a  þdtã@sLddlZddlmZddlmZGdd„deƒZdd„ZGdd „d eƒZdS) éN)ÚCounter)Ú _LRSchedulercs*eZdZdZd ‡fdd„ Zdd „Z‡ZS) ÚMultiStepRestartLRaÑ MultiStep with restarts learning rate scheme. Args: optimizer (torch.nn.optimizer): Torch optimizer. milestones (list): Iterations that will decrease learning rate. gamma (float): Decrease ratio. Default: 0.1. restarts (list): Restart iterations. Default: [0]. restart_weights (list): Restart weights at each restart iteration. Default: [1]. last_epoch (int): Used in _LRScheduler. Default: -1. çš™™™™™¹?©r©ééÿÿÿÿcsNt|ƒ|_||_||_||_t|jƒt|jƒks8Jdƒ‚tt|ƒ ||¡dS)Nz(restarts and their weights do not match.) rÚ milestonesÚgammaÚrestartsÚrestart_weightsÚlenÚsuperrÚ__init__)ÚselfÚ optimizerr r r r Ú last_epoch©Ú __class__©úd/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/basicsr/models/lr_scheduler.pyrs  zMultiStepRestartLR.__init__csjˆjˆjvr6ˆjˆj ˆj¡‰‡fdd„ˆjjDƒSˆjˆjvrTdd„ˆjjDƒS‡fdd„ˆjjDƒS)Ncsg|]}|dˆ‘qS)Z initial_lrr©Ú.0Úgroup)ÚweightrrÚ óz-MultiStepRestartLR.get_lr..cSsg|] }|d‘qS©Úlrrrrrrr rcs&g|]}|dˆjˆjˆj‘qSr)r r rr©rrrr!r)rr r ÚindexrZ param_groupsr r r)rrrÚget_lrs   zMultiStepRestartLR.get_lr)rrrr ©Ú__name__Ú __module__Ú __qualname__Ú__doc__rr"Ú __classcell__rrrrrs rcCs&t|ƒD]\}}||kr|SqdS)aìGet the position from a period list. It will return the index of the right-closest number in the period list. For example, the cumulative_period = [100, 200, 300, 400], if iteration == 50, return 0; if iteration == 210, return 2; if iteration == 300, return 2. Args: iteration (int): Current iteration. cumulative_period (list[int]): Cumulative period list. Returns: int: The position of the right-closest number in the period list. N)Ú enumerate)Ú iterationÚcumulative_periodÚiZperiodrrrÚget_position_from_periods$sr-cs*eZdZdZd ‡fdd„ Zdd„Z‡ZS) ÚCosineAnnealingRestartLRa’ Cosine annealing with restarts learning rate scheme. An example of config: periods = [10, 10, 10, 10] restart_weights = [1, 0.5, 0.5, 0.5] eta_min=1e-7 It has four cycles, each has 10 iterations. At 10th, 20th, 30th, the scheduler will restart with the weights in restart_weights. Args: optimizer (torch.nn.optimizer): Torch optimizer. periods (list): Period for each cosine anneling cycle. restart_weights (list): Restart weights at each restart iteration. Default: [1]. eta_min (float): The minimum lr. Default: 0. last_epoch (int): Used in _LRScheduler. Default: -1. rrr csd|ˆ_|ˆ_|ˆ_tˆjƒtˆjƒks.Jdƒ‚‡fdd„tdtˆjƒƒDƒˆ_ttˆƒ ||¡dS)Nz8periods and restart_weights should have the same length.cs"g|]}tˆjd|d…ƒ‘qS)rr)ÚsumÚperiods)rr,r rrrSrz5CosineAnnealingRestartLR.__init__..r) r0r Úeta_minrÚranger+rr.r)rrr0r r1rrr rrMs ÿÿ z!CosineAnnealingRestartLR.__init__csVtˆjˆjƒ}ˆj|‰|dkr$dn ˆj|d‰ˆj|‰‡‡‡‡fdd„ˆjDƒS)Nrrc sBg|]:}ˆjˆd|ˆjdt tjˆjˆˆ¡‘qS)gà?r)r1ÚmathÚcosÚpir)rZbase_lr©Zcurrent_periodZcurrent_weightZnearest_restartrrrr\sþÿz3CosineAnnealingRestartLR.get_lr..)r-rr+r r0Zbase_lrs)rÚidxrr6rr"Vs  ýzCosineAnnealingRestartLR.get_lr)rrr r#rrrrr.9s r.)r3Ú collectionsrZtorch.optim.lr_schedulerrrr-r.rrrrÚs