pytorch_pfn_extras.training.triggers.ManualScheduleTrigger#

class pytorch_pfn_extras.training.triggers.ManualScheduleTrigger(points, unit)#

Bases: Trigger

Trigger invoked at specified point(s) of iterations or epochs.

This trigger accepts iterations or epochs indicated by given point(s). There are two ways to specify the point(s): iteration and epoch. iteration means the number of updates, while epoch means the number of sweeps over the training dataset. Fractional values are allowed if the point is a number of epochs; the trigger uses the iteration and epoch_detail attributes defined by the manager.

Parameters:

  • points (int, float, or list of int or float) – time of the trigger. Must be an integer or list of integer if unit is 'iteration'.

  • unit (str) – Unit of the time specified by points. It must be either 'iteration' or 'epoch'.

Methods

__init__(points, unit)

load_state_dict(state)

may_fire(iteration, epoch_length)

Flags if the trigger may fire at the current iteration

state_dict()

__call__(manager)#

Decides whether the extension should be called on this iteration.

Parameters:

manager (ExtensionsManager) – Manager object that this trigger is associated with. The iteration information in this manager is used to determine if the trigger should fire.

Returns:

True if the corresponding extension should be invoked in this iteration.

Return type:

bool

__init__(points, unit)#
Parameters:

  • points (Union[float, Sequence[float]]) –

  • unit (UnitLiteral) –

may_fire(iteration, epoch_length)#

Flags if the trigger may fire at the current iteration

This must not alter the trigger state

Parameters:

  • iteration (int) –

  • epoch_length (int) –

Return type:

bool