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cyclic_scheduler.py
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cyclic_scheduler.py
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from torch.optim import Optimizer
from torch.optim.lr_scheduler import _LRScheduler
import math
import torch
class ReduceMaxLROnRestart:
def __init__(self, ratio=0.75):
self.ratio = ratio
def __call__(self, eta_min, eta_max):
return eta_min, eta_max * self.ratio
class ExpReduceMaxLROnIteration:
def __init__(self, gamma=1):
self.gamma = gamma
def __call__(self, eta_min, eta_max, iterations):
return eta_min, eta_max * self.gamma ** iterations
class CosinePolicy:
def __call__(self, t_cur, restart_period):
return 0.5 * (1. + math.cos(math.pi *
(t_cur / restart_period)))
class ArccosinePolicy:
def __call__(self, t_cur, restart_period):
return (math.acos(max(-1, min(1, 2 * t_cur
/ restart_period - 1))) / math.pi)
class TriangularPolicy:
def __init__(self, triangular_step=0.5):
self.triangular_step = triangular_step
def __call__(self, t_cur, restart_period):
inflection_point = self.triangular_step * restart_period
point_of_triangle = (t_cur / inflection_point
if t_cur < inflection_point
else 1.0 - (t_cur - inflection_point)
/ (restart_period - inflection_point))
return point_of_triangle
class CyclicLRWithRestarts(_LRScheduler):
"""Decays learning rate with cosine annealing, normalizes weight decay
hyperparameter value, implements restarts.
https://arxiv.org/abs/1711.05101
Args:
optimizer (Optimizer): Wrapped optimizer.
batch_size: minibatch size
epoch_size: training samples per epoch
restart_period: epoch count in the first restart period
t_mult: multiplication factor by which the next restart period will expand/shrink
policy: ["cosine", "arccosine", "triangular", "triangular2", "exp_range"]
min_lr: minimum allowed learning rate
verbose: print a message on every restart
gamma: exponent used in "exp_range" policy
eta_on_restart_cb: callback executed on every restart, adjusts max or min lr
eta_on_iteration_cb: callback executed on every iteration, adjusts max or min lr
triangular_step: adjusts ratio of increasing/decreasing phases for triangular policy
Example:
>>> scheduler = CyclicLRWithRestarts(optimizer, 32, 1024, restart_period=5, t_mult=1.2)
>>> for epoch in range(100):
>>> scheduler.step()
>>> train(...)
>>> ...
>>> optimizer.zero_grad()
>>> loss.backward()
>>> optimizer.step()
>>> scheduler.batch_step()
>>> validate(...)
"""
def __init__(self, optimizer, batch_size, epoch_size, restart_period=100,
t_mult=2, last_epoch=-1, verbose=False,
policy="cosine", policy_fn=None, min_lr=1e-7,
eta_on_restart_cb=None, eta_on_iteration_cb=None,
gamma=1.0, triangular_step=0.5):
if not isinstance(optimizer, Optimizer):
raise TypeError('{} is not an Optimizer'.format(
type(optimizer).__name__))
self.optimizer = optimizer
if last_epoch == -1:
for group in optimizer.param_groups:
group.setdefault('initial_lr', group['lr'])
group.setdefault('minimum_lr', min_lr)
else:
for i, group in enumerate(optimizer.param_groups):
if 'initial_lr' not in group:
raise KeyError("param 'initial_lr' is not specified "
"in param_groups[{}] when resuming an"
" optimizer".format(i))
self.base_lrs = [group['initial_lr'] for group
in optimizer.param_groups]
self.min_lrs = [group['minimum_lr'] for group
in optimizer.param_groups]
self.base_weight_decays = [group['weight_decay'] for group
in optimizer.param_groups]
self.policy = policy
self.eta_on_restart_cb = eta_on_restart_cb
self.eta_on_iteration_cb = eta_on_iteration_cb
if policy_fn is not None:
self.policy_fn = policy_fn
elif self.policy == "cosine":
self.policy_fn = CosinePolicy()
elif self.policy == "arccosine":
self.policy_fn = ArccosinePolicy()
elif self.policy == "triangular":
self.policy_fn = TriangularPolicy(triangular_step=triangular_step)
elif self.policy == "triangular2":
self.policy_fn = TriangularPolicy(triangular_step=triangular_step)
self.eta_on_restart_cb = ReduceMaxLROnRestart(ratio=0.5)
elif self.policy == "exp_range":
self.policy_fn = TriangularPolicy(triangular_step=triangular_step)
self.eta_on_iteration_cb = ExpReduceMaxLROnIteration(gamma=gamma)
self.last_epoch = last_epoch
self.batch_size = batch_size
self.epoch_size = epoch_size
self.iteration = 0
self.total_iterations = 0
self.t_mult = t_mult
self.verbose = verbose
self.restart_period = math.ceil(restart_period)
self.restarts = 0
self.t_epoch = -1
self.epoch = -1
self.eta_min = 0
self.eta_max = 1
self.end_of_period = False
self.batch_increments = []
self._set_batch_increment()
def _on_restart(self):
if self.eta_on_restart_cb is not None:
self.eta_min, self.eta_max = self.eta_on_restart_cb(self.eta_min,
self.eta_max)
def _on_iteration(self):
if self.eta_on_iteration_cb is not None:
self.eta_min, self.eta_max = self.eta_on_iteration_cb(self.eta_min,
self.eta_max,
self.total_iterations)
def get_lr(self, t_cur):
eta_t = (self.eta_min + (self.eta_max - self.eta_min)
* self.policy_fn(t_cur, self.restart_period))
weight_decay_norm_multi = math.sqrt(self.batch_size /
(self.epoch_size *
self.restart_period))
lrs = [min_lr + (base_lr - min_lr) * eta_t for base_lr, min_lr
in zip(self.base_lrs, self.min_lrs)]
weight_decays = [base_weight_decay * eta_t * weight_decay_norm_multi
for base_weight_decay in self.base_weight_decays]
if (self.t_epoch + 1) % self.restart_period < self.t_epoch:
self.end_of_period = True
if self.t_epoch % self.restart_period < self.t_epoch:
if self.verbose:
print("Restart {} at epoch {}".format(self.restarts + 1,
self.last_epoch))
self.restart_period = math.ceil(self.restart_period * self.t_mult)
self.restarts += 1
self.t_epoch = 0
self._on_restart()
self.end_of_period = False
return zip(lrs, weight_decays)
def _set_batch_increment(self):
d, r = divmod(self.epoch_size, self.batch_size)
batches_in_epoch = d + 2 if r > 0 else d + 1
self.iteration = 0
self.batch_increments = torch.linspace(0, 1, batches_in_epoch).tolist()
def step(self):
self.last_epoch += 1
self.t_epoch += 1
self._set_batch_increment()
self.batch_step()
def batch_step(self):
try:
t_cur = self.t_epoch + self.batch_increments[self.iteration]
self._on_iteration()
self.iteration += 1
self.total_iterations += 1
except (IndexError):
raise StopIteration("Epoch size and batch size used in the "
"training loop and while initializing "
"scheduler should be the same.")
for param_group, (lr, weight_decay) in zip(self.optimizer.param_groups,
self.get_lr(t_cur)):
param_group['lr'] = lr
param_group['weight_decay'] = weight_decay