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Running
on
Zero
File size: 3,285 Bytes
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# Copyright (c) OpenMMLab. All rights reserved.
from mmengine.optim.scheduler import \
ConstantParamScheduler as MMENGINE_ConstantParamScheduler
from mmengine.optim.scheduler.lr_scheduler import LRSchedulerMixin
from mmpose.registry import PARAM_SCHEDULERS
INF = int(1e9)
class ConstantParamScheduler(MMENGINE_ConstantParamScheduler):
"""Decays the parameter value of each parameter group by a small constant
factor until the number of epoch reaches a pre-defined milestone: ``end``.
Notice that such decay can happen simultaneously with other changes to the
parameter value from outside this scheduler. The factor range restriction
is removed.
Args:
optimizer (Optimizer or BaseOptimWrapper): optimizer or Wrapped
optimizer.
param_name (str): Name of the parameter to be adjusted, such as
``lr``, ``momentum``.
factor (float): The number we multiply parameter value until the
milestone. Defaults to 1./3.
begin (int): Step at which to start updating the parameters.
Defaults to 0.
end (int): Step at which to stop updating the parameters.
Defaults to INF.
last_step (int): The index of last step. Used for resume without
state dict. Defaults to -1.
by_epoch (bool): Whether the scheduled parameters are updated by
epochs. Defaults to True.
verbose (bool): Whether to print the value for each update.
Defaults to False.
"""
def __init__(self,
optimizer,
param_name: str,
factor: float = 1.0 / 3,
begin: int = 0,
end: int = INF,
last_step: int = -1,
by_epoch: bool = True,
verbose: bool = False):
self.factor = factor
self.total_iters = end - begin - 1
super(MMENGINE_ConstantParamScheduler, self).__init__(
optimizer,
param_name=param_name,
begin=begin,
end=end,
last_step=last_step,
by_epoch=by_epoch,
verbose=verbose)
@PARAM_SCHEDULERS.register_module()
class ConstantLR(LRSchedulerMixin, ConstantParamScheduler):
"""Decays the learning rate value of each parameter group by a small
constant factor until the number of epoch reaches a pre-defined milestone:
``end``. Notice that such decay can happen simultaneously with other
changes to the learning rate value from outside this scheduler.
Args:
optimizer (Optimizer or OptimWrapper): Wrapped optimizer.
factor (float): The number we multiply learning rate until the
milestone. Defaults to 1./3.
begin (int): Step at which to start updating the learning rate.
Defaults to 0.
end (int): Step at which to stop updating the learning rate.
Defaults to INF.
last_step (int): The index of last step. Used for resume without state
dict. Defaults to -1.
by_epoch (bool): Whether the scheduled learning rate is updated by
epochs. Defaults to True.
verbose (bool): Whether to print the learning rate for each update.
Defaults to False.
"""
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