mmengine.optim

Optimizer

AmpOptimWrapper	A subclass of OptimWrapper that supports automatic mixed precision training based on torch.cuda.amp.
ApexOptimWrapper	A subclass of OptimWrapper that supports automatic mixed precision training based on apex.amp.
OptimWrapper	Optimizer wrapper provides a common interface for updating parameters.
OptimWrapperDict	A dictionary container of OptimWrapper.
DefaultOptimWrapperConstructor	Default constructor for optimizers.
ZeroRedundancyOptimizer	A wrapper class of ZeroRedundancyOptimizer that gets a optimizer type as string.

build_optim_wrapper

Build function of OptimWrapper.

Scheduler

_ParamScheduler	Base class for parameter schedulers.
ConstantLR	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.
ConstantMomentum	Decays the momentum value of each parameter group by a small constant factor until the number of epoch reaches a pre-defined milestone: end.
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.
CosineAnnealingLR	Set the learning rate of each parameter group using a cosine annealing schedule, where \(\eta_{max}\) is set to the initial value and \(T_{cur}\) is the number of epochs since the last restart in SGDR:
CosineAnnealingMomentum	Set the momentum of each parameter group using a cosine annealing schedule, where \(\eta_{max}\) is set to the initial value and \(T_{cur}\) is the number of epochs since the last restart in SGDR:
CosineAnnealingParamScheduler	Set the parameter value of each parameter group using a cosine annealing schedule, where \(\eta_{max}\) is set to the initial value and \(T_{cur}\) is the number of epochs since the last restart in SGDR:
ExponentialLR	Decays the learning rate of each parameter group by gamma every epoch.
ExponentialMomentum	Decays the momentum of each parameter group by gamma every epoch.
ExponentialParamScheduler	Decays the parameter value of each parameter group by gamma every epoch.
LinearLR	Decays the learning rate of each parameter group by linearly changing small multiplicative factor until the number of epoch reaches a pre-defined milestone: end.
LinearMomentum	Decays the momentum of each parameter group by linearly changing small multiplicative factor until the number of epoch reaches a pre-defined milestone: end.
LinearParamScheduler	Decays the parameter value of each parameter group by linearly changing small multiplicative factor until the number of epoch reaches a pre-defined milestone: end.
MultiStepLR	Decays the specified learning rate in each parameter group by gamma once the number of epoch reaches one of the milestones.
MultiStepMomentum	Decays the specified momentum in each parameter group by gamma once the number of epoch reaches one of the milestones.
MultiStepParamScheduler	Decays the specified parameter in each parameter group by gamma once the number of epoch reaches one of the milestones.
OneCycleLR	Sets the learning rate of each parameter group according to the 1cycle learning rate policy.
OneCycleParamScheduler	Sets the parameters of each parameter group according to the 1cycle learning rate policy.
PolyLR	Decays the learning rate of each parameter group in a polynomial decay scheme.
PolyMomentum	Decays the momentum of each parameter group in a polynomial decay scheme.
PolyParamScheduler	Decays the parameter value of each parameter group in a polynomial decay scheme.
StepLR	Decays the learning rate of each parameter group by gamma every step_size epochs.
StepMomentum	Decays the momentum of each parameter group by gamma every step_size epochs.
StepParamScheduler	Decays the parameter value of each parameter group by gamma every step_size epochs.
ReduceOnPlateauLR	Reduce the learning rate of each parameter group when a metric has stopped improving.
ReduceOnPlateauMomentum	Reduce the momentum of each parameter group when a metric has stopped improving.
ReduceOnPlateauParamScheduler	Reduce the parameters of each parameter group when a metric has stopped improving.