towhee.trainer.scheduler.configure_polynomial_decay_scheduler_with_warmup¶

towhee.trainer.scheduler.configure_polynomial_decay_scheduler_with_warmup(optimizer, num_warmup_steps, num_training_steps, lr_end=1e-07, power=1.0, last_epoch=-1)[source]¶

Return a scheduler with a learning rate that decreases as a polynomial decay from the initial lr set in the optimizer to end lr defined by lr_end, after a warmup period during which it increases linearly from 0 to the initial lr set in the optimizer.

Parameters:

optimizer (Optimizer) – The optimizer to be scheduled.
num_warmup_steps (int) – The steps for the warmup phase.
num_training_steps (int) – The number of training steps
lr_end (float) – The end LR.
power (float) – Power factor.
last_epoch (int) – The index of the last epoch when training is resumed.

Return (LambdaLR):: A polynomial decay scheduler with warmup.

Example

>>> from towhee.trainer.scheduler import configure_polynomial_decay_scheduler_with_warmup
>>> from towhee.trainer.optimization.adamw import AdamW
>>> from torch import nn
>>> def unwrap_scheduler(scheduler, num_steps=10):
>>>     lr_sch = []
>>>     for _ in range(num_steps):
>>>         lr_sch.append(scheduler.get_lr()[0])
>>>         scheduler.step()
>>>     return lr_sch
>>> mdl = nn.Linear(50, 50)
>>> optimizer = AdamW(mdl.parameters(), lr=10.0)
>>> num_steps = 10
>>> num_warmup_steps = 4
>>> num_training_steps = 10
>>> power = 2.0
>>> lr_end = 1e-7
>>> scheduler = configure_polynomial_decay_scheduler_with_warmup(optimizer,
num_warmup_steps, num_training_steps, num_cycles)
>>> lr_sch_1 = unwrap_scheduler(scheduler, num_steps)
[0.0, 5.0, 10.0, 7.656, 5.625, 3.906, 2.5, 1.406, 0.625, 0.156]