# Copyright (c) Alibaba, Inc. and its affiliates.
from mmcv.runner.hooks import HOOKS
from mmcv.runner.hooks.lr_updater import (CosineAnnealingLrUpdaterHook,
annealing_cos)
[docs]@HOOKS.register_module(force=True)
class YOLOXLrUpdaterHook(CosineAnnealingLrUpdaterHook):
"""YOLOX learning rate scheme.
There are two main differences between YOLOXLrUpdaterHook
and CosineAnnealingLrUpdaterHook.
1. When the current running epoch is greater than
`max_epoch-last_epoch`, a fixed learning rate will be used
2. The exp warmup scheme is different with LrUpdaterHook in MMCV
Args:
num_last_epochs (int): The number of epochs with a fixed learning rate
before the end of the training.
"""
[docs] def __init__(self, num_last_epochs, **kwargs):
self.num_last_epochs = num_last_epochs
super(YOLOXLrUpdaterHook, self).__init__(**kwargs)
[docs] def get_warmup_lr(self, cur_iters):
def _get_warmup_lr(cur_iters, regular_lr):
# exp warmup scheme
k = self.warmup_ratio * pow(
(cur_iters + 1) / float(self.warmup_iters), 2)
warmup_lr = [_lr * k for _lr in regular_lr]
return warmup_lr
if isinstance(self.base_lr, dict):
lr_groups = {}
for key, base_lr in self.base_lr.items():
lr_groups[key] = _get_warmup_lr(cur_iters, base_lr)
return lr_groups
else:
return _get_warmup_lr(cur_iters, self.base_lr)
[docs] def get_lr(self, runner, base_lr):
last_iter = len(runner.data_loader) * self.num_last_epochs
if self.by_epoch:
progress = runner.epoch
max_progress = runner.max_epochs
else:
progress = runner.iter
max_progress = runner.max_iters
progress += 1
if self.min_lr_ratio is not None:
target_lr = base_lr * self.min_lr_ratio
else:
target_lr = self.min_lr
if progress >= max_progress - last_iter:
# fixed learning rate
return target_lr
else:
assert max_progress > (self.warmup_iters + last_iter
), 'Please increase the train epoch'
return annealing_cos(
base_lr, target_lr, (progress - self.warmup_iters) /
(max_progress - self.warmup_iters - last_iter))