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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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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)
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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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