import torch
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from torch.optim.lr_scheduler import _LRScheduler
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# class CustomLambdaLR(_LRScheduler):
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# def __init__(self, optimizer, warmup_steps, last_epoch=-1):
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# self.warmup_steps = warmup_steps
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# super().__init__(optimizer, last_epoch)
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#
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# def get_lr(self):
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# if self.last_epoch < self.warmup_steps:
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# return [
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# base_lr * min(self.last_epoch / self.warmup_steps, 1) for base_lr in self.base_lrs
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# ]
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# else:
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# return [base_lr for base_lr in self.base_lrs]
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class CustomLambdaLR(_LRScheduler):
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def __init__(
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self,
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optimizer,
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warmup_steps: int = 25000,
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total_steps: int = 500000,
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last_epoch=-1,
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verbose=False,
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):
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self.warmup_steps = warmup_steps
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self.total_steps = total_steps
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super().__init__(optimizer, last_epoch, verbose)
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def get_lr(self):
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step = self.last_epoch + 1
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if step < self.warmup_steps:
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lr_scale = step / self.warmup_steps
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else:
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lr_scale = max(
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0.0, 1 - (step - self.warmup_steps) / (self.total_steps - self.warmup_steps)
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)
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return [base_lr * lr_scale for base_lr in self.base_lrs]
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