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import math
from torch import inf
from torch.optim.optimizer import Optimizer
class ReduceLROnPlateauWithWarmup(object):
"""Reduce learning rate when a metric has stopped improving.
Models often benefit from reducing the learning rate by a factor
of 2-10 once learning stagnates. This scheduler reads a metrics
quantity and if no improvement is seen for a 'patience' number
of epochs, the learning rate is reduced.
Args:
optimizer (Optimizer): Wrapped optimizer.
mode (str): One of `min`, `max`. In `min` mode, lr will
be reduced when the quantity monitored has stopped
decreasing; in `max` mode it will be reduced when the
quantity monitored has stopped increasing. Default: 'min'.
factor (float): Factor by which the learning rate will be
reduced. new_lr = lr * factor. Default: 0.1.
patience (int): Number of epochs with no improvement after
which learning rate will be reduced. For example, if
`patience = 2`, then we will ignore the first 2 epochs
with no improvement, and will only decrease the LR after the
3rd epoch if the loss still hasn't improved then.
Default: 10.
threshold (float): Threshold for measuring the new optimum,
to only focus on significant changes. Default: 1e-4.
threshold_mode (str): One of `rel`, `abs`. In `rel` mode,
dynamic_threshold = best * ( 1 + threshold ) in 'max'
mode or best * ( 1 - threshold ) in `min` mode.
In `abs` mode, dynamic_threshold = best + threshold in
`max` mode or best - threshold in `min` mode. Default: 'rel'.
cooldown (int): Number of epochs to wait before resuming
normal operation after lr has been reduced. Default: 0.
min_lr (float or list): A scalar or a list of scalars. A
lower bound on the learning rate of all param groups
or each group respectively. Default: 0.
eps (float): Minimal decay applied to lr. If the difference
between new and old lr is smaller than eps, the update is
ignored. Default: 1e-8.
verbose (bool): If ``True``, prints a message to stdout for
each update. Default: ``False``.
warmup_lr: float or None, the learning rate to be touched after warmup
warmup: int, the number of steps to warmup
"""
def __init__(
self,
optimizer,
mode="min",
factor=0.1,
patience=10,
threshold=1e-4,
threshold_mode="rel",
cooldown=0,
min_lr=0,
eps=1e-8,
verbose=False,
warmup_lr=None,
warmup=0,
):
if factor >= 1.0:
raise ValueError("Factor should be < 1.0.")
self.factor = factor
# Attach optimizer
if not isinstance(optimizer, Optimizer):
raise TypeError("{} is not an Optimizer".format(type(optimizer).__name__))
self.optimizer = optimizer
if isinstance(min_lr, list) or isinstance(min_lr, tuple):
if len(min_lr) != len(optimizer.param_groups):
raise ValueError(
"expected {} min_lrs, got {}".format(
len(optimizer.param_groups), len(min_lr)
)
)
self.min_lrs = list(min_lr)
else:
self.min_lrs = [min_lr] * len(optimizer.param_groups)
self.patience = patience
self.verbose = verbose
self.cooldown = cooldown
self.cooldown_counter = 0
self.mode = mode
self.threshold = threshold
self.threshold_mode = threshold_mode
self.warmup_lr = warmup_lr
self.warmup = warmup
self.best = None
self.num_bad_epochs = None
self.mode_worse = None # the worse value for the chosen mode
self.eps = eps
self.last_epoch = 0
self._init_is_better(
mode=mode, threshold=threshold, threshold_mode=threshold_mode
)
self._reset()
def _prepare_for_warmup(self):
if self.warmup_lr is not None:
if isinstance(self.warmup_lr, (list, tuple)):
if len(self.warmup_lr) != len(self.optimizer.param_groups):
raise ValueError(
"expected {} warmup_lrs, got {}".format(
len(self.optimizer.param_groups), len(self.warmup_lr)
)
)
self.warmup_lrs = list(self.warmup_lr)
else:
self.warmup_lrs = [self.warmup_lr] * len(self.optimizer.param_groups)
else:
self.warmup_lrs = None
if self.warmup > self.last_epoch:
curr_lrs = [group["lr"] for group in self.optimizer.param_groups]
self.warmup_lr_steps = [
max(0, (self.warmup_lrs[i] - curr_lrs[i]) / float(self.warmup))
for i in range(len(curr_lrs))
]
else:
self.warmup_lr_steps = None
def _reset(self):
"""Resets num_bad_epochs counter and cooldown counter."""
self.best = self.mode_worse
self.cooldown_counter = 0
self.num_bad_epochs = 0
def step(self, metrics):
# convert `metrics` to float, in case it's a zero-dim Tensor
current = float(metrics)
epoch = self.last_epoch + 1
self.last_epoch = epoch
if epoch <= self.warmup:
self._increase_lr(epoch)
else:
if self.is_better(current, self.best):
self.best = current
self.num_bad_epochs = 0
else:
self.num_bad_epochs += 1
if self.in_cooldown:
self.cooldown_counter -= 1
self.num_bad_epochs = 0 # ignore any bad epochs in cooldown
if self.num_bad_epochs > self.patience:
self._reduce_lr(epoch)
self.cooldown_counter = self.cooldown
self.num_bad_epochs = 0
self._last_lr = [group["lr"] for group in self.optimizer.param_groups]
def _reduce_lr(self, epoch):
for i, param_group in enumerate(self.optimizer.param_groups):
old_lr = float(param_group["lr"])
new_lr = max(old_lr * self.factor, self.min_lrs[i])
if old_lr - new_lr > self.eps:
param_group["lr"] = new_lr
if self.verbose:
print(
"Epoch {:5d}: reducing learning rate"
" of group {} to {:.4e}.".format(epoch, i, new_lr)
)
def _increase_lr(self, epoch):
# used for warmup
for i, param_group in enumerate(self.optimizer.param_groups):
old_lr = float(param_group["lr"])
new_lr = max(old_lr + self.warmup_lr_steps[i], self.min_lrs[i])
param_group["lr"] = new_lr
if self.verbose:
print(
"Epoch {:5d}: increasing learning rate"
" of group {} to {:.4e}.".format(epoch, i, new_lr)
)
@property
def in_cooldown(self):
return self.cooldown_counter > 0
def is_better(self, a, best):
if self.mode == "min" and self.threshold_mode == "rel":
rel_epsilon = 1.0 - self.threshold
return a < best * rel_epsilon
elif self.mode == "min" and self.threshold_mode == "abs":
return a < best - self.threshold
elif self.mode == "max" and self.threshold_mode == "rel":
rel_epsilon = self.threshold + 1.0
return a > best * rel_epsilon
else: # mode == 'max' and epsilon_mode == 'abs':
return a > best + self.threshold
def _init_is_better(self, mode, threshold, threshold_mode):
if mode not in {"min", "max"}:
raise ValueError("mode " + mode + " is unknown!")
if threshold_mode not in {"rel", "abs"}:
raise ValueError("threshold mode " + threshold_mode + " is unknown!")
if mode == "min":
self.mode_worse = inf
else: # mode == 'max':
self.mode_worse = -inf
self.mode = mode
self.threshold = threshold
self.threshold_mode = threshold_mode
self._prepare_for_warmup()
def state_dict(self):
return {
key: value for key, value in self.__dict__.items() if key != "optimizer"
}
def load_state_dict(self, state_dict):
self.__dict__.update(state_dict)
self._init_is_better(
mode=self.mode, threshold=self.threshold, threshold_mode=self.threshold_mode
)
class CosineAnnealingLRWithWarmup(object):
"""
adjust lr:
args:
warmup_lr: float or None, the learning rate to be touched after warmup
warmup: int, the number of steps to warmup
"""
def __init__(
self,
optimizer,
T_max,
last_epoch=-1,
verbose=False,
min_lr=0,
warmup_lr=None,
warmup=0,
):
self.optimizer = optimizer
self.T_max = T_max
self.last_epoch = last_epoch
self.verbose = verbose
self.warmup_lr = warmup_lr
self.warmup = warmup
if isinstance(min_lr, list) or isinstance(min_lr, tuple):
if len(min_lr) != len(optimizer.param_groups):
raise ValueError(
"expected {} min_lrs, got {}".format(
len(optimizer.param_groups), len(min_lr)
)
)
self.min_lrs = list(min_lr)
else:
self.min_lrs = [min_lr] * len(optimizer.param_groups)
self.max_lrs = [lr for lr in self.min_lrs]
self._prepare_for_warmup()
def step(self):
epoch = self.last_epoch + 1
self.last_epoch = epoch
if epoch <= self.warmup:
self._increase_lr(epoch)
else:
self._reduce_lr(epoch)
def _reduce_lr(self, epoch):
for i, param_group in enumerate(self.optimizer.param_groups):
progress = float(epoch - self.warmup) / float(
max(1, self.T_max - self.warmup)
)
factor = max(0.0, 0.5 * (1.0 + math.cos(math.pi * progress)))
old_lr = float(param_group["lr"])
new_lr = max(self.max_lrs[i] * factor, self.min_lrs[i])
param_group["lr"] = new_lr
if self.verbose:
print(
"Epoch {:5d}: reducing learning rate"
" of group {} to {:.4e}.".format(epoch, i, new_lr)
)
def _increase_lr(self, epoch):
# used for warmup
for i, param_group in enumerate(self.optimizer.param_groups):
old_lr = float(param_group["lr"])
new_lr = old_lr + self.warmup_lr_steps[i]
param_group["lr"] = new_lr
self.max_lrs[i] = max(self.max_lrs[i], new_lr)
if self.verbose:
print(
"Epoch {:5d}: increasing learning rate"
" of group {} to {:.4e}.".format(epoch, i, new_lr)
)
def _prepare_for_warmup(self):
if self.warmup_lr is not None:
if isinstance(self.warmup_lr, (list, tuple)):
if len(self.warmup_lr) != len(self.optimizer.param_groups):
raise ValueError(
"expected {} warmup_lrs, got {}".format(
len(self.optimizer.param_groups), len(self.warmup_lr)
)
)
self.warmup_lrs = list(self.warmup_lr)
else:
self.warmup_lrs = [self.warmup_lr] * len(self.optimizer.param_groups)
else:
self.warmup_lrs = None
if self.warmup > self.last_epoch:
curr_lrs = [group["lr"] for group in self.optimizer.param_groups]
self.warmup_lr_steps = [
max(0, (self.warmup_lrs[i] - curr_lrs[i]) / float(self.warmup))
for i in range(len(curr_lrs))
]
else:
self.warmup_lr_steps = None
def state_dict(self):
return {
key: value for key, value in self.__dict__.items() if key != "optimizer"
}
def load_state_dict(self, state_dict):
self.__dict__.update(state_dict)
self._prepare_for_warmup()
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