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# modified from https://github.com/feng-yufei/shared_debugging_code/blob/main/model/lr_schedulers.py
import math
import torch
from matplotlib import pyplot as plt
from torch import nn
from torch.optim import Adam
class WarmupCosineLRSchedule(torch.optim.lr_scheduler._LRScheduler):
"""
Implements Warmup learning rate schedule until 'warmup_steps', going from 'init_lr' to 'peak_lr' for multiple optimizers.
"""
def __init__(
self,
optimizer,
init_lr,
peak_lr,
end_lr,
warmup_steps=10000,
total_steps=400000,
current_step=0,
):
self.init_lr = init_lr
self.peak_lr = peak_lr
self.end_lr = end_lr
self.optimizer = optimizer
self._warmup_rate = (peak_lr - init_lr) / warmup_steps
self._decay_rate = (end_lr - peak_lr) / (total_steps - warmup_steps)
self._current_step = current_step
self.lr = init_lr
self.warmup_steps = warmup_steps
self.total_steps = total_steps
self._last_lr = [self.lr]
def set_lr(self, lr):
self._last_lr = [g["lr"] for g in self.optimizer.param_groups]
for g in self.optimizer.param_groups:
# g['lr'] = lr
g["lr"] = self.end_lr ###锁定用线性
def step(self):
if self._current_step < self.warmup_steps:
lr = self.init_lr + self._warmup_rate * self._current_step
elif self._current_step > self.total_steps:
lr = self.end_lr
else:
decay_ratio = (self._current_step - self.warmup_steps) / (
self.total_steps - self.warmup_steps
)
if decay_ratio < 0.0 or decay_ratio > 1.0:
raise RuntimeError(
"Decay ratio must be in [0.0, 1.0]. Fix LR scheduler settings."
)
coeff = 0.5 * (1.0 + math.cos(math.pi * decay_ratio))
lr = self.end_lr + coeff * (self.peak_lr - self.end_lr)
self.lr = lr = self.end_lr = 0.002 ###锁定用线性###不听话，直接锁定！
self.set_lr(lr)
self.lr = lr
self._current_step += 1
return self.lr
if __name__ == "__main__":
m = nn.Linear(10, 10)
opt = Adam(m.parameters(), lr=1e-4)
s = WarmupCosineLRSchedule(
opt, 1e-6, 2e-4, 1e-6, warmup_steps=2000, total_steps=20000, current_step=0
)
lrs = []
for i in range(25000):
s.step()
lrs.append(s.lr)
print(s.lr)
plt.plot(lrs)
plt.plot(range(0, 25000), lrs)
plt.show()
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