Source code for catalyst.dl.callbacks.scheduler

from typing import Optional

from catalyst.core.callbacks import LRUpdater
from catalyst.dl import State

[docs]class LRFinder(LRUpdater): """ Helps you find an optimal learning rate for a model, as per suggestion of `Cyclical Learning Rates for Training Neural Networks`_ paper. Learning rate is increased in linear or log scale, depending on user input. See `How Do You Find A Good Learning Rate`_ article for details. .. _Cyclical Learning Rates for Training Neural Networks: .. _How Do You Find A Good Learning Rate: """
[docs] def __init__( self, final_lr, scale: str = "log", num_steps: Optional[int] = None, optimizer_key: str = None, ): """ Args: final_lr: final learning rate to try with scale (str): learning rate increasing scale ("log" or "linear") num_steps (Optional[int]): number of batches to try; if None - whole loader would be used. optimizer_key (str): which optimizer key to use for learning rate scheduling """ super().__init__(optimizer_key=optimizer_key) self.final_lr = final_lr self.scale = scale self.num_steps = num_steps self.multiplier = 0 self.lr_step = 0 self.find_iter = 0 self._calc_lr = None if scale == "log": self._calc_lr = self._calc_lr_log elif scale == "linear": self._calc_lr = self._calc_lr_linear else: raise Exception("Not supported")
def _calc_lr_log(self): return self.init_lr * self.multiplier ** self.find_iter def _calc_lr_linear(self): return self.init_lr + self.lr_step * self.find_iter
[docs] def calc_lr(self): """@TODO: Docs. Contribution is welcome.""" res = self._calc_lr() self.find_iter += 1 return res
[docs] def on_loader_start(self, state: State): """@TODO: Docs. Contribution is welcome. Args: state (State): current state """ if state.is_train_loader: lr_ = self.final_lr / self.init_lr self.num_steps = self.num_steps or state.loader_len self.multiplier = lr_ ** (1 / self.num_steps) self.lr_step = (self.final_lr - self.init_lr) / self.num_steps super().on_loader_start(state=state)
[docs] def on_batch_end(self, state: State): """@TODO: Docs. Contribution is welcome. Args: state (State): current state """ super().on_batch_end(state=state) if self.find_iter > self.num_steps: raise NotImplementedError("End of LRFinder")
__all__ = ["LRFinder"]