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mmengine.optim.scheduler.momentum_scheduler 源代码

# Copyright (c) OpenMMLab. All rights reserved.
from mmengine.registry import PARAM_SCHEDULERS
# yapf: disable
from .param_scheduler import (ConstantParamScheduler,
                              CosineAnnealingParamScheduler,
                              CosineRestartParamScheduler,
                              ExponentialParamScheduler, LinearParamScheduler,
                              MultiStepParamScheduler, PolyParamScheduler,
                              ReduceOnPlateauParamScheduler,
                              StepParamScheduler)

# yapf: enable


class MomentumSchedulerMixin:
    """A mixin class for momentum schedulers.

    It can schedule the momentum in SGD and the beta_0 in Adam series.
    """

    def __init__(self, optimizer, *args, **kwargs):
        self.use_betas = False
        if 'momentum' in optimizer.defaults:
            param_name = 'momentum'
        elif 'betas' in optimizer.defaults:
            # for Adam series optimizer, the momentum is beta_0
            self.use_betas = True
            param_name = 'momentum'
            for group in optimizer.param_groups:
                # set a reference momentum in the param groups for scheduling
                group[param_name] = group['betas'][0]
        else:
            raise ValueError(
                'optimizer must support momentum when using momentum scheduler'
            )
        super().__init__(optimizer, param_name, *args, **kwargs)

    def step(self):
        """Adjusts the momentum of each parameter group based on the specified
        schedule."""
        super().step()
        if self.use_betas:
            for group in self.optimizer.param_groups:
                _, beta_1 = group['betas']
                # update the betas with the calculated value
                group['betas'] = (group['momentum'], beta_1)


[文档]@PARAM_SCHEDULERS.register_module() class ConstantMomentum(MomentumSchedulerMixin, ConstantParamScheduler): """Decays the momentum 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 momentum value from outside this scheduler. Args: optimizer (Optimizer or OptimWrapper): optimizer or Wrapped optimizer. factor (float): The number we multiply momentum until the milestone. Defaults to 1./3. begin (int): Step at which to start updating the momentum. Defaults to 0. end (int): Step at which to stop updating the momentum. 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 momentum is updated by epochs. Defaults to True. verbose (bool): Whether to print the momentum for each update. Defaults to False. """
[文档]@PARAM_SCHEDULERS.register_module() class CosineAnnealingMomentum(MomentumSchedulerMixin, CosineAnnealingParamScheduler): r"""Set the momentum of each parameter group using a cosine annealing schedule, where :math:`\eta_{max}` is set to the initial value and :math:`T_{cur}` is the number of epochs since the last restart in SGDR: .. math:: \begin{aligned} \eta_t & = \eta_{min} + \frac{1}{2}(\eta_{max} - \eta_{min})\left(1 + \cos\left(\frac{T_{cur}}{T_{max}}\pi\right)\right), & T_{cur} \neq (2k+1)T_{max}; \\ \eta_{t+1} & = \eta_{t} + \frac{1}{2}(\eta_{max} - \eta_{min}) \left(1 - \cos\left(\frac{1}{T_{max}}\pi\right)\right), & T_{cur} = (2k+1)T_{max}. \end{aligned} Notice that because the schedule is defined recursively, the momentum can be simultaneously modified outside this scheduler by other operators. If the momentum is set solely by this scheduler, the momentum at each step becomes: .. math:: \eta_t = \eta_{min} + \frac{1}{2}(\eta_{max} - \eta_{min})\left(1 + \cos\left(\frac{T_{cur}}{T_{max}}\pi\right)\right) It has been proposed in `SGDR: Stochastic Gradient Descent with Warm Restarts`_. Note that this only implements the cosine annealing part of SGDR, and not the restarts. Args: optimizer (Optimizer or OptimWrapper): optimizer or Wrapped optimizer. T_max (int): Maximum number of iterations. eta_min (float): Minimum momentum value. Defaults to None. begin (int): Step at which to start updating the momentum. Defaults to 0. end (int): Step at which to stop updating the momentum. 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 momentum is updated by epochs. Defaults to True. verbose (bool): Whether to print the momentum for each update. Defaults to False. eta_min_ratio (float, optional): The ratio of the minimum parameter value to the base parameter value. Either `eta_min` or `eta_min_ratio` should be specified. Defaults to None. New in version 0.3.2. .. _SGDR\: Stochastic Gradient Descent with Warm Restarts: https://arxiv.org/abs/1608.03983 """
[文档]@PARAM_SCHEDULERS.register_module() class ExponentialMomentum(MomentumSchedulerMixin, ExponentialParamScheduler): """Decays the momentum of each parameter group by gamma every epoch. Args: optimizer (Optimizer or OptimWrapper): optimizer or Wrapped optimizer. gamma (float): Multiplicative factor of momentum value decay. begin (int): Step at which to start updating the momentum. Defaults to 0. end (int): Step at which to stop updating the momentum. 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 momentum is updated by epochs. Defaults to True. verbose (bool): Whether to print the momentum for each update. Defaults to False. """
[文档]@PARAM_SCHEDULERS.register_module() class LinearMomentum(MomentumSchedulerMixin, LinearParamScheduler): """Decays the momentum of each parameter group by linearly changing small multiplicative factor until the number of epoch reaches a pre-defined milestone: ``end``. Notice that such decay can happen simultaneously with other changes to the momentum from outside this scheduler. Args: optimizer (Optimizer or OptimWrapper): optimizer or Wrapped optimizer. start_factor (float): The number we multiply momentum in the first epoch. The multiplication factor changes towards end_factor in the following epochs. Defaults to 1./3. end_factor (float): The number we multiply momentum at the end of linear changing process. Defaults to 1.0. begin (int): Step at which to start updating the momentum. Defaults to 0. end (int): Step at which to stop updating the momentum. 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 momentum is updated by epochs. Defaults to True. verbose (bool): Whether to print the momentum for each update. Defaults to False. """
[文档]@PARAM_SCHEDULERS.register_module() class MultiStepMomentum(MomentumSchedulerMixin, MultiStepParamScheduler): """Decays the specified momentum in each parameter group by gamma once the number of epoch reaches one of the milestones. Notice that such decay can happen simultaneously with other changes to the momentum from outside this scheduler. Args: optimizer (Optimizer or OptimWrapper): optimizer or Wrapped optimizer. milestones (list): List of epoch indices. Must be increasing. gamma (float): Multiplicative factor of momentum value decay. Defaults to 0.1. begin (int): Step at which to start updating the momentum. Defaults to 0. end (int): Step at which to stop updating the momentum. 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 momentum is updated by epochs. Defaults to True. verbose (bool): Whether to print the momentum for each update. Defaults to False. """
[文档]@PARAM_SCHEDULERS.register_module() class StepMomentum(MomentumSchedulerMixin, StepParamScheduler): """Decays the momentum of each parameter group by gamma every step_size epochs. Notice that such decay can happen simultaneously with other changes to the momentum from outside this scheduler. Args: optimizer (Optimizer or OptimWrapper): optimizer or Wrapped optimizer. step_size (int): Period of momentum value decay. gamma (float): Multiplicative factor of momentum value decay. Defaults to 0.1. begin (int): Step at which to start updating the momentum. Defaults to 0. end (int): Step at which to stop updating the momentum. 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 momentum is updated by epochs. Defaults to True. verbose (bool): Whether to print the momentum for each update. Defaults to False. """
[文档]@PARAM_SCHEDULERS.register_module() class PolyMomentum(MomentumSchedulerMixin, PolyParamScheduler): """Decays the momentum of each parameter group in a polynomial decay scheme. Notice that such decay can happen simultaneously with other changes to the parameter value from outside this scheduler. Args: optimizer (Optimizer or OptimWrapper): optimizer or Wrapped optimizer. eta_min (float): Minimum momentum at the end of scheduling. Defaults to 0. power (float): The power of the polynomial. Defaults to 1.0. 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. """
@PARAM_SCHEDULERS.register_module() class CosineRestartMomentum(MomentumSchedulerMixin, CosineRestartParamScheduler): """Sets the momentum of each parameter group according to the cosine annealing with restarts scheme. The cosine restart policy anneals the momentum from the initial value to `eta_min` with a cosine annealing schedule and then restarts another period from the maximum value multiplied with `restart_weight`. Args: optimizer (Optimizer or OptimWrapper): optimizer or Wrapped optimizer. periods (list[int]): Periods for each cosine anneling cycle. restart_weights (list[float]): Restart weights at each restart iteration. Defaults to [1]. eta_min (float): Minimum parameter value at the end of scheduling. Defaults to None. eta_min_ratio (float, optional): The ratio of minimum parameter value to the base parameter value. Either `min_lr` or `min_lr_ratio` should be specified. Defaults to None. 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. """
[文档]@PARAM_SCHEDULERS.register_module() class ReduceOnPlateauMomentum(MomentumSchedulerMixin, ReduceOnPlateauParamScheduler): """Reduce the momentum of each parameter group when a metric has stopped improving. Models often benefit from reducing the momentum 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 momentum is reduced. Args: optimizer (Optimizer or OptimWrapper): optimizer or Wrapped optimizer. monitor (str): Key name of the value to monitor in metrics dict. rule (str): One of `less`, `greater`. In `less` rule, momentum will be reduced when the quantity monitored has stopped decreasing; in `greater` rule it will be reduced when the quantity monitored has stopped increasing. Defaults to 'less'. The ``rule`` is the renaming of ``mode`` in pytorch. factor (float): Factor by which the momentum will be reduced. new_param = param * factor. Defaults to 0.1. patience (int): Number of epochs with no improvement after which momentum will be reduced. For example, if ``patience = 2``, then we will ignore the first 2 epochs with no improvement, and will only decrease the momentum after the 3rd epoch if the monitor value still hasn't improved then. Defaults to 10. threshold (float): Threshold for measuring the new optimum, to only focus on significant changes. Defaults to 1e-4. threshold_rule (str): One of `rel`, `abs`. In `rel` rule, dynamic_threshold = best * ( 1 + threshold ) in 'greater' rule or best * ( 1 - threshold ) in `less` rule. In `abs` rule, dynamic_threshold = best + threshold in `greater` rule or best - threshold in `less` rule. Defaults to 'rel'. cooldown (int): Number of epochs to wait before resuming normal operation after momentum has been reduced. Defaults to 0. min_value (float or list[float]): A scalar or a sequence of scalars. A lower bound on the momentum of each parameter group respectively. Defaults to 0. . eps (float): Minimal decay applied to momentum. If the difference between new and old momentum is smaller than eps, the update is ignored. Defaults to 1e-8. begin (int): Step at which to start triggering the scheduler to monitor in val within the interval calculated according to epoch of training. Defaults to 0. end (int): Step at which to stop triggering the scheduler to monitor in val within the interval calculated according to epoch of training. 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. """
[文档] def step(self, metrics=None): """Adjusts the momentum of each parameter group based on the specified schedule. Args: metrics (Dict[str, float], optional): Evaluation results of all metrics on validation dataset. The keys are the names of the metrics, and the values are corresponding results. Defaults to None. """ super(MomentumSchedulerMixin, self).step(metrics) if self.use_betas: for group in self.optimizer.param_groups: _, beta_1 = group['betas'] # update the betas with the calculated value group['betas'] = (group['momentum'], beta_1)

© Copyright 2022, mmengine contributors. Revision 317d8f31.

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