Cyclic#

class vision_architectures.schedulers.cyclic.SineScheduler(start_value, max_value, decay=0.0, wavelength=None)[source]#

Bases: object

__init__(start_value, max_value, decay=0.0, wavelength=None)[source]#
set_wavelength(wavelength)[source]#
is_ready()[source]#
get()[source]#
step()[source]#
class vision_architectures.schedulers.cyclic.SineLR(optimizer, start_lr, max_lr, wavelength, decay, last_epoch=-1, verbose='deprecated')[source]#

Bases: LRScheduler

__init__(optimizer, start_lr, max_lr, wavelength, decay, last_epoch=-1, verbose='deprecated')[source]#
get_lr()[source]#

Compute learning rate using chainable form of the scheduler.

step(epoch=None)[source]#

Perform a step.

class vision_architectures.schedulers.cyclic.Phase(value)[source]#

Bases: Enum

An enumeration.

UP = 1#
TOP = 2#
DOWN = 3#
BOTTOM = 4#
class vision_architectures.schedulers.cyclic.CyclicAnnealingScheduler(start_value, max_value, up_annealing_steps=None, top_fixed_steps=None, down_annealing_steps=None, bottom_fixed_steps=None)[source]#

Bases: object

Cyclic Annealing Schedule, inspired by the paper Cyclical Annealing Schedule: A Simple Approach to Mitigating KL Vanishing.

NOT_READY_ERROR_MSG = 'Number of steps for each phase must be set before using the scheduler'#
__init__(start_value, max_value, up_annealing_steps=None, top_fixed_steps=None, down_annealing_steps=None, bottom_fixed_steps=None)[source]#
set_num_annealing_steps(up_annealing_steps=None, top_annealing_steps=None, down_annealing_steps=None, bottom_annealing_steps=None)[source]#
set_next_phase()[source]#
is_ready()[source]#
get()[source]#
step()[source]#
class vision_architectures.schedulers.cyclic.CyclicAnnealingLR(optimizer, start_lr, max_lr, up_annealing_steps, top_fixed_steps, down_annealing_steps, bottom_fixed_steps, last_epoch=-1, verbose='deprecated')[source]#

Bases: LRScheduler

__init__(optimizer, start_lr, max_lr, up_annealing_steps, top_fixed_steps, down_annealing_steps, bottom_fixed_steps, last_epoch=-1, verbose='deprecated')[source]#
get_lr()[source]#

Compute learning rate using chainable form of the scheduler.

step(epoch=None)[source]#

Perform a step.