EasyCV/easycv/hooks/lr_update_hook.py

57 lines
2.1 KiB
Python

# Copyright (c) OpenMMLab. All rights reserved.
from mmcv.runner import HOOKS
from mmcv.runner.hooks.lr_updater import (CosineAnnealingLrUpdaterHook,
annealing_cos)
@HOOKS.register_module()
class StepFixCosineAnnealingLrUpdaterHook(CosineAnnealingLrUpdaterHook):
def get_warmup_lr(self, cur_iters):
def _get_warmup_lr(cur_iters, regular_lr):
if self.warmup == 'constant':
warmup_lr = [_lr * self.warmup_ratio for _lr in regular_lr]
elif self.warmup == 'linear':
k = (1 - cur_iters / self.warmup_iters)
warmup_lr = [_lr * (1 - k) for _lr in regular_lr]
elif self.warmup == 'exp':
k = self.warmup_ratio**(1 - cur_iters / self.warmup_iters)
warmup_lr = [_lr * k for _lr in regular_lr]
return warmup_lr
if isinstance(self.regular_lr, dict):
lr_groups = {}
for key, regular_lr in self.regular_lr.items():
lr_groups[key] = _get_warmup_lr(cur_iters, regular_lr)
return lr_groups
else:
return _get_warmup_lr(cur_iters, self.regular_lr)
def get_lr(self, runner, base_lr):
if self.by_epoch:
progress = runner.epoch
max_progress = runner.max_epochs
# Delete warmup epochs
if self.warmup is not None:
progress = progress - self.warmup_iters // len(
runner.data_loader)
max_progress = max_progress - self.warmup_iters // len(
runner.data_loader)
else:
progress = runner.iter
max_progress = runner.max_iters
# Delete warmup iters
if self.warmup is not None:
progress = progress - self.warmup_iters
max_progress = max_progress - self.warmup_iters
if self.min_lr_ratio is not None:
target_lr = base_lr * self.min_lr_ratio
else:
target_lr = self.min_lr
return annealing_cos(base_lr, target_lr, progress / max_progress)