MultiFactorScheduler

class MultiFactorScheduler(step, factor=1, base_lr=0.01, warmup_steps=0, warmup_begin_lr=0, warmup_mode='linear')

Reduce the learning rate by given a list of steps.

Assume there exists k such that:

step[k] <= num_update and num_update < step[k+1]

Then calculate the new learning rate by:

base_lr * pow(factor, k+1)

Parameters:

step: list of int

The list of steps to schedule a change

factor: float

The factor to change the learning rate.

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