#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
#####################################################
import math, torch
import torch.nn as nn
from bisect import bisect_right
from torch.optim import Optimizer
class _LRScheduler(object):
def __init__(self, optimizer, warmup_epochs, epochs):
if not isinstance(optimizer, Optimizer):
raise TypeError("{:} is not an Optimizer".format(type(optimizer).__name__))
self.optimizer = optimizer
for group in optimizer.param_groups:
group.setdefault("initial_lr", group["lr"])
self.base_lrs = list(
map(lambda group: group["initial_lr"], optimizer.param_groups)
)
self.max_epochs = epochs
self.warmup_epochs = warmup_epochs
self.current_epoch = 0
self.current_iter = 0
def extra_repr(self):
return ""
def __repr__(self):
return "{name}(warmup={warmup_epochs}, max-epoch={max_epochs}, current::epoch={current_epoch}, iter={current_iter:.2f}".format(
name=self.__class__.__name__, **self.__dict__
) + ", {:})".format(
self.extra_repr()
)
def state_dict(self):
return {
key: value for key, value in self.__dict__.items() if key != "optimizer"
}
def load_state_dict(self, state_dict):
self.__dict__.update(state_dict)
def get_lr(self):
raise NotImplementedError
def get_min_info(self):
lrs = self.get_lr()
return "#LR=[{:.6f}~{:.6f}] epoch={:03d}, iter={:4.2f}#".format(
min(lrs), max(lrs), self.current_epoch, self.current_iter
)
def get_min_lr(self):
return min(self.get_lr())
def update(self, cur_epoch, cur_iter):
if cur_epoch is not None:
assert (
isinstance(cur_epoch, int) and cur_epoch >= 0
), "invalid cur-epoch : {:}".format(cur_epoch)
self.current_epoch = cur_epoch
if cur_iter is not None:
assert (
isinstance(cur_iter, float) and cur_iter >= 0
), "invalid cur-iter : {:}".format(cur_iter)
self.current_iter = cur_iter
for param_group, lr in zip(self.optimizer.param_groups, self.get_lr()):
param_group["lr"] = lr
class CosineAnnealingLR(_LRScheduler):
def __init__(self, optimizer, warmup_epochs, epochs, T_max, eta_min):
self.T_max = T_max
self.eta_min = eta_min
super(CosineAnnealingLR, self).__init__(optimizer, warmup_epochs, epochs)
def extra_repr(self):
return "type={:}, T-max={:}, eta-min={:}".format(
"cosine", self.T_max, self.eta_min
)
def get_lr(self):
lrs = []
for base_lr in self.base_lrs:
if (
self.current_epoch >= self.warmup_epochs
and self.current_epoch < self.max_epochs
):
last_epoch = self.current_epoch - self.warmup_epochs
# if last_epoch < self.T_max:
# if last_epoch < self.max_epochs:
lr = (
self.eta_min
+ (base_lr - self.eta_min)
* (1 + math.cos(math.pi * last_epoch / self.T_max))
/ 2
)
# else:
# lr = self.eta_min + (base_lr - self.eta_min) * (1 + math.cos(math.pi * (self.T_max-1.0) / self.T_max)) / 2
elif self.current_epoch >= self.max_epochs:
lr = self.eta_min
else:
lr = (
self.current_epoch / self.warmup_epochs
+ self.current_iter / self.warmup_epochs
) * base_lr
lrs.append(lr)
return lrs
class MultiStepLR(_LRScheduler):
def __init__(self, optimizer, warmup_epochs, epochs, milestones, gammas):
assert len(milestones) == len(gammas), "invalid {:} vs {:}".format(
len(milestones), len(gammas)
)
self.milestones = milestones
self.gammas = gammas
super(MultiStepLR, self).__init__(optimizer, warmup_epochs, epochs)
def extra_repr(self):
return "type={:}, milestones={:}, gammas={:}, base-lrs={:}".format(
"multistep", self.milestones, self.gammas, self.base_lrs
)
def get_lr(self):
lrs = []
for base_lr in self.base_lrs:
if self.current_epoch >= self.warmup_epochs:
last_epoch = self.current_epoch - self.warmup_epochs
idx = bisect_right(self.milestones, last_epoch)
lr = base_lr
for x in self.gammas[:idx]:
lr *= x
else:
lr = (
self.current_epoch / self.warmup_epochs
+ self.current_iter / self.warmup_epochs
) * base_lr
lrs.append(lr)
return lrs
class ExponentialLR(_LRScheduler):
def __init__(self, optimizer, warmup_epochs, epochs, gamma):
self.gamma = gamma
super(ExponentialLR, self).__init__(optimizer, warmup_epochs, epochs)
def extra_repr(self):
return "type={:}, gamma={:}, base-lrs={:}".format(
"exponential", self.gamma, self.base_lrs
)
def get_lr(self):
lrs = []
for base_lr in self.base_lrs:
if self.current_epoch >= self.warmup_epochs:
last_epoch = self.current_epoch - self.warmup_epochs
assert last_epoch >= 0, "invalid last_epoch : {:}".format(last_epoch)
lr = base_lr * (self.gamma ** last_epoch)
else:
lr = (
self.current_epoch / self.warmup_epochs
+ self.current_iter / self.warmup_epochs
) * base_lr
lrs.append(lr)
return lrs
class LinearLR(_LRScheduler):
def __init__(self, optimizer, warmup_epochs, epochs, max_LR, min_LR):
self.max_LR = max_LR
self.min_LR = min_LR
super(LinearLR, self).__init__(optimizer, warmup_epochs, epochs)
def extra_repr(self):
return "type={:}, max_LR={:}, min_LR={:}, base-lrs={:}".format(
"LinearLR", self.max_LR, self.min_LR, self.base_lrs
)
def get_lr(self):
lrs = []
for base_lr in self.base_lrs:
if self.current_epoch >= self.warmup_epochs:
last_epoch = self.current_epoch - self.warmup_epochs
assert last_epoch >= 0, "invalid last_epoch : {:}".format(last_epoch)
ratio = (
(self.max_LR - self.min_LR)
* last_epoch
/ self.max_epochs
/ self.max_LR
)
lr = base_lr * (1 - ratio)
else:
lr = (
self.current_epoch / self.warmup_epochs
+ self.current_iter / self.warmup_epochs
) * base_lr
lrs.append(lr)
return lrs
class CrossEntropyLabelSmooth(nn.Module):
def __init__(self, num_classes, epsilon):
super(CrossEntropyLabelSmooth, self).__init__()
self.num_classes = num_classes
self.epsilon = epsilon
self.logsoftmax = nn.LogSoftmax(dim=1)
def forward(self, inputs, targets):
log_probs = self.logsoftmax(inputs)
targets = torch.zeros_like(log_probs).scatter_(1, targets.unsqueeze(1), 1)
targets = (1 - self.epsilon) * targets + self.epsilon / self.num_classes
loss = (-targets * log_probs).mean(0).sum()
return loss
def get_optim_scheduler(parameters, config):
assert (
hasattr(config, "optim")
and hasattr(config, "scheduler")
and hasattr(config, "criterion")
), "config must have optim / scheduler / criterion keys instead of {:}".format(
config
)
if config.optim == "SGD":
optim = torch.optim.SGD(
parameters,
config.LR,
momentum=config.momentum,
weight_decay=config.decay,
nesterov=config.nesterov,
)
elif config.optim == "RMSprop":
optim = torch.optim.RMSprop(
parameters, config.LR, momentum=config.momentum, weight_decay=config.decay
)
else:
raise ValueError("invalid optim : {:}".format(config.optim))
if config.scheduler == "cos":
T_max = getattr(config, "T_max", config.epochs)
scheduler = CosineAnnealingLR(
optim, config.warmup, config.epochs, T_max, config.eta_min
)
elif config.scheduler == "multistep":
scheduler = MultiStepLR(
optim, config.warmup, config.epochs, config.milestones, config.gammas
)
elif config.scheduler == "exponential":
scheduler = ExponentialLR(optim, config.warmup, config.epochs, config.gamma)
elif config.scheduler == "linear":
scheduler = LinearLR(
optim, config.warmup, config.epochs, config.LR, config.LR_min
)
else:
raise ValueError("invalid scheduler : {:}".format(config.scheduler))
if config.criterion == "Softmax":
criterion = torch.nn.CrossEntropyLoss()
elif config.criterion == "SmoothSoftmax":
criterion = CrossEntropyLabelSmooth(config.class_num, config.label_smooth)
else:
raise ValueError("invalid criterion : {:}".format(config.criterion))
return optim, scheduler, criterion