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import math
from torch.optim.lr_scheduler import LambdaLR
from diffusers.utils import logging
logger = logging.get_logger(__name__)
def get_one_cycle_schedule(optimizer, num_training_steps, annealing="cos", min_lr=0.001, mid_point=0.4, last_epoch=-1):
"""
Create a schedule with a learning rate that decreases linearly from the initial lr set in the optimizer to 0, after
a warmup period during which it increases linearly from 0 to the initial lr set in the optimizer.
Args:
optimizer ([`~torch.optim.Optimizer`]):
The optimizer for which to schedule the learning rate.
num_training_steps (`int`):
The total number of training steps.
last_epoch (`int`, *optional*, defaults to -1):
The index of the last epoch when resuming training.
Return:
`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
"""
def lr_lambda(current_step: int):
thresh_up = int(num_training_steps * min(mid_point, 0.5))
if current_step < thresh_up:
return min_lr + float(current_step) / float(max(1, thresh_up)) * (1 - min_lr)
if annealing == "linear":
thresh_down = thresh_up * 2
if current_step < thresh_down:
return min_lr + float(thresh_down - current_step) / float(max(1, thresh_down - thresh_up)) * (1 - min_lr)
progress = float(num_training_steps - current_step) / float(max(1, num_training_steps - thresh_down))
return max(0.0, progress) * min_lr
else:
progress = float(current_step - thresh_up) / float(max(1, num_training_steps - thresh_up))
return max(0.0, 1.0 + math.cos(math.pi * (0.5 + 0.5 * progress)))
return LambdaLR(optimizer, lr_lambda, last_epoch)
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