8b7b61b3bd
* Adding Equations, solving typos * improve _code.rst * the team rst and restuctore index.rst * fixing errors --------- Co-authored-by: Dario Coscia <dariocoscia@dhcp-015.eduroam.sissa.it>
81 lines
3.4 KiB
Python
81 lines
3.4 KiB
Python
'''PINA Callbacks Implementations'''
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from pytorch_lightning.callbacks import Callback
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import torch
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from ..utils import check_consistency
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class SwitchOptimizer(Callback):
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def __init__(self, new_optimizers, new_optimizers_kwargs, epoch_switch):
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"""
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PINA Implementation of a Lightning Callback to switch optimizer during training.
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This callback allows for switching between different optimizers during training, enabling
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the exploration of multiple optimization strategies without the need to stop training.
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:param new_optimizers: The model optimizers to switch to. Can be a single
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:class:`torch.optim.Optimizer` or a list of them for multiple model solvers.
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:type new_optimizers: torch.optim.Optimizer | list
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:param new_optimizers_kwargs: The keyword arguments for the new optimizers. Can be a single dictionary
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or a list of dictionaries corresponding to each optimizer.
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:type new_optimizers_kwargs: dict | list
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:param epoch_switch: The epoch at which to switch to the new optimizer.
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:type epoch_switch: int
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:raises ValueError: If `epoch_switch` is less than 1 or if there is a mismatch in the number of
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optimizers and their corresponding keyword argument dictionaries.
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Example:
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>>> switch_callback = SwitchOptimizer(new_optimizers=[optimizer1, optimizer2],
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>>> new_optimizers_kwargs=[{'lr': 0.001}, {'lr': 0.01}],
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>>> epoch_switch=10)
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"""
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super().__init__()
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# check type consistency
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check_consistency(new_optimizers, torch.optim.Optimizer, subclass=True)
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check_consistency(new_optimizers_kwargs, dict)
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check_consistency(epoch_switch, int)
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if epoch_switch < 1:
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raise ValueError('epoch_switch must be greater than one.')
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if not isinstance(new_optimizers, list):
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new_optimizers = [new_optimizers]
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new_optimizers_kwargs = [new_optimizers_kwargs]
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len_optimizer = len(new_optimizers)
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len_optimizer_kwargs = len(new_optimizers_kwargs)
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if len_optimizer_kwargs != len_optimizer:
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raise ValueError('You must define one dictionary of keyword'
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' arguments for each optimizers.'
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f' Got {len_optimizer} optimizers, and'
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f' {len_optimizer_kwargs} dicitionaries')
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# save new optimizers
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self._new_optimizers = new_optimizers
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self._new_optimizers_kwargs = new_optimizers_kwargs
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self._epoch_switch = epoch_switch
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def on_train_epoch_start(self, trainer, __):
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"""
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Callback function to switch optimizer at the start of each training epoch.
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:param trainer: The trainer object managing the training process.
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:type trainer: pytorch_lightning.Trainer
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:param _: Placeholder argument (not used).
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:return: None
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:rtype: None
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"""
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if trainer.current_epoch == self._epoch_switch:
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optims = []
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for idx, (optim, optim_kwargs) in enumerate(
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zip(self._new_optimizers, self._new_optimizers_kwargs)):
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optims.append(
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optim(trainer._model.models[idx].parameters(),
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**optim_kwargs))
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trainer.optimizers = optims
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