Spaces:
Sleeping
Sleeping
File size: 5,608 Bytes
749745d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from bisect import bisect_right
import math
import torch
# FIXME ideally this would be achieved with a CombinedLRScheduler,
# separating MultiStepLR with WarmupLR
# but the current LRScheduler design doesn't allow it
class WarmupMultiStepLR(torch.optim.lr_scheduler._LRScheduler):
def __init__(
self,
optimizer,
milestones,
gamma=0.1,
warmup_factor=1.0 / 3,
warmup_iters=500,
warmup_method="linear",
last_epoch=-1,
):
if not list(milestones) == sorted(milestones):
raise ValueError(
"Milestones should be a list of" " increasing integers. Got {}",
milestones,
)
if warmup_method not in ("constant", "linear"):
raise ValueError("Only 'constant' or 'linear' warmup_method accepted" "got {}".format(warmup_method))
self.milestones = milestones
self.gamma = gamma
self.warmup_factor = warmup_factor
self.warmup_iters = warmup_iters
self.warmup_method = warmup_method
super(WarmupMultiStepLR, self).__init__(optimizer, last_epoch)
def get_lr(self):
warmup_factor = 1
if self.last_epoch < self.warmup_iters:
if self.warmup_method == "constant":
warmup_factor = self.warmup_factor
elif self.warmup_method == "linear":
alpha = float(self.last_epoch) / self.warmup_iters
warmup_factor = self.warmup_factor * (1 - alpha) + alpha
return [
base_lr * warmup_factor * self.gamma ** bisect_right(self.milestones, self.last_epoch)
for base_lr in self.base_lrs
]
class WarmupCosineAnnealingLR(torch.optim.lr_scheduler._LRScheduler):
def __init__(
self,
optimizer,
max_iters,
gamma=0.1,
warmup_factor=1.0 / 3,
warmup_iters=500,
warmup_method="linear",
eta_min=0,
last_epoch=-1,
):
if warmup_method not in ("constant", "linear"):
raise ValueError("Only 'constant' or 'linear' warmup_method accepted" "got {}".format(warmup_method))
self.max_iters = max_iters
self.gamma = gamma
self.warmup_factor = warmup_factor
self.warmup_iters = warmup_iters
self.warmup_method = warmup_method
self.eta_min = eta_min
super(WarmupCosineAnnealingLR, self).__init__(optimizer, last_epoch)
def get_lr(self):
warmup_factor = 1
if self.last_epoch < self.warmup_iters:
if self.warmup_method == "constant":
warmup_factor = self.warmup_factor
elif self.warmup_method == "linear":
alpha = float(self.last_epoch) / self.warmup_iters
warmup_factor = self.warmup_factor * (1 - alpha) + alpha
return [base_lr * warmup_factor for base_lr in self.base_lrs]
else:
return [
self.eta_min
+ (base_lr - self.eta_min)
* (1 + math.cos(math.pi * (self.last_epoch - self.warmup_iters) / self.max_iters))
/ 2
for base_lr in self.base_lrs
]
class WarmupReduceLROnPlateau(torch.optim.lr_scheduler.ReduceLROnPlateau):
def __init__(
self,
optimizer,
max_iters,
gamma=0.1,
warmup_factor=1.0 / 3,
warmup_iters=500,
warmup_method="linear",
eta_min=0,
last_epoch=-1,
patience=5,
verbose=False,
):
if warmup_method not in ("constant", "linear"):
raise ValueError("Only 'constant' or 'linear' warmup_method accepted" "got {}".format(warmup_method))
self.warmup_factor = warmup_factor
self.warmup_iters = warmup_iters
self.warmup_method = warmup_method
self.eta_min = eta_min
if last_epoch == -1:
for group in optimizer.param_groups:
group.setdefault("initial_lr", group["lr"])
else:
for i, group in enumerate(optimizer.param_groups):
if "initial_lr" not in group:
raise KeyError(
"param 'initial_lr' is not specified "
"in param_groups[{}] when resuming an optimizer".format(i)
)
self.base_lrs = list(map(lambda group: group["initial_lr"], optimizer.param_groups))
super(WarmupReduceLROnPlateau, self).__init__(
optimizer, factor=gamma, patience=patience, mode="max", min_lr=eta_min, verbose=verbose
)
def step(self, metrics=None):
warmup_factor = 1
if self.last_epoch < self.warmup_iters:
if self.warmup_method == "constant":
warmup_factor = self.warmup_factor
elif self.warmup_method == "linear":
alpha = float(self.last_epoch) / self.warmup_iters
warmup_factor = self.warmup_factor * (1 - alpha) + alpha
if self.last_epoch >= self.warmup_iters - 1:
warmup_factor = 1.0
warmup_lrs = [base_lr * warmup_factor for base_lr in self.base_lrs]
for param_group, lr in zip(self.optimizer.param_groups, warmup_lrs):
param_group["lr"] = lr
self.last_epoch += 1
elif metrics:
super().step(metrics)
|