cosinewarmupcheduler

CosineWarmupScheduler

Bases: _LRScheduler

Cosine annealing with warmup learning rate scheduler.

Parameters:

Name	Type	Description	Default
optimizer	Optimizer	The optimizer to use during training.	required
warmup	int	The number of warmup steps.	required
max_iters	int	The number of maximum iterations the model is trained for.	required

Example

optimizer = torch.optim.SGD(model.parameters(), lr=0.1) scheduler = CosineWarmupScheduler(optimizer, warmup=10, max_iters=100) for epoch in range(100): scheduler.step() train(…)

Source code in spotPython/torch/cosinewarmupcheduler.py

class CosineWarmupScheduler(optim.lr_scheduler._LRScheduler):
    """Cosine annealing with warmup learning rate scheduler.

    Args:
        optimizer (torch.optim.Optimizer): The optimizer to use during training.
        warmup (int): The number of warmup steps.
        max_iters (int): The number of maximum iterations the model is trained for.

    Example:
        >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1)
        >>> scheduler = CosineWarmupScheduler(optimizer, warmup=10, max_iters=100)
        >>> for epoch in range(100):
        >>>     scheduler.step()
        >>>     train(...)
    """

    def __init__(self, optimizer, warmup, max_iters):
        self.warmup = warmup
        self.max_num_iters = max_iters
        super().__init__(optimizer)

    def get_lr(self):
        lr_factor = self.get_lr_factor(epoch=self.last_epoch)
        return [base_lr * lr_factor for base_lr in self.base_lrs]

    def get_lr_factor(self, epoch):
        lr_factor = 0.5 * (1 + np.cos(np.pi * epoch / self.max_num_iters))
        if epoch <= self.warmup:
            lr_factor *= epoch * 1.0 / self.warmup
        return lr_factor