minor fix

pina/callbacks/__init__.py

@@ -0,0 +1,7 @@
+__all__ = [
+    'SwitchOptimizer',
+    'R3Refinement',
+]
+
+from .optimizer_callbacks import SwitchOptimizer
+from .adaptive_refinment_callbacks import R3Refinement

pina/callbacks/adaptive_refinment_callbacks.py

@@ -2,65 +2,9 @@
 
 from lightning.pytorch.callbacks import Callback
 import torch
-from .utils import check_consistency
+from ..utils import check_consistency
 
 
-class SwitchOptimizer(Callback):
-    """
-    PINA implementation of a Lightining Callback to switch
-    optimizer during training. The rouutine can be used to
-    try multiple optimizers during the training, without the
-    need to stop training.
-    """
-    def __init__(self, new_optimizers, new_optimizers_kargs, epoch_switch):
-        """
-        SwitchOptimizer is a routine for switching optimizer during training.
-
-        :param torch.optim.Optimizer | list new_optimizers: The model optimizers to
-            switch to. It must be a list of :class:`torch.optim.Optimizer` or list of
-            :class:`torch.optim.Optimizer` for multiple model solvers.
-        :param dict| list new_optimizers: The model optimizers keyword arguments to
-            switch use. It must be a dict or list of dict for multiple optimizers.
-        :param int epoch_switch: Epoch for switching optimizer.
-        """
-        super().__init__()
-
-        # check type consistency
-        check_consistency(new_optimizers, torch.optim.Optimizer, subclass=True)
-        check_consistency(new_optimizers_kargs, 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):
-            optimizers = [new_optimizers]
-            optimizers_kwargs = [new_optimizers_kargs]
-        len_optimizer = len(optimizers)
-        len_optimizer_kwargs = len(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 = optimizers
-        self._new_optimizers_kwargs = optimizers_kwargs
-        self._epoch_switch = epoch_switch
-
-    def on_train_epoch_start(self, trainer, __):
-        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
-
 
 class R3Refinement(Callback):
     """

pina/callbacks/optimizer_callbacks.py

@@ -0,0 +1,62 @@
+'''PINA Callbacks Implementations'''
+
+from lightning.pytorch.callbacks import Callback
+import torch
+from ..utils import check_consistency
+
+
+class SwitchOptimizer(Callback):
+    """
+    PINA implementation of a Lightining Callback to switch
+    optimizer during training. The rouutine can be used to
+    try multiple optimizers during the training, without the
+    need to stop training.
+    """
+    def __init__(self, new_optimizers, new_optimizers_kwargs, epoch_switch):
+        """
+        SwitchOptimizer is a routine for switching optimizer during training.
+
+        :param torch.optim.Optimizer | list new_optimizers: The model optimizers to
+            switch to. It must be a list of :class:`torch.optim.Optimizer` or list of
+            :class:`torch.optim.Optimizer` for multiple model solvers.
+        :param dict| list new_optimizers: The model optimizers keyword arguments to
+            switch use. It must be a dict or list of dict for multiple optimizers.
+        :param int epoch_switch: Epoch for switching optimizer.
+        """
+        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
+
+    def on_train_epoch_start(self, trainer, __):
+        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