Note

You are reading the documentation for MMSelfSup 0.x, which will soon be deprecated by the end of 2022. We recommend you upgrade to MMSelfSup 1.0.0rc versions to enjoy fruitful new features and better performance brought by OpenMMLab 2.0. Check out the changelog, code and documentation of MMSelfSup 1.0.0rc for more details.

Source code for mmselfsup.core.hooks.cosine_annealing_hook

# 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)