Tutorials v0.1 (#178)

Tutorial update and small fixes

* Tutorials update + Tutorial FNO
* Create a metric tracker callback
* Update PINN for logging
* Update plotter for plotting
* Small fix LabelTensor
* Small fix FNO

---------

Co-authored-by: Dario Coscia <dariocoscia@cli-10-110-13-250.WIFIeduroamSTUD.units.it>
Co-authored-by: Dario Coscia <dariocoscia@dhcp-176.eduroam.sissa.it>

pina/plotter.py

@@ -1,6 +1,7 @@
 """ Module for plotting. """
 import matplotlib.pyplot as plt
 import torch
+from pina.callbacks import MetricTracker
 
 from pina import LabelTensor
 
@@ -129,12 +130,12 @@ class Plotter:
                 *grids, pred_output.cpu().detach(), **kwargs)
             fig.colorbar(cb, ax=ax)
 
-    def plot(self, solver, components=None, fixed_variables={}, method='contourf',
+    def plot(self, trainer, components=None, fixed_variables={}, method='contourf',
              res=256, filename=None, **kwargs):
         """
         Plot sample of SolverInterface output.
 
-        :param SolverInterface solver: the SolverInterface object.
+        :param Trainer trainer: the Trainer object.
         :param list(str) components: the output variable to plot. If None, all
             the output variables of the problem are selected. Default value is
             None.
@@ -149,6 +150,7 @@ class Plotter:
         :param str filename: the file name to save the plot. If None, the plot
             is shown using the setted matplotlib frontend. Default is None.
         """
+        solver = trainer.solver
         if components is None:
             components = [solver.problem.output_variables]
         v = [
@@ -186,25 +188,38 @@ class Plotter:
         else:
             plt.show()
 
-    # TODO loss
-    # def plot_loss(self, solver, label=None, log_scale=True):
-    #     """
-    #     Plot the loss function values during traininig.
+    def plot_loss(self, trainer, metric=None, label=None, log_scale=True):
+        """
+        Plot the loss function values during traininig.
 
-    #     :param SolverInterface solver: the SolverInterface object.
-    #     :param str label: the label to use in the legend, defaults to None.
-    #     :param bool log_scale: If True, the y axis is in log scale. Default is
-    #         True.
-    #     """
+        :param SolverInterface solver: the SolverInterface object.
+        :param str metric: the metric to use in the y axis.
+        :param str label: the label to use in the legend, defaults to None.
+        :param bool log_scale: If True, the y axis is in log scale. Default is
+            True.
+        """
 
-    #     if not label:
-    #         label = str(solver)
+        # check that MetricTracker has been used
+        list_ = [idx for idx, s in enumerate(trainer.callbacks) if isinstance(s, MetricTracker)]
+        if not bool(list_):
+            raise FileNotFoundError('MetricTracker should be used as a callback during training to'
+                                    ' use this method.')
 
-    #     epochs = list(solver.history_loss.keys())
-    #     loss = np.array(list(solver.history_loss.values()))
-    #     if loss.ndim != 1:
-    #         loss = loss[:, 0]
+        metrics = trainer.callbacks[list_[0]].metrics
 
-    #     plt.plot(epochs, loss, label=label)
-    #     if log_scale:
-    #         plt.yscale('log')
+        if not metric:
+            metric = 'mean_loss'
+
+        loss = metrics[metric]
+        epochs = range(len(loss))
+
+        if label is not None:
+            plt.plot(epochs, loss, label=label)
+            plt.legend()
+        else:
+            plt.plot(epochs, loss)
+
+        if log_scale:
+            plt.yscale('log')
+        plt.xlabel('epoch')
+        plt.ylabel(metric)