Renaming

* solvers -> solver
* adaptive_functions -> adaptive_function
* callbacks -> callback
* operators -> operator
* pinns -> physics_informed_solver
* layers -> block

pina/callback/optimizer_callback.py

@@ -0,0 +1,65 @@
+"""PINA Callbacks Implementations"""
+
+from lightning.pytorch.callbacks import Callback
+import torch
+from ..utils import check_consistency
+from pina.optim import TorchOptimizer
+
+
+class SwitchOptimizer(Callback):
+
+    def __init__(self, new_optimizers, 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 solver.
+        :type new_optimizers: pina.optim.TorchOptimizer | list
+        :param epoch_switch: The epoch at which to switch to the new optimizer.
+        :type epoch_switch: int
+
+        Example:
+            >>> switch_callback = SwitchOptimizer(new_optimizers=optimizer,
+            >>>                                  epoch_switch=10)
+        """
+        super().__init__()
+
+        if epoch_switch < 1:
+            raise ValueError("epoch_switch must be greater than one.")
+
+        if not isinstance(new_optimizers, list):
+            new_optimizers = [new_optimizers]
+
+        # check type consistency
+        for optimizer in new_optimizers:
+            check_consistency(optimizer, TorchOptimizer)
+        check_consistency(epoch_switch, int)
+        # save new optimizers
+        self._new_optimizers = new_optimizers
+        self._epoch_switch = epoch_switch
+
+    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 in enumerate(self._new_optimizers):
+                optim.hook(trainer.solver.models[idx].parameters())
+                optims.append(optim.instance)
+
+            trainer.optimizers = optims