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Source code for mmedit.core.scheduler.lr_updater

# Copyright (c) OpenMMLab. All rights reserved.
from mmcv.runner import HOOKS, LrUpdaterHook


[docs]@HOOKS.register_module() class LinearLrUpdaterHook(LrUpdaterHook): """Linear learning rate scheduler for image generation. In the beginning, the learning rate is 'base_lr' defined in mmcv. We give a target learning rate 'target_lr' and a start point 'start' (iteration / epoch). Before 'start', we fix learning rate as 'base_lr'; After 'start', we linearly update learning rate to 'target_lr'. Args: target_lr (float): The target learning rate. Default: 0. start (int): The start point (iteration / epoch, specified by args 'by_epoch' in its parent class in mmcv) to update learning rate. Default: 0. interval (int): The interval to update the learning rate. Default: 1. """ def __init__(self, target_lr=0, start=0, interval=1, **kwargs): super().__init__(**kwargs) self.target_lr = target_lr self.start = start self.interval = interval
[docs] def get_lr(self, runner, base_lr): """Calculates the learning rate. Args: runner (object): The passed runner. base_lr (float): Base learning rate. Returns: float: Current learning rate. """ if self.by_epoch: progress = runner.epoch max_progress = runner.max_epochs else: progress = runner.iter max_progress = runner.max_iters assert max_progress >= self.start if max_progress == self.start: return base_lr # Before 'start', fix lr; After 'start', linearly update lr. factor = (max(0, progress - self.start) // self.interval) / ( (max_progress - self.start) // self.interval) return base_lr + (self.target_lr - base_lr) * factor
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