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73 lines
2.8 KiB
Python
73 lines
2.8 KiB
Python
from paddle.optimizer import lr
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import logging
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__all__ = ["Polynomial"]
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class Polynomial(object):
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"""
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Polynomial learning rate decay
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Args:
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learning_rate (float): The initial learning rate. It is a python float number.
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epochs(int): The decay epoch size. It determines the decay cycle, when by_epoch is set to true, it will change to epochs=epochs*step_each_epoch.
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step_each_epoch: all steps in each epoch.
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end_lr(float, optional): The minimum final learning rate. Default: 0.0001.
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power(float, optional): Power of polynomial. Default: 1.0.
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warmup_epoch(int): The epoch numbers for LinearWarmup. Default: 0, , when by_epoch is set to true, it will change to warmup_epoch=warmup_epoch*step_each_epoch.
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warmup_start_lr(float): Initial learning rate of warm up. Default: 0.0.
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last_epoch (int, optional): The index of last epoch. Can be set to restart training. Default: -1, means initial learning rate.
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by_epoch: Whether the set parameter is based on epoch or iter, when set to true,, epochs and warmup_epoch will be automatically multiplied by step_each_epoch. Default: True
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"""
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def __init__(
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self,
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learning_rate,
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epochs,
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step_each_epoch,
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end_lr=0.0,
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power=1.0,
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warmup_epoch=0,
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warmup_start_lr=0.0,
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last_epoch=-1,
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by_epoch=True,
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**kwargs,
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):
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super().__init__()
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if warmup_epoch >= epochs:
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msg = f'When using warm up, the value of "epochs" must be greater than value of "Optimizer.lr.warmup_epoch". The value of "Optimizer.lr.warmup_epoch" has been set to {epochs}.'
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logging.warning(msg)
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warmup_epoch = epochs
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self.learning_rate = learning_rate
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self.epochs = epochs
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self.end_lr = end_lr
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self.power = power
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self.last_epoch = last_epoch
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self.warmup_epoch = warmup_epoch
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self.warmup_start_lr = warmup_start_lr
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if by_epoch:
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self.epochs *= step_each_epoch
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self.warmup_epoch = int(self.warmup_epoch * step_each_epoch)
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def __call__(self):
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learning_rate = (
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lr.PolynomialDecay(
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learning_rate=self.learning_rate,
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decay_steps=self.epochs,
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end_lr=self.end_lr,
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power=self.power,
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last_epoch=self.last_epoch,
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)
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if self.epochs > 0
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else self.learning_rate
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)
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if self.warmup_epoch > 0:
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learning_rate = lr.LinearWarmup(
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learning_rate=learning_rate,
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warmup_steps=self.warmup_epoch,
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start_lr=self.warmup_start_lr,
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end_lr=self.learning_rate,
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last_epoch=self.last_epoch,
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)
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return learning_rate
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