Callback module restructuring

docs/source/_rst/_code.rst

@@ -254,12 +254,13 @@ Callbacks
 .. toctree::
     :titlesonly:
 
-    Processing callback <callback/processing_callback.rst>
-    Optimizer callback <callback/optimizer_callback.rst>
-    Switch Scheduler <callback/switch_scheduler.rst>
-    R3 Refinment callback <callback/refinement/r3_refinement.rst>
-    Refinment Interface callback <callback/refinement/refinement_interface.rst>
-    Normalizer callback <callback/normalizer_data_callback.rst>
+    Switch Optimizer <callback/optim/switch_optimizer.rst>
+    Switch Scheduler <callback/optim/switch_scheduler.rst>
+    Normalizer Data <callback/processing/normalizer_data_callback.rst>
+    PINA Progress Bar <callback/processing/pina_progress_bar.rst>
+    Metric Tracker <callback/processing/metric_tracker.rst>
+    Refinement Interface <callback/refinement/refinement_interface.rst>
+    R3 Refinement <callback/refinement/r3_refinement.rst>
 
 Losses and Weightings
 ---------------------

docs/source/_rst/callback/optimizer_callback.rst → docs/source/_rst/callback/optim/switch_optimizer.rst

@@ -1,7 +1,7 @@
-Optimizer callbacks
+Switch Optimizer
 =====================
 
-.. currentmodule:: pina.callback.optimizer_callback
+.. currentmodule:: pina.callback.optim.switch_optimizer
 .. autoclass:: SwitchOptimizer
    :members:
    :show-inheritance:

docs/source/_rst/callback/switch_scheduler.rst → docs/source/_rst/callback/optim/switch_scheduler.rst

@@ -1,7 +1,7 @@
 Switch Scheduler
 =====================
 
-.. currentmodule:: pina.callback.switch_scheduler
+.. currentmodule:: pina.callback.optim.switch_scheduler
 .. autoclass:: SwitchScheduler
    :members:
    :show-inheritance:

docs/source/_rst/callback/processing/metric_tracker.rst

@@ -0,0 +1,7 @@
+Metric Tracker
+==================
+.. currentmodule:: pina.callback.processing.metric_tracker
+
+.. autoclass:: MetricTracker
+   :members:
+   :show-inheritance:

docs/source/_rst/callback/normalizer_data_callback.rst → docs/source/_rst/callback/processing/normalizer_data_callback.rst

@@ -1,7 +1,7 @@
-Normalizer callbacks
+Normalizer Data
 =======================
 
-.. currentmodule:: pina.callback.normalizer_data_callback
+.. currentmodule:: pina.callback.processing.normalizer_data_callback
 .. autoclass:: NormalizerDataCallback
    :members:
    :show-inheritance:

docs/source/_rst/callback/processing/pina_progress_bar.rst

@@ -0,0 +1,7 @@
+PINA Progress Bar
+==================
+.. currentmodule:: pina.callback.processing.pina_progress_bar
+
+.. autoclass:: PINAProgressBar
+   :members:
+   :show-inheritance:

pina/callback/__init__.py

@@ -3,14 +3,15 @@
 __all__ = [
     "SwitchOptimizer",
     "SwitchScheduler",
-    "MetricTracker",
+    "NormalizerDataCallback",
     "PINAProgressBar",
+    "MetricTracker",
     "R3Refinement",
-    "NormalizerDataCallback",
 ]
 
-from .optimizer_callback import SwitchOptimizer
-from .processing_callback import MetricTracker, PINAProgressBar
+from .optim.switch_optimizer import SwitchOptimizer
+from .optim.switch_scheduler import SwitchScheduler
+from .processing.normalizer_data_callback import NormalizerDataCallback
+from .processing.pina_progress_bar import PINAProgressBar
+from .processing.metric_tracker import MetricTracker
 from .refinement import R3Refinement
-from .normalizer_data_callback import NormalizerDataCallback
-from .switch_scheduler import SwitchScheduler

pina/callback/optimizer_callback.py → pina/callback/optim/switch_optimizer.py

@@ -1,8 +1,8 @@
 """Module for the SwitchOptimizer callback."""
 
 from lightning.pytorch.callbacks import Callback
-from ..optim import TorchOptimizer
-from ..utils import check_consistency
+from ...optim import TorchOptimizer
+from ...utils import check_consistency
 
 
 class SwitchOptimizer(Callback):

pina/callback/switch_scheduler.py → pina/callback/optim/switch_scheduler.py

@@ -1,8 +1,8 @@
 """Module for the SwitchScheduler callback."""
 
 from lightning.pytorch.callbacks import Callback
-from ..optim import TorchScheduler
-from ..utils import check_consistency, check_positive_integer
+from ...optim import TorchScheduler
+from ...utils import check_consistency, check_positive_integer
 
 
 class SwitchScheduler(Callback):

pina/callback/processing/metric_tracker.py

@@ -0,0 +1,80 @@
+"""Module for the Metric Tracker."""
+
+import copy
+import torch
+from lightning.pytorch.callbacks import Callback
+
+
+class MetricTracker(Callback):
+    """
+    Lightning Callback for Metric Tracking.
+    """
+
+    def __init__(self, metrics_to_track=None):
+        """
+        Tracks specified metrics during training.
+
+        :param metrics_to_track: List of metrics to track.
+            Defaults to train loss.
+        :type metrics_to_track: list[str], optional
+        """
+        super().__init__()
+        self._collection = []
+        # Default to tracking 'train_loss' if not specified
+        self.metrics_to_track = metrics_to_track
+
+    def setup(self, trainer, pl_module, stage):
+        """
+        Called when fit, validate, test, predict, or tune begins.
+
+        :param Trainer trainer: A :class:`~pina.trainer.Trainer` instance.
+        :param SolverInterface pl_module: A
+            :class:`~pina.solver.solver.SolverInterface` instance.
+        :param str stage: Either 'fit', 'test' or 'predict'.
+        """
+        if self.metrics_to_track is None and trainer.batch_size is None:
+            self.metrics_to_track = ["train_loss"]
+        elif self.metrics_to_track is None:
+            self.metrics_to_track = ["train_loss_epoch"]
+        return super().setup(trainer, pl_module, stage)
+
+    def on_train_epoch_end(self, trainer, pl_module):
+        """
+        Collect and track metrics at the end of each training epoch.
+
+        :param trainer: The trainer object managing the training process.
+        :type trainer: pytorch_lightning.Trainer
+        :param pl_module: The model being trained (not used here).
+        """
+        # Track metrics after the first epoch onwards
+        if trainer.current_epoch > 0:
+            # Append only the tracked metrics to avoid unnecessary data
+            tracked_metrics = {
+                k: v
+                for k, v in trainer.logged_metrics.items()
+                if k in self.metrics_to_track
+            }
+            self._collection.append(copy.deepcopy(tracked_metrics))
+
+    @property
+    def metrics(self):
+        """
+        Aggregate collected metrics over all epochs.
+
+        :return: A dictionary containing aggregated metric values.
+        :rtype: dict
+        """
+        if not self._collection:
+            return {}
+
+        # Get intersection of keys across all collected dictionaries
+        common_keys = set(self._collection[0]).intersection(
+            *self._collection[1:]
+        )
+
+        # Stack the metric values for common keys and return
+        return {
+            k: torch.stack([dic[k] for dic in self._collection])
+            for k in common_keys
+            if k in self.metrics_to_track
+        }

pina/callback/normalizer_data_callback.py → pina/callback/processing/normalizer_data_callback.py

@@ -2,10 +2,10 @@
 
 import torch
 from lightning.pytorch import Callback
-from ..label_tensor import LabelTensor
-from ..utils import check_consistency, is_function
-from ..condition import InputTargetCondition
-from ..data.dataset import PinaGraphDataset
+from ...label_tensor import LabelTensor
+from ...utils import check_consistency, is_function
+from ...condition import InputTargetCondition
+from ...data.dataset import PinaGraphDataset
 
 
 class NormalizerDataCallback(Callback):

pina/callback/processing_callback.py → pina/callback/processing/pina_progress_bar.py

@@ -1,90 +1,12 @@
 """Module for the Processing Callbacks."""
 
-import copy
-import torch
-
-from lightning.pytorch.callbacks import Callback, TQDMProgressBar
+from lightning.pytorch.callbacks import TQDMProgressBar
 from lightning.pytorch.callbacks.progress.progress_bar import (
     get_standard_metrics,
 )
 from pina.utils import check_consistency
 
 
-class MetricTracker(Callback):
-    """
-    Lightning Callback for Metric Tracking.
-    """
-
-    def __init__(self, metrics_to_track=None):
-        """
-        Tracks specified metrics during training.
-
-        :param metrics_to_track: List of metrics to track.
-            Defaults to train loss.
-        :type metrics_to_track: list[str], optional
-        """
-        super().__init__()
-        self._collection = []
-        # Default to tracking 'train_loss' if not specified
-        self.metrics_to_track = metrics_to_track
-
-    def setup(self, trainer, pl_module, stage):
-        """
-        Called when fit, validate, test, predict, or tune begins.
-
-        :param Trainer trainer: A :class:`~pina.trainer.Trainer` instance.
-        :param SolverInterface pl_module: A
-            :class:`~pina.solver.solver.SolverInterface` instance.
-        :param str stage: Either 'fit', 'test' or 'predict'.
-        """
-        if self.metrics_to_track is None and trainer.batch_size is None:
-            self.metrics_to_track = ["train_loss"]
-        elif self.metrics_to_track is None:
-            self.metrics_to_track = ["train_loss_epoch"]
-        return super().setup(trainer, pl_module, stage)
-
-    def on_train_epoch_end(self, trainer, pl_module):
-        """
-        Collect and track metrics at the end of each training epoch.
-
-        :param trainer: The trainer object managing the training process.
-        :type trainer: pytorch_lightning.Trainer
-        :param pl_module: The model being trained (not used here).
-        """
-        # Track metrics after the first epoch onwards
-        if trainer.current_epoch > 0:
-            # Append only the tracked metrics to avoid unnecessary data
-            tracked_metrics = {
-                k: v
-                for k, v in trainer.logged_metrics.items()
-                if k in self.metrics_to_track
-            }
-            self._collection.append(copy.deepcopy(tracked_metrics))
-
-    @property
-    def metrics(self):
-        """
-        Aggregate collected metrics over all epochs.
-
-        :return: A dictionary containing aggregated metric values.
-        :rtype: dict
-        """
-        if not self._collection:
-            return {}
-
-        # Get intersection of keys across all collected dictionaries
-        common_keys = set(self._collection[0]).intersection(
-            *self._collection[1:]
-        )
-
-        # Stack the metric values for common keys and return
-        return {
-            k: torch.stack([dic[k] for dic in self._collection])
-            for k in common_keys
-            if k in self.metrics_to_track
-        }
-
-
 class PINAProgressBar(TQDMProgressBar):
     """
     PINA Implementation of a Lightning Callback for enriching the progress bar.

tests/test_callback/test_progress_bar.py → tests/test_callback/test_pina_progress_bar.py

@@ -1,7 +1,7 @@
 from pina.solver import PINN
 from pina.trainer import Trainer
 from pina.model import FeedForward
-from pina.callback.processing_callback import PINAProgressBar
+from pina.callback import PINAProgressBar
 from pina.problem.zoo import Poisson2DSquareProblem as Poisson
 
 

tests/test_callback/test_adaptive_refinement_callback.py → tests/test_callback/test_r3_refinement.py

@@ -1,12 +1,10 @@
 import pytest
-
 from torch.nn import MSELoss
-
 from pina.solver import PINN
 from pina.trainer import Trainer
 from pina.model import FeedForward
 from pina.problem.zoo import Poisson2DSquareProblem as Poisson
-from pina.callback.refinement import R3Refinement
+from pina.callback import R3Refinement
 
 
 # make the problem