3,
anneal_strategy='cos',
three_phase=False,
verbose=False):
initial_lr = max_learning_rate / divide_factor
if three_phase:
_end_steps = [
float(phase_pct * total_steps) - 1,
float(2 * phase_pct * total_steps) - 2, total_steps - 1
]
_schedule_phases = [
{
'start_lr': initial_lr,
'end_lr': max_learning_rate,
},
{
'start_lr': max_learning_rate,
'end_lr': initial_lr,
},
{
'start_lr': initial_lr,
'end_lr': end_learning_rate,
},
]
else:
_end_steps = [float(phase_pct * total_steps) - 1, total_steps - 1]
_schedule_phases = [
{
'start_lr': initial_lr,
'end_lr': max_learning_rate,
},
{
'start_lr': max_learning_rate,
'end_lr': end_learning_rate,
},
]
if anneal_strategy == 'cos':
def anneal_func(start, end, pct):
cos_out = math.cos(math.pi * pct) + 1
return end + (start - end) / 2.0 * cos_out
else:
def anneal_func(start, end, pct):
return (end - start) * pct + start
start_step = 0
for i, phase in enumerate(_schedule_phases):
end_step = _end_steps[i]
if epoch_num <= end_step or i == len(_schedule_phases) - 1:
pct = (epoch_num - start_step) / (end_step - start_step)
computed_lr = anneal_func(phase['start_lr'], phase['end_lr'], pct)
break
start_step = end_step
return computed_lr
def cyclic_lr(epoch_num,
base_learning_rate,
max_learning_rate,
step_size_up,
step_size_down,
mode,
exp_gamma=0.1,
scale_fn=None,
scale_mode='cycle',
verbose=False):
total_steps = step_size_up + step_size_down
step_ratio = step_size_up / total_steps
def triangular(x):
return 1.
def triangular2(x):
return 1 / (2.**(x - 1))
def exp_range(x):
return exp_gamma**x
if scale_fn is None:
if mode == 'triangular':
scale_fn = triangular
scale_mode = 'cycle'
elif mode == 'triangular2':
scale_fn = triangular2
scale_mode = 'cycle'
elif mode == 'exp_range':
scale_fn = exp_range
scale_mode = 'iterations'
cycle = math.floor(1 + epoch_num / total_steps)
iterations = epoch_num
x = 1. + epoch_num / total_steps - cycle
if x <= step_ratio:
scale_factor = x / step_ratio
else:
scale_factor = (x - 1) / (step_ratio - 1)
base_height = (max_learning_rate - base_learning_rate) * scale_factor
return base_learning_rate + base_height * scale_fn(eval(scale_mode))
class TestLRScheduler(unittest.TestCase):
def _test_static(self, python_func, paddle_api, kwarg, place):
scheduler = paddle_api(**kwarg)
adam = paddle.optimizer.Adam(learning_rate=scheduler)
main_prog = paddle.static.Program()
start_prog = paddle.static.Program()
with paddle.static.program_guard(main_prog, start_prog):
x = paddle.static.data(name='x', shape=[3, 4, 5])
loss = paddle.mean(x)
adam.minimize(loss)
lr_var = adam._global_learning_rate()
test_prog = main_prog.clone()
num = 0
exe = paddle.static.Executor(place)
exe.run(start_prog)
for epoch in range(5):
for batch_id in range(2):
out = exe.run(
main_prog,
feed={'x': np.random.randn(3, 4, 5).astype('float32')},
fetch_list=lr_var.name)
self.assertEqual(out, np.array(python_func(num, **kwarg)))
scheduler.step()
num += 1
for epoch in range(5):
for batch_id in range(2):
out = exe.run(
test_prog,
feed={'x': np.random.randn(3, 4, 5).astype('float32')},
fetch_list=lr_var.name)
self.assertEqual(out, np.array(python_func(num, **kwarg)))
scheduler.step()
num += 1
if isinstance(place, paddle.CPUPlace):
compiled_train_prog = paddle.static.CompiledProgram(
main_prog).with_data_parallel(loss_name=loss.name,
places=fluid.cpu_places(4))
for epoch in range(5):
python_result = python_func(num, **kwarg)
for batch_id in range(2):
_ = exe.run(
compiled_train_prog,
feed={'x': np.random.randn(12, 4, 5).astype('float32')},
fetch_list=lr_var.name)
scopes = compiled_train_prog._executor.local_scopes()
out = np.array(scopes[0].var(lr_var.name).get_tensor())
self.assertEqual(out, np.array(python_result))
out = np.array(scopes[1].var(lr_var.name).get_tensor())
self.assertEqual(out, np.array(python_result))
out = np.array(scopes[2].var(lr_var.name).get_tensor())
self.assertEqual(out, np.array(python_result))
out = np.array(scopes[3].var(lr_var.name).get_tensor())
self.assertEqual(out, np.array(python_result))
scheduler.step()
num += 1
compiled_test_prog = paddle.static.CompiledProgram(
test_prog).with_data_parallel(
loss_name=loss.name,
share_vars_from=compiled_train_prog,
places=fluid.cpu_places(4))
for epoch in range(5):
python_result = python_func(num, **kwarg)
for batch_id in range(2):
_ = exe.run(
compiled_test_prog,
feed={'x': np.random.randn(12, 4, 5).astype('float32')},
fetch_list=lr_var.name)
scopes = compiled_test_prog._executor.local_scopes()
out = np.array(scopes[0].var(lr_var.name).get_tensor())
self.assertEqual(out, np.array(python_result))
out = np.array(scopes[1].var(lr_var.name).get_tensor())
self.assertEqual(out, np.array(python_result))
out = np.array(scopes[2].var(lr_var.name).get_tensor())
self.assertEqual(out, np.array(python_result))
out = np.array(scopes[3].var(lr_var.name).get_tensor())
self.assertEqual(out, np.array(python_result))
scheduler.step()
num += 1
def _test_dygraph(self, python_func, paddle_api, kwarg, place):
paddle.disable_static(place)
x = np.random.uniform(-1, 1, [10, 10]).astype("float32")
linear = paddle.nn.Linear(10, 10)
if paddle_api.__name__ == "LinearWarmup":
kwarg['learning_rate'] = paddle.optimizer.lr.PiecewiseDecay(
[3, 6], [0.5, 0.2, 0.1])
scheduler = paddle_api(**kwarg)
adam = paddle.optimizer.Adam(learning_rate=scheduler,
parameters=linear.parameters())
for epoch in range(20):
for batch_id in range(2):
x = paddle.to_tensor(x)
out = linear(x)
loss = paddle.mean(out)
loss.backward()
adam.step()
adam.clear_grad()
current_lr = adam.get_lr()
expected_lr = python_func(epoch, **kwarg)
if paddle_api.__name__ == "CosineAnnealingDecay":
self.assertAlmostEqual(current_lr, expected_lr)
scheduler.step(epoch + 1)
elif paddle_api.__name__ == "LinearWarmup":
self.assertAlmostEqual(current_lr, expected_lr)
state_dict = adam.state_dict()
scheduler1 = paddle.optimizer.lr.LinearWarmup(**kwarg)
adam1 = paddle.optimizer.Adam(learning_rate=scheduler1,
parameters=linear.parameters())
adam1.set_state_dict(state_dict)
self.assertEqual(scheduler.last_epoch, scheduler1.last_epoch)
self.assertEqual(scheduler.last_lr, scheduler1.last_lr)
self.assertEqual(scheduler.learning_rate.last_lr,
scheduler1.learning_rate.last_lr)
self.assertEqual(scheduler.learning_rate.last_epoch,
scheduler1.learning_rate.last_epoch)
scheduler.step()
else:
self.assertEqual(current_lr, expected_lr)
scheduler.step()
def test_scheduler(self):
with self.assertRaises(NotImplementedError):
paddle.optimizer.lr.LRScheduler().step()
with self.assertRaises(TypeError):
paddle.optimizer.lr.MultiStepDecay(learning_rate="test",
milestones=[1, 2, 3])
with self.assertRaises(TypeError):
paddle.optimizer.lr.MultiStepDecay(learning_rate=0.5,
milestones='test')
with self.assertRaises(ValueError):
paddle.optimizer.lr.MultiStepDecay(learning_rate=0.5,
milestones=[3, 2, 1])
with self.assertRaises(ValueError):
paddle.optimizer.lr.MultiStepDecay(learning_rate=0.5,
milestones=[1, 2, 3],
gamma=2)
# check type of max_learning_rate
with self.assertRaises(TypeError):
paddle.optimizer.lr.OneCycleLR(max_learning_rate='test',
total_steps=20)
# check value of max_learning_rate
with self.assertRaises(ValueError):
paddle.optimizer.lr.OneCycleLR(max_learning_rate=-1.5,
total_steps=20)
# check type of end_learning_rate
with self.assertRaises(TypeError):
paddle.optimizer.lr.OneCycleLR(max_learning_rate=0.1,
total_steps=20,
end_learning_rate='test')
# check value of end_learning_rate
with self.assertRaises(ValueError):
paddle.optimizer.lr.OneCycleLR(max_learning_rate=0.1,
total_steps=20,
end_learning_rate=-1)
# check type of total_steps
with self.assertRaises(TypeError):
paddle.optimizer.lr.OneCycleLR(max_learning_rate=0.1,
total_steps='test')
# check value of total_steps
with self.assertRaises(ValueError):
paddle.optimizer.lr.OneCycleLR(max_learning_rate=0.1,
total_steps=-10)
# check value of anneal_strategy
with self.assertRaises(ValueError):
paddle.optimizer.lr.OneCycleLR(max_learning_rate=0.1,
total_steps=20,
anneal_strategy='test')
# check value of phase_pct when three_phase is True
with self.assertRaises(ValueError):
paddle.optimizer.lr.OneCycleLR(max_learning_rate=0.1,
total_steps=20,
phase_pct=0.6,
three_phase=True)
# check type of max_learning_rate
with self.assertRaises(TypeError):
paddle.optimizer.lr.CyclicLR(base_learning_rate=0.5,
max_learning_rate='test',
step_size_up=10)
# check value of max_learning_rate
with self.assertRaises(ValueError):
paddle.optimizer.lr.CyclicLR(base_learning_rate=0.5,
max_learning_rate=-1,
step_size_up=10)
# check type of step_size_up
with self.assertRaises(TypeError):
paddle.optimizer.lr.CyclicLR(base_learning_rate=0.5,
max_learning_rate=1.0,
step_size_up='test')
# check value of step_size_up
with self.assertRaises(ValueError):
paddle.optimizer.lr.CyclicLR(base_learning_rate=0.5,
max_learning_rate=1.0,
step_size_up=-1)
# check type of step_size_down
with self.assertRaises(TypeError):
paddle.optimizer.lr.CyclicLR(base_learning_rate=0.5,
max_learning_rate=1.0,
step_size_up=500,
step_size_down='test')
# check type of step_size_down
with self.assertRaises(ValueError):
paddle.optimizer.lr.CyclicLR(base_learning_rate=0.5,
max_learning_rate=1.0,
step_size_up=500,
step_size_down=-1)
# check value of mode
with self.assertRaises(ValueError):
paddle.optimizer.lr.CyclicLR(base_learning_rate=0.5,
max_learning_rate=1.0,
step_size_up=500,
step_size_down=500,
mode='test')
# check type value of scale_mode
with self.assertRaises(ValueError):
paddle.optimizer.lr.CyclicLR(base_learning_rate=0.5,
max_learning_rate=1.0,
step_size_up=500,
step_size_down=-1,
scale_mode='test')
func_api_kwargs = [
(noam_lr, paddle.optimizer.lr.NoamDecay, {
"d_model": 0.01,
"warmup_steps": 100,
"verbose": False
}),
(piecewise_lr, paddle.optimizer.lr.PiecewiseDecay, {
"boundaries": [3, 6, 9, 15, 20],
"values": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6],
"verbose": False
}),
(natural_exp_lr, paddle.optimizer.lr.NaturalExpDecay, {
"learning_rate": 0.5,
"gamma": 0.1,
"verbose": True
}),
(inverse_time_lr, paddle.optimizer.lr.InverseTimeDecay, {
"learning_rate": 0.5,
"gamma": 0.1,
"verbose": False
}),
(polynomial_lr, paddle.optimizer.lr.PolynomialDecay, {
"learning_rate": 0.5,
"decay_steps": 20,
"end_lr": 0,
"power": 1.0,
"cycle": False
}),
(polynomial_lr, paddle.optimizer.lr.PolynomialDecay, {
"learning_rate": 0.5,
"decay_steps": 20,
"end_lr": 0,
"power": 1.0,
"cycle": True,
"verbose": False
}),
(linear_warmup_lr, paddle.optimizer.lr.LinearWarmup, {
'learning_rate': 0.5,
'warmup_steps': 10,
'start_lr': 0,
'end_lr': 0.5
}),
(exponential_lr, paddle.optimizer.lr.ExponentialDecay, {
"learning_rate": 0.5,
"gamma": 0.9,
"verbose": False
}),
(multi_step_lr, paddle.optimizer.lr.MultiStepDecay, {
"learning_rate": 0.5,
"milestones": [3, 6, 9, 15, 20],
"gamma": 0.8
}),
(step_lr, paddle.optimizer.lr.StepDecay, {
"learning_rate": 0.5,
"step_size": 2,
"gamma": 0.8,
"verbose": False
}),
(lambda_lr, paddle.optimizer.lr.LambdaDecay, {
"learning_rate": 0.5,
"lr_lambda": lambda x: 0.95**x,
"verbose": True
}),
(multiplicative_lr, paddle.optimizer.lr.MultiplicativeDecay, {
"learning_rate": 0.5,
"lr_lambda": lambda x: 0.95,
"verbose": True
}),
(cosine_annealing_lr, paddle.optimizer.lr.CosineAnnealingDecay, {
"learning_rate": 0.5,
"T_max": 10,
"verbose": False
}),
(one_cycle_lr, paddle.optimizer.lr.OneCycleLR, {
"max_learning_rate": 0.1,
"total_steps": 20,
"divide_factor": 5,
"end_learning_rate": 0.0001,
"anneal_strategy": 'cos',
"phase_pct": 0.3,
"three_phase": False,
}),
(one_cycle_lr, paddle.optimizer.lr.OneCycleLR, {
"max_learning_rate": 0.5,
"total_steps": 20,
"divide_factor": 10,
"end_learning_rate": 0.001,
"anneal_strategy": 'linear',
"phase_pct": 0.4,
"three_phase": False,
}),
(one_cycle_lr, paddle.optimizer.lr.OneCycleLR, {
"max_learning_rate": 1.0,
"total_steps": 20,
"divide_factor": 9,
"end_learning_rate": 0.0001,
"anneal_strategy": 'cos',
"phase_pct": 0.3,
"three_phase": True,
}),
(one_cycle_lr, paddle.optimizer.lr.OneCycleLR, {
"max_learning_rate": 0.3,
"total_steps": 20,
"divide_factor": 25,
"end_learning_rate": 0.0005,
"anneal_strategy": 'linear',
"phase_pct": 0.2,
"three_phase": True,
}),
(cyclic_lr, paddle.optimizer.lr.CyclicLR, {
"base_learning_rate": 0.5,
"max_learning_rate": 1.0,
"step_size_up": 15,
"step_size_down": 5,
"mode": 'triangular',
"exp_gamma": 1.,
"scale_fn": None,
"scale_mode": 'cycle',
"verbose": False
}),
(cyclic_lr, paddle.optimizer.lr.CyclicLR, {
"base_learning_rate": 0.5,
"max_learning_rate": 1.0,
"step_size_up": 15,
"step_size_down": 5,
"mode": 'triangular2',
"exp_gamma": 1.,
"scale_fn": None,
"scale_mode": 'cycle',
"verbose": False
}),
(cyclic_lr, paddle.optimizer.lr.CyclicLR, {
"base_learning_rate": 0.5,
"max_learning_rate": 1.0,
"step_size_up": 15,
"step_size_down": 5,
"mode": 'exp_range',
"exp_gamma": 0.8,
"scale_fn": None,
"scale_mode": 'cycle',
"verbose": False
}),
(cyclic_lr, paddle.optimizer.lr.CyclicLR, {
"base_learning_rate": 0.5,
"max_learning_rate": 1.0,
"step_size_up": 15,
"step_size_down": 5,
"mode": 'exp_range',
"exp_gamma": 1.,
"scale_fn": lambda x: 0.95**x,
"scale_mode": 'cycle',
"verbose": False
}),
(cyclic_lr, paddle.optimizer.lr.CyclicLR, {
"base_learning_rate": 0.5,
"max_learning_rate": 1.0,
"step_size_up": 15,
"step_size_down": 5,
"mode": 'exp_range',
"exp_gamma": 1.,
"scale_fn": lambda x: 0.95,
"scale_mode": 'iterations',
"verbose": False
})
]
for python_func, paddle_api, kwarg in func_api_kwargs:
places = [paddle.CPUPlace()]
if core.is_compiled_with_cuda():
places.append(paddle.CUDAPlace(0))
for place in places:
paddle.enable_static()
self._test_static(python_func, paddle_api, kwarg, place)
paddle.disable_static(place)
self._test_dygraph(python_func, paddle_api, kwarg, place)
paddle.enable_static()
def test_linear_warmp(self):
natural_lr = paddle.optimizer.lr.NaturalExpDecay(learning_rate=0.5,
gamma=0.1)
natural_lr_warmup = paddle.optimizer.lr.LinearWarmup(
learning_rate=natural_lr, warmup_steps=10, start_lr=0.0, end_lr=0.1)
for idx in range(30):
if idx >= 10:
self.assertEqual(natural_lr_warmup.get_lr(),
natural_lr.get_lr())
natural_lr.step()
natural_lr_warmup.step()
if __name__ == '__main__':
paddle.enable_static()
unittest.main()