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lr_scheduler.py
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lr_scheduler.py
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#!/usr/bin/python
# -*- encoding: utf-8 -*-
import math
import torch
from torch.optim.lr_scheduler import _LRScheduler, LambdaLR
import numpy as np
class WarmupExpLrScheduler(_LRScheduler):
def __init__(
self,
optimizer,
power,
step_interval=1,
warmup_iter=500,
warmup_ratio=5e-4,
warmup='exp',
last_epoch=-1,
):
self.power = power
self.step_interval = step_interval
self.warmup_iter = warmup_iter
self.warmup_ratio = warmup_ratio
self.warmup = warmup
super(WarmupExpLrScheduler, self).__init__(optimizer, last_epoch)
def get_lr(self):
ratio = self.get_lr_ratio()
lrs = [ratio * lr for lr in self.base_lrs]
return lrs
def get_lr_ratio(self):
if self.last_epoch < self.warmup_iter:
ratio = self.get_warmup_ratio()
else:
real_iter = self.last_epoch - self.warmup_iter
ratio = self.power ** (real_iter // self.step_interval)
return ratio
def get_warmup_ratio(self):
assert self.warmup in ('linear', 'exp')
alpha = self.last_epoch / self.warmup_iter
if self.warmup == 'linear':
ratio = self.warmup_ratio + (1 - self.warmup_ratio) * alpha
elif self.warmup == 'exp':
ratio = self.warmup_ratio ** (1. - alpha)
return ratio
class WarmupPolyLrScheduler(_LRScheduler):
def __init__(
self,
optimizer,
power,
max_iter,
warmup_iter,
warmup_ratio=5e-4,
warmup='exp',
last_epoch=-1,
):
self.power = power
self.max_iter = max_iter
self.warmup_iter = warmup_iter
self.warmup_ratio = warmup_ratio
self.warmup = warmup
super(WarmupPolyLrScheduler, self).__init__(optimizer, last_epoch)
def get_lr(self):
ratio = self.get_lr_ratio()
lrs = [ratio * lr for lr in self.base_lrs]
return lrs
def get_lr_ratio(self):
if self.last_epoch < self.warmup_iter:
ratio = self.get_warmup_ratio()
else:
real_iter = self.last_epoch - self.warmup_iter
real_max_iter = self.max_iter - self.warmup_iter
alpha = real_iter / real_max_iter
ratio = (1 - alpha) ** self.power
return ratio
def get_warmup_ratio(self):
assert self.warmup in ('linear', 'exp')
alpha = self.last_epoch / self.warmup_iter
if self.warmup == 'linear':
ratio = self.warmup_ratio + (1 - self.warmup_ratio) * alpha
elif self.warmup == 'exp':
ratio = self.warmup_ratio ** (1. - alpha)
return ratio
class WarmupCosineLrScheduler(_LRScheduler):
'''
This is different from official definition, this is implemented according to
the paper of fix-match
'''
def __init__(
self,
optimizer,
max_iter,
warmup_iter,
warmup_ratio=5e-4,
warmup='exp',
last_epoch=-1,
):
self.max_iter = max_iter
self.warmup_iter = warmup_iter
self.warmup_ratio = warmup_ratio
self.warmup = warmup
super(WarmupCosineLrScheduler, self).__init__(optimizer, last_epoch)
def get_lr(self):
ratio = self.get_lr_ratio()
lrs = [ratio * lr for lr in self.base_lrs]
return lrs
def get_lr_ratio(self):
if self.last_epoch < self.warmup_iter:
ratio = self.get_warmup_ratio()
else:
real_iter = self.last_epoch - self.warmup_iter
real_max_iter = self.max_iter - self.warmup_iter
ratio = np.cos((7 * np.pi * real_iter) / (16 * real_max_iter))
return ratio
def get_warmup_ratio(self):
assert self.warmup in ('linear', 'exp')
alpha = self.last_epoch / self.warmup_iter
if self.warmup == 'linear':
ratio = self.warmup_ratio + (1 - self.warmup_ratio) * alpha
elif self.warmup == 'exp':
ratio = self.warmup_ratio ** (1. - alpha)
return ratio
# from Fixmatch-pytorch
def get_cosine_schedule_with_warmup(optimizer,
num_warmup_steps,
num_training_steps,
num_cycles=7./16.,
last_epoch=-1):
def _lr_lambda(current_step):
if current_step < num_warmup_steps:
return float(current_step) / float(max(1, num_warmup_steps))
no_progress = float(current_step - num_warmup_steps) / float(max(1, num_training_steps - num_warmup_steps))
# return max(0., math.cos(math.pi * num_cycles * no_progress))
return max(0., (math.cos(math.pi * num_cycles * no_progress) + 1) * 0.5)
return LambdaLR(optimizer, _lr_lambda, last_epoch)
if __name__ == "__main__":
model = torch.nn.Conv2d(3, 16, 3, 1, 1)
optim = torch.optim.SGD(model.parameters(), lr=1e-3)
max_iter = 20000
# lr_scheduler = WarmupCosineLrScheduler(optim, max_iter, 0)
lr_scheduler = get_cosine_schedule_with_warmup(
optim, 0, max_iter)
lrs = []
for _ in range(max_iter):
lr = lr_scheduler.get_lr()[0]
print(lr)
lrs.append(lr)
lr_scheduler.step()
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
lrs = np.array(lrs)
n_lrs = len(lrs)
plt.plot(np.arange(n_lrs), lrs)
plt.title('3')
plt.grid()
plt.show()