* Fixing mean tracked loss

* Adding a PINA progress bar

docs/source/_rst/_code.rst

@@ -15,7 +15,7 @@ The pipeline to solve differential equations with PINA follows just five steps:
     2. Generate data using built in `Geometries`_, or load high level simulation results as :doc:`LabelTensor <label_tensor>`
     3. Choose or build one or more `Models`_ to solve the problem
     4. Choose a solver across PINA available `Solvers`_, or build one using the :doc:`SolverInterface <solvers/solver_interface>`
-    5. Train the model with the PINA :doc:`Trainer <solvers/solver_interface>`, enhance the train with `Callbacks_`
+    5. Train the model with the PINA :doc:`Trainer <solvers/solver_interface>`, enhance the train with `Callbacks`_
 
 PINA Features
 --------------
@@ -155,9 +155,9 @@ Callbacks
 .. toctree::
     :titlesonly:
 
-    Metric tracking <callbacks/processing_callbacks.rst>
-    Optimizer callbacks <callbacks/optimizer_callbacks.rst>
-    Adaptive Refinments <callbacks/adaptive_refinment_callbacks.rst>
+    Processing Callbacks <callbacks/processing_callbacks.rst>
+    Optimizer Callbacks <callbacks/optimizer_callbacks.rst>
+    Adaptive Refinment Callback <callbacks/adaptive_refinment_callbacks.rst>
 
 Metrics and Losses
 --------------------

docs/source/_rst/callbacks/processing_callbacks.rst

@@ -3,5 +3,9 @@ Processing callbacks
 
 .. currentmodule:: pina.callbacks.processing_callbacks
 .. autoclass:: MetricTracker
+   :members:
+   :show-inheritance:
+
+.. autoclass:: PINAProgressBar
    :members:
    :show-inheritance:

pina/callbacks/__init__.py

@@ -1,5 +1,10 @@
-__all__ = ["SwitchOptimizer", "R3Refinement", "MetricTracker"]
+__all__ = [
+    "SwitchOptimizer",
+    "R3Refinement",
+    "MetricTracker",
+    "PINAProgressBar"
+    ]
 
 from .optimizer_callbacks import SwitchOptimizer
 from .adaptive_refinment_callbacks import R3Refinement
-from .processing_callbacks import MetricTracker
+from .processing_callbacks import MetricTracker, PINAProgressBar

pina/callbacks/processing_callbacks.py

@@ -1,21 +1,25 @@
 """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
 
+from pytorch_lightning.callbacks import Callback, TQDMProgressBar
+from lightning.pytorch.callbacks.progress.progress_bar import get_standard_metrics
+from pina.utils import check_consistency
 
 class MetricTracker(Callback):
 
     def __init__(self):
         """
         PINA Implementation of a Lightning Callback for Metric Tracking.
 
-        This class provides functionality to track relevant metrics during the training process.
+        This class provides functionality to track relevant metrics during
+        the training process.
 
-        :ivar _collection: A list to store collected metrics after each training epoch.
+        :ivar _collection: A list to store collected metrics after each
+        training epoch.
 
         :param trainer: The trainer object managing the training process.
         :type trainer: pytorch_lightning.Trainer
@@ -28,44 +32,23 @@ def __init__(self):
             >>> # ... Perform training ...
             >>> metrics = tracker.metrics
         """
+        super().__init__()
         self._collection = []
 
-    def on_train_epoch_start(self, trainer, pl_module):
+    def on_train_epoch_end(self, trainer, pl_module):
         """
-        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``.
+        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: Placeholder argument.
-
-        :return: None
-        :rtype: None
         """
         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):
         """
@@ -80,3 +63,91 @@ def metrics(self):
             for k in common_keys
         }
         return v
+
+
+class PINAProgressBar(TQDMProgressBar):
+
+    BAR_FORMAT = "{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_noinv_fmt}{postfix}]"
+    def __init__(self, metrics='mean', **kwargs):
+        """
+        PINA Implementation of a Lightning Callback for enriching the progress
+        bar.
+
+        This class provides functionality to display only relevant metrics
+        during the training process.
+
+        :param metrics: Logged metrics to display during the training. It should
+            be a subset of the conditions keys defined in
+            :obj:`pina.condition.Condition`.
+        :type metrics: str | list(str) | tuple(str)
+
+        :Keyword Arguments:
+            The additional keyword arguments specify the progress bar
+            and can be choosen from the `pytorch-lightning
+            TQDMProgressBar API <https://lightning.ai/docs/pytorch/stable/_modules/lightning/pytorch/callbacks/progress/tqdm_progress.html#TQDMProgressBar>`_
+
+        Example:
+            >>> pbar = PINAProgressBar(['mean'])
+            >>> # ... Perform training ...
+            >>> trainer = Trainer(solver, callbacks=[pbar])
+        """
+        super().__init__(**kwargs)
+        # check consistency
+        if not isinstance(metrics, (list, tuple)):
+            metrics = [metrics]
+        check_consistency(metrics, str)
+        self._sorted_metrics = metrics
+
+    def get_metrics(self, trainer, pl_module):
+        r"""Combines progress bar metrics collected from the trainer with
+        standard metrics from get_standard_metrics.
+        Implement this to override the items displayed in the progress bar.
+        The progress bar metrics are sorted according to ``metrics``.
+
+        Here is an example of how to override the defaults:
+
+        .. code-block:: python
+
+            def get_metrics(self, trainer, model):
+                # don't show the version number
+                items = super().get_metrics(trainer, model)
+                items.pop("v_num", None)
+                return items
+
+        :return: Dictionary with the items to be displayed in the progress bar.
+        :rtype: tuple(dict)
+
+        """
+        standard_metrics = get_standard_metrics(trainer)
+        pbar_metrics = trainer.progress_bar_metrics
+        if pbar_metrics:
+            pbar_metrics = {
+                key: pbar_metrics[key] for key in self._sorted_metrics
+                }
+        duplicates = list(standard_metrics.keys() & pbar_metrics.keys())
+        if duplicates:
+            rank_zero_warn(
+                f"The progress bar already tracks a metric with the name(s) '{', '.join(duplicates)}' and"
+                f" `self.log('{duplicates[0]}', ..., prog_bar=True)` will overwrite this value. "
+                " If this is undesired, change the name or override `get_metrics()` in the progress bar callback.",
+            )
+
+        return {**standard_metrics, **pbar_metrics}
+
+    def on_fit_start(self, trainer, pl_module):
+        """
+        Check that the metrics defined in the initialization are available,
+        i.e. are correctly logged.
+
+        :param trainer: The trainer object managing the training process.
+        :type trainer: pytorch_lightning.Trainer
+        :param pl_module: Placeholder argument.
+        """
+        # Check if all keys in sort_keys are present in the dictionary
+        for key in self._sorted_metrics:
+            if key not in trainer.solver.problem.conditions.keys() and key != 'mean':
+                raise KeyError(f"Key '{key}' is not present in the dictionary")
+        # add the loss pedix
+        self._sorted_metrics = [
+            metric + '_loss' for metric in self._sorted_metrics]
+        return super().on_fit_start(trainer, pl_module)

pina/solvers/pinns/basepinn.py

@@ -126,8 +126,9 @@ def training_step(self, batch, _):
         # clamp unknown parameters in InverseProblem (if needed)
         self._clamp_params()
 
-        # total loss (must be a torch.Tensor)
+        # total loss (must be a torch.Tensor), and logs
         total_loss = sum(condition_losses)
+        self.save_logs_and_release()
         return total_loss.as_subclass(torch.Tensor)
 
     def loss_data(self, input_tensor, output_tensor):
@@ -205,7 +206,7 @@ def store_log(self, loss_value):
         )
         self.__logged_res_losses.append(loss_value)
 
-    def on_train_epoch_end(self):
+    def save_logs_and_release(self):
         """
         At the end of each epoch we free the stored losses. This function
         should not be override if not intentionally.
@@ -218,7 +219,6 @@ def on_train_epoch_end(self):
             )
             # free the logged losses
             self.__logged_res_losses = []
-        return super().on_train_epoch_end()
 
     def _clamp_inverse_problem_params(self):
         """

tests/test_callbacks/test_progress_bar.py

@@ -0,0 +1,78 @@
+import torch
+import pytest
+
+from pina.problem import SpatialProblem
+from pina.operators import laplacian
+from pina.geometry import CartesianDomain
+from pina import Condition, LabelTensor
+from pina.solvers import PINN
+from pina.trainer import Trainer
+from pina.model import FeedForward
+from pina.equation.equation import Equation
+from pina.equation.equation_factory import FixedValue
+from pina.callbacks.processing_callbacks import PINAProgressBar
+
+
+def laplace_equation(input_, output_):
+    force_term = (torch.sin(input_.extract(['x']) * torch.pi) *
+                  torch.sin(input_.extract(['y']) * torch.pi))
+    delta_u = laplacian(output_.extract(['u']), input_)
+    return delta_u - force_term
+
+
+my_laplace = Equation(laplace_equation)
+in_ = LabelTensor(torch.tensor([[0., 1.]]), ['x', 'y'])
+out_ = LabelTensor(torch.tensor([[0.]]), ['u'])
+
+
+class Poisson(SpatialProblem):
+    output_variables = ['u']
+    spatial_domain = CartesianDomain({'x': [0, 1], 'y': [0, 1]})
+
+    conditions = {
+        'gamma1': Condition(
+            location=CartesianDomain({'x': [0, 1], 'y':  1}),
+            equation=FixedValue(0.0)),
+        'gamma2': Condition(
+            location=CartesianDomain({'x': [0, 1], 'y': 0}),
+            equation=FixedValue(0.0)),
+        'gamma3': Condition(
+            location=CartesianDomain({'x':  1, 'y': [0, 1]}),
+            equation=FixedValue(0.0)),
+        'gamma4': Condition(
+            location=CartesianDomain({'x': 0, 'y': [0, 1]}),
+            equation=FixedValue(0.0)),
+        'D': Condition(
+            input_points=LabelTensor(torch.rand(size=(100, 2)), ['x', 'y']),
+            equation=my_laplace),
+        'data': Condition(
+            input_points=in_,
+            output_points=out_)
+    }
+
+
+# make the problem
+poisson_problem = Poisson()
+boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
+n = 10
+poisson_problem.discretise_domain(n, 'grid', locations=boundaries)
+model = FeedForward(len(poisson_problem.input_variables),
+                    len(poisson_problem.output_variables))
+
+# make the solver
+solver = PINN(problem=poisson_problem, model=model)
+
+
+def test_progress_bar_constructor():
+    PINAProgressBar(['mean_loss'])
+
+def test_progress_bar_routine():
+    # make the trainer
+    trainer = Trainer(solver=solver,
+                      callbacks=[
+                          PINAProgressBar(['mean', 'D'])
+                      ],
+                      accelerator='cpu',
+                      max_epochs=5)
+    trainer.train()
+    # TODO there should be a check that the correct metrics are displayed