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# EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction
# Han Cai, Junyan Li, Muyan Hu, Chuang Gan, Song Han
# International Conference on Computer Vision (ICCV), 2023
import math
import torch
from efficientvit.models.utils.list import val2list
__all__ = ["CosineLRwithWarmup"]
class CosineLRwithWarmup(torch.optim.lr_scheduler._LRScheduler):
def __init__(
self,
optimizer: torch.optim.Optimizer,
warmup_steps: int,
warmup_lr: float,
decay_steps: int or list[int],
last_epoch: int = -1,
) -> None:
self.warmup_steps = warmup_steps
self.warmup_lr = warmup_lr
self.decay_steps = val2list(decay_steps)
super().__init__(optimizer, last_epoch)
def get_lr(self) -> list[float]:
if self.last_epoch < self.warmup_steps:
return [
(base_lr - self.warmup_lr) * (self.last_epoch + 1) / self.warmup_steps
+ self.warmup_lr
for base_lr in self.base_lrs
]
else:
current_steps = self.last_epoch - self.warmup_steps
decay_steps = [0] + self.decay_steps
idx = len(decay_steps) - 2
for i, decay_step in enumerate(decay_steps[:-1]):
if decay_step <= current_steps < decay_steps[i + 1]:
idx = i
break
current_steps -= decay_steps[idx]
decay_step = decay_steps[idx + 1] - decay_steps[idx]
return [
0.5 * base_lr * (1 + math.cos(math.pi * current_steps / decay_step))
for base_lr in self.base_lrs
]
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