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from torch.optim.lr_scheduler import _LRScheduler | |
class FixLR(_LRScheduler): | |
"""Sets the learning rate of each parameter group to the initial lr | |
decayed by gamma every step_size epochs. When last_epoch=-1, sets | |
initial lr as lr. | |
Args: | |
optimizer (Optimizer): Wrapped optimizer. | |
step_size (int): Period of learning rate decay. | |
gamma (float): Multiplicative factor of learning rate decay. | |
Default: 0.1. | |
last_epoch (int): The index of last epoch. Default: -1. | |
Example: | |
>>> # Fixed leraning rate | |
>>> scheduler = FixLR(optimizer, step_size=30, gamma=0.1) | |
>>> for epoch in range(100): | |
>>> scheduler.step() | |
>>> train(...) | |
>>> validate(...) | |
""" | |
def __init__(self, optimizer, last_epoch=-1): | |
super().__init__(optimizer, last_epoch) | |
def get_lr(self): | |
return self.base_lrs | |