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import math
from typing import Literal
import torch
from torch.optim.lr_scheduler import LambdaLR
from diffusers.utils import logging
logger = logging.get_logger(__name__)
def get_one_cycle_schedule(
optimizer: torch.optim.Optimizer,
num_training_steps: int,
warmup: Literal["cos", "linear"] = "cos",
annealing: Literal["cos", "half_cos", "linear"] = "cos",
warmup_exp: int = 1,
annealing_exp: int = 1,
min_lr: int = 0.04,
mid_point: int = 0.3,
last_epoch: int = -1
):
def lr_lambda(current_step: int):
thresh_up = int(num_training_steps * min(mid_point, 0.5))
if current_step < thresh_up:
progress = float(current_step) / float(max(1, thresh_up))
if warmup == "linear":
return min_lr + progress * (1 - min_lr)
lr = 0.5 * (1.0 + math.cos(math.pi * (1 + progress)))
lr = lr ** warmup_exp
return min_lr + lr * (1 - min_lr)
if annealing == "linear":
thresh_down = thresh_up * 2
if current_step < thresh_down:
progress = float(thresh_down - current_step) / float(max(1, thresh_down - thresh_up))
return min_lr + progress * (1 - min_lr)
progress = float(num_training_steps - current_step) / float(max(1, num_training_steps - thresh_down))
return progress * min_lr
progress = float(current_step - thresh_up) / float(max(1, num_training_steps - thresh_up))
if annealing == "half_cos":
lr = 1.0 + math.cos(math.pi * (0.5 + 0.5 * progress))
lr = lr ** annealing_exp
return lr
lr = 0.5 * (1.0 + math.cos(math.pi * progress))
lr = lr ** annealing_exp
return lr
return LambdaLR(optimizer, lr_lambda, last_epoch)
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