* Adding a test for all PINN solvers to assert that the metrics are correctly log

* Adding test for Metric Tracker
* Modify Metric Tracker to correctly log metrics

pina/callbacks/processing_callbacks.py

@@ -1,6 +1,8 @@
 """PINA Callbacks Implementations"""
 
 from pytorch_lightning.callbacks import Callback
+from pytorch_lightning.core.module import LightningModule
+from pytorch_lightning.trainer.trainer import Trainer
 import torch
 import copy
 
@@ -28,20 +30,41 @@ class MetricTracker(Callback):
         """
         self._collection = []
 
-    def on_train_epoch_end(self, trainer, __):
+    def on_train_epoch_start(self, trainer, pl_module):
         """
-        Collect and track metrics at the end of each training epoch.
+        Collect and track metrics at the start of each training epoch. At epoch
+        zero the metric is not saved. At epoch ``k`` the metric which is tracked
+        is the one of epoch ``k-1``.
 
         :param trainer: The trainer object managing the training process.
         :type trainer: pytorch_lightning.Trainer
-        :param _: Placeholder argument.
+        :param pl_module: Placeholder argument.
 
         :return: None
         :rtype: None
         """
-        self._collection.append(
-            copy.deepcopy(trainer.logged_metrics)
-        )  # track them
+        super().on_train_epoch_end(trainer, pl_module)
+        if trainer.current_epoch > 0:
+            self._collection.append(
+                copy.deepcopy(trainer.logged_metrics)
+            )  # track them
+
+    def on_train_end(self, trainer, pl_module):
+        """
+        Collect and track metrics at the end of training.
+
+        :param trainer: The trainer object managing the training process.
+        :type trainer: pytorch_lightning.Trainer
+        :param pl_module: Placeholder argument.
+
+        :return: None
+        :rtype: None
+        """
+        super().on_train_end(trainer, pl_module)
+        if trainer.current_epoch > 0:
+            self._collection.append(
+                copy.deepcopy(trainer.logged_metrics)
+            )  # track them
 
     @property
     def metrics(self):