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