Source code for pina.callbacks.optimizer_callbacks

"""PINA Callbacks Implementations"""

from pytorch_lightning.callbacks import Callback
import torch
from ..utils import check_consistency


[docs] class SwitchOptimizer(Callback): def __init__(self, new_optimizers, new_optimizers_kwargs, epoch_switch): """ PINA Implementation of a Lightning Callback to switch optimizer during training. This callback allows for switching between different optimizers during training, enabling the exploration of multiple optimization strategies without the need to stop training. :param new_optimizers: The model optimizers to switch to. Can be a single :class:`torch.optim.Optimizer` or a list of them for multiple model solvers. :type new_optimizers: torch.optim.Optimizer | list :param new_optimizers_kwargs: The keyword arguments for the new optimizers. Can be a single dictionary or a list of dictionaries corresponding to each optimizer. :type new_optimizers_kwargs: dict | list :param epoch_switch: The epoch at which to switch to the new optimizer. :type epoch_switch: int :raises ValueError: If `epoch_switch` is less than 1 or if there is a mismatch in the number of optimizers and their corresponding keyword argument dictionaries. Example: >>> switch_callback = SwitchOptimizer(new_optimizers=[optimizer1, optimizer2], >>> new_optimizers_kwargs=[{'lr': 0.001}, {'lr': 0.01}], >>> epoch_switch=10) """ super().__init__() # check type consistency check_consistency(new_optimizers, torch.optim.Optimizer, subclass=True) check_consistency(new_optimizers_kwargs, dict) check_consistency(epoch_switch, int) if epoch_switch < 1: raise ValueError("epoch_switch must be greater than one.") if not isinstance(new_optimizers, list): new_optimizers = [new_optimizers] new_optimizers_kwargs = [new_optimizers_kwargs] len_optimizer = len(new_optimizers) len_optimizer_kwargs = len(new_optimizers_kwargs) if len_optimizer_kwargs != len_optimizer: raise ValueError( "You must define one dictionary of keyword" " arguments for each optimizers." f" Got {len_optimizer} optimizers, and" f" {len_optimizer_kwargs} dicitionaries" ) # save new optimizers self._new_optimizers = new_optimizers self._new_optimizers_kwargs = new_optimizers_kwargs self._epoch_switch = epoch_switch
[docs] def on_train_epoch_start(self, trainer, __): """ Callback function to switch optimizer at the start of each training epoch. :param trainer: The trainer object managing the training process. :type trainer: pytorch_lightning.Trainer :param _: Placeholder argument (not used). :return: None :rtype: None """ if trainer.current_epoch == self._epoch_switch: optims = [] for idx, (optim, optim_kwargs) in enumerate( zip(self._new_optimizers, self._new_optimizers_kwargs) ): optims.append( optim( trainer._model.models[idx].parameters(), **optim_kwargs ) ) trainer.optimizers = optims