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Zero
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import numpy as np
import torch
import torch.optim as optim
def build_LR_scheduler(
optimizer, scheduler_name, lr_decay_ratio, max_epochs, start_epoch=0
):
# print("-LR scheduler:%s"%scheduler_name)
if scheduler_name == "LambdaLR":
decay_ratio = lr_decay_ratio
decay_epochs = max_epochs
def polynomial_decay(epoch):
return (
1 + (decay_ratio - 1) * ((epoch + start_epoch) / decay_epochs)
if (epoch + start_epoch) < decay_epochs
else decay_ratio
)
lr_scheduler = torch.optim.lr_scheduler.LambdaLR(
optimizer, lr_lambda=polynomial_decay
)
elif scheduler_name == "CosineAnnealingLR":
last_epoch = -1 if start_epoch == 0 else start_epoch
lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer, T_max=max_epochs, last_epoch=last_epoch
)
elif scheduler_name == "ReduceLROnPlateau":
lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
optimizer, mode="min", factor=0.5, threshold=0.01, patience=5
)
else:
raise NotImplementedError
return lr_scheduler
class LambdaLRScheduler:
# target: torch.optim.lr_scheduler.LambdaLR
def __init__(self, start_step, final_decay_ratio, decay_steps):
self.final_decay_ratio = final_decay_ratio
self.decay_steps = decay_steps
self.start_step = start_step
def schedule(self, step):
if step + self.start_step < self.decay_steps:
return 1.0 + (self.final_decay_ratio - 1) * (
(step + self.start_step) / self.decay_steps
)
else:
return self.final_decay_ratio
def __call__(self, step):
return self.scheduler(step)
class CosineAnnealingLRScheduler:
# target: torch.optim.lr_scheduler.CosineAnnealingLR
def __init__(self, start_step, decay_steps):
self.decay_steps = decay_steps
self.start_step = start_step
def __call__(self, step):
pass
class LambdaWarmUpCosineScheduler:
"""
note: use with a base_lr of 1.0
"""
def __init__(
self,
warm_up_steps,
lr_min,
lr_max,
lr_start,
max_decay_steps,
verbosity_interval=0,
):
self.lr_warm_up_steps = warm_up_steps
self.lr_start = lr_start
self.lr_min = lr_min
self.lr_max = lr_max
self.lr_max_decay_steps = max_decay_steps
self.last_lr = 0.0
self.verbosity_interval = verbosity_interval
def schedule(self, n, **kwargs):
if self.verbosity_interval > 0:
if n % self.verbosity_interval == 0:
print(f"current step: {n}, recent lr-multiplier: {self.last_lr}")
if n < self.lr_warm_up_steps:
lr = (
self.lr_max - self.lr_start
) / self.lr_warm_up_steps * n + self.lr_start
self.last_lr = lr
return lr
else:
t = (n - self.lr_warm_up_steps) / (
self.lr_max_decay_steps - self.lr_warm_up_steps
)
t = min(t, 1.0)
lr = self.lr_min + 0.5 * (self.lr_max - self.lr_min) * (
1 + np.cos(t * np.pi)
)
self.last_lr = lr
return lr
def __call__(self, n, **kwargs):
return self.schedule(n, **kwargs)
class LambdaWarmUpCosineScheduler2:
"""
supports repeated iterations, configurable via lists
note: use with a base_lr of 1.0.
"""
def __init__(
self, warm_up_steps, f_min, f_max, f_start, cycle_lengths, verbosity_interval=0
):
assert (
len(warm_up_steps)
== len(f_min)
== len(f_max)
== len(f_start)
== len(cycle_lengths)
)
self.lr_warm_up_steps = warm_up_steps
self.f_start = f_start
self.f_min = f_min
self.f_max = f_max
self.cycle_lengths = cycle_lengths
self.cum_cycles = np.cumsum([0] + list(self.cycle_lengths))
self.last_f = 0.0
self.verbosity_interval = verbosity_interval
def find_in_interval(self, n):
interval = 0
for cl in self.cum_cycles[1:]:
if n <= cl:
return interval
interval += 1
def schedule(self, n, **kwargs):
cycle = self.find_in_interval(n)
n = n - self.cum_cycles[cycle]
if self.verbosity_interval > 0:
if n % self.verbosity_interval == 0:
print(
f"current step: {n}, recent lr-multiplier: {self.last_f}, "
f"current cycle {cycle}"
)
if n < self.lr_warm_up_steps[cycle]:
f = (self.f_max[cycle] - self.f_start[cycle]) / self.lr_warm_up_steps[
cycle
] * n + self.f_start[cycle]
self.last_f = f
return f
else:
t = (n - self.lr_warm_up_steps[cycle]) / (
self.cycle_lengths[cycle] - self.lr_warm_up_steps[cycle]
)
t = min(t, 1.0)
f = self.f_min[cycle] + 0.5 * (self.f_max[cycle] - self.f_min[cycle]) * (
1 + np.cos(t * np.pi)
)
self.last_f = f
return f
def __call__(self, n, **kwargs):
return self.schedule(n, **kwargs)
class LambdaLinearScheduler(LambdaWarmUpCosineScheduler2):
def schedule(self, n, **kwargs):
cycle = self.find_in_interval(n)
n = n - self.cum_cycles[cycle]
if self.verbosity_interval > 0:
if n % self.verbosity_interval == 0:
print(
f"current step: {n}, recent lr-multiplier: {self.last_f}, "
f"current cycle {cycle}"
)
if n < self.lr_warm_up_steps[cycle]:
f = (self.f_max[cycle] - self.f_start[cycle]) / self.lr_warm_up_steps[
cycle
] * n + self.f_start[cycle]
self.last_f = f
return f
else:
f = self.f_min[cycle] + (self.f_max[cycle] - self.f_min[cycle]) * (
self.cycle_lengths[cycle] - n
) / (self.cycle_lengths[cycle])
self.last_f = f
return f
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