PolyScheduler

class PolyScheduler(max_update, base_lr=0.01, pwr=2, final_lr=0, warmup_steps=0, warmup_begin_lr=0, warmup_mode='linear')

Reduce the learning rate according to a polynomial of given power.

Calculate the new learning rate, after warmup if any, by:

final_lr + (start_lr - final_lr) * (1-nup/max_nup)^pwr
if nup < max_nup, 0 otherwise.

Parameters:

max_update: int

maximum number of updates before the decay reaches final learning rate.

base_lr: float

base learning rate to start from

pwr: int

power of the decay term as a function of the current number of updates.

final_lr: float

final learning rate after all steps

warmup_steps: int

number of warmup steps used before this scheduler starts decay

warmup_begin_lr: float

if using warmup, the learning rate from which it starts warming up

warmup_mode: string

warmup can be done in two modes. ‘linear’ mode gradually increases lr with each step in equal increments ‘constant’ mode keeps lr at warmup_begin_lr for warmup_steps