Shortcuts

Note

You are reading the documentation for MMClassification 0.x, which will soon be deprecated at the end of 2022. We recommend you upgrade to MMClassification 1.0 to enjoy fruitful new features and better performance brought by OpenMMLab 2.0. Check the installation tutorial, migration tutorial and changelog for more details.

Source code for mmcls.core.hook.lr_updater

# Copyright (c) OpenMMLab. All rights reserved.
from math import cos, pi

from mmcv.runner.hooks import HOOKS, LrUpdaterHook


[docs]@HOOKS.register_module() class CosineAnnealingCooldownLrUpdaterHook(LrUpdaterHook): """Cosine annealing learning rate scheduler with cooldown. Args: min_lr (float, optional): The minimum learning rate after annealing. Defaults to None. min_lr_ratio (float, optional): The minimum learning ratio after nnealing. Defaults to None. cool_down_ratio (float): The cooldown ratio. Defaults to 0.1. cool_down_time (int): The cooldown time. Defaults to 10. by_epoch (bool): If True, the learning rate changes epoch by epoch. If False, the learning rate changes iter by iter. Defaults to True. warmup (string, optional): Type of warmup used. It can be None (use no warmup), 'constant', 'linear' or 'exp'. Defaults to None. warmup_iters (int): The number of iterations or epochs that warmup lasts. Defaults to 0. warmup_ratio (float): LR used at the beginning of warmup equals to ``warmup_ratio * initial_lr``. Defaults to 0.1. warmup_by_epoch (bool): If True, the ``warmup_iters`` means the number of epochs that warmup lasts, otherwise means the number of iteration that warmup lasts. Defaults to False. Note: You need to set one and only one of ``min_lr`` and ``min_lr_ratio``. """ def __init__(self, min_lr=None, min_lr_ratio=None, cool_down_ratio=0.1, cool_down_time=10, **kwargs): assert (min_lr is None) ^ (min_lr_ratio is None) self.min_lr = min_lr self.min_lr_ratio = min_lr_ratio self.cool_down_time = cool_down_time self.cool_down_ratio = cool_down_ratio super(CosineAnnealingCooldownLrUpdaterHook, self).__init__(**kwargs) def get_lr(self, runner, base_lr): if self.by_epoch: progress = runner.epoch max_progress = runner.max_epochs else: progress = runner.iter max_progress = runner.max_iters 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 - self.cool_down_time: return target_lr * self.cool_down_ratio else: max_progress = max_progress - self.cool_down_time return annealing_cos(base_lr, target_lr, progress / max_progress)
def annealing_cos(start, end, factor, weight=1): """Calculate annealing cos learning rate. Cosine anneal from `weight * start + (1 - weight) * end` to `end` as percentage goes from 0.0 to 1.0. Args: start (float): The starting learning rate of the cosine annealing. end (float): The ending learing rate of the cosine annealing. factor (float): The coefficient of `pi` when calculating the current percentage. Range from 0.0 to 1.0. weight (float, optional): The combination factor of `start` and `end` when calculating the actual starting learning rate. Default to 1. """ cos_out = cos(pi * factor) + 1 return end + 0.5 * weight * (start - end) * cos_out
Read the Docs v: latest
Versions
master
latest
1.x
dev-1.x
Downloads
html
epub
On Read the Docs
Project Home
Builds

Free document hosting provided by Read the Docs.