Lightining update (#104)
* multiple functions for version 0.0 * lightining update * minor changes * data pinn loss added --------- Co-authored-by: Nicola Demo <demo.nicola@gmail.com> Co-authored-by: Dario Coscia <dariocoscia@cli-10-110-3-125.WIFIeduroamSTUD.units.it> Co-authored-by: Dario Coscia <dariocoscia@Dario-Coscia.station> Co-authored-by: Dario Coscia <dariocoscia@Dario-Coscia.local> Co-authored-by: Dario Coscia <dariocoscia@192.168.1.38>
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Dario Coscia
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Nicola Demo
Nicola Demo
parent
0e3625de80
commit
63fd068988
@@ -11,11 +11,11 @@ class Plotter:
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Implementation of a plotter class, for easy visualizations.
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"""
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def plot_samples(self, pinn, variables=None):
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def plot_samples(self, solver, variables=None):
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"""
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Plot a sample of solution.
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Plot the training grid samples.
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:param PINN pinn: the PINN object.
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:param SolverInterface solver: the SolverInterface object.
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:param list(str) variables: variables to plot. If None, all variables
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are plotted. If 'spatial', only spatial variables are plotted. If
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'temporal', only temporal variables are plotted. Defaults to None.
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@@ -26,15 +26,15 @@ class Plotter:
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:Example:
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>>> plotter = Plotter()
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>>> plotter.plot_samples(pinn=pinn, variables='spatial')
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>>> plotter.plot_samples(solver=solver, variables='spatial')
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"""
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if variables is None:
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variables = pinn.problem.domain.variables
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variables = solver.problem.domain.variables
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elif variables == 'spatial':
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variables = pinn.problem.spatial_domain.variables
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variables = solver.problem.spatial_domain.variables
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elif variables == 'temporal':
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variables = pinn.problem.temporal_domain.variables
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variables = solver.problem.temporal_domain.variables
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if len(variables) not in [1, 2, 3]:
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raise ValueError
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@@ -42,8 +42,8 @@ class Plotter:
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fig = plt.figure()
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proj = '3d' if len(variables) == 3 else None
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ax = fig.add_subplot(projection=proj)
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for location in pinn.input_pts:
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coords = pinn.input_pts[location].extract(variables).T.detach()
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for location in solver.problem.input_pts:
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coords = solver.problem.input_pts[location].extract(variables).T.detach()
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if coords.shape[0] == 1: # 1D samples
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ax.plot(coords[0], torch.zeros(coords[0].shape), '.',
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label=location)
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@@ -69,7 +69,7 @@ class Plotter:
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:param pts: Points to plot the solution.
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:type pts: torch.Tensor
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:param pred: PINN solution evaluated at 'pts'.
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:param pred: SolverInterface solution evaluated at 'pts'.
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:type pred: torch.Tensor
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:param method: not used, kept for code compatibility
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:type method: None
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@@ -95,7 +95,7 @@ class Plotter:
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:param pts: Points to plot the solution.
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:type pts: torch.Tensor
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:param pred: PINN solution evaluated at 'pts'.
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:param pred: SolverInterface solution evaluated at 'pts'.
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:type pred: torch.Tensor
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:param method: matplotlib method to plot 2-dimensional data,
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see https://matplotlib.org/stable/api/axes_api.html for
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@@ -129,12 +129,12 @@ class Plotter:
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*grids, pred_output.cpu().detach(), **kwargs)
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fig.colorbar(cb, ax=ax)
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def plot(self, pinn, components=None, fixed_variables={}, method='contourf',
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def plot(self, solver, components=None, fixed_variables={}, method='contourf',
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res=256, filename=None, **kwargs):
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"""
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Plot sample of PINN output.
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Plot sample of SolverInterface output.
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:param PINN pinn: the PINN object.
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:param SolverInterface solver: the SolverInterface object.
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:param list(str) components: the output variable to plot. If None, all
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the output variables of the problem are selected. Default value is
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None.
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@@ -150,12 +150,12 @@ class Plotter:
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is shown using the setted matplotlib frontend. Default is None.
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"""
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if components is None:
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components = [pinn.problem.output_variables]
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components = [solver.problem.output_variables]
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v = [
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var for var in pinn.problem.input_variables
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var for var in solver.problem.input_variables
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if var not in fixed_variables.keys()
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]
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pts = pinn.problem.domain.sample(res, 'grid', variables=v)
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pts = solver.problem.domain.sample(res, 'grid', variables=v)
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fixed_pts = torch.ones(pts.shape[0], len(fixed_variables))
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fixed_pts *= torch.tensor(list(fixed_variables.values()))
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@@ -163,15 +163,15 @@ class Plotter:
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fixed_pts.labels = list(fixed_variables.keys())
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pts = pts.append(fixed_pts)
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pts = pts.to(device=pinn.device)
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pts = pts.to(device=solver.device)
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predicted_output = pinn.model(pts)
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predicted_output = solver.forward(pts)
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if isinstance(components, str):
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predicted_output = predicted_output.extract(components)
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elif callable(components):
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predicted_output = components(predicted_output)
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truth_solution = getattr(pinn.problem, 'truth_solution', None)
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truth_solution = getattr(solver.problem, 'truth_solution', None)
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if len(v) == 1:
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self._1d_plot(pts, predicted_output, method, truth_solution,
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**kwargs)
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@@ -186,37 +186,25 @@ class Plotter:
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else:
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plt.show()
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def plot_loss(self, pinn, label=None, log_scale=True, filename=None):
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"""
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Plot the loss function values during traininig.
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# TODO loss
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# def plot_loss(self, solver, label=None, log_scale=True):
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# """
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# Plot the loss function values during traininig.
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:param PINN pinn: the PINN object.
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:param str label: the label to use in the legend, defaults to None.
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:param bool log_scale: If True, the y axis is in log scale. Default is
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True.
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:param str filename: the file name to save the plot. If None, the plot
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is not saved. Default is None.
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"""
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# :param SolverInterface solver: the SolverInterface object.
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# :param str label: the label to use in the legend, defaults to None.
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# :param bool log_scale: If True, the y axis is in log scale. Default is
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# True.
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# """
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if not label:
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label = str(pinn)
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# if not label:
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# label = str(solver)
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epochs = list(pinn.history_loss.keys())
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loss = np.array(list(pinn.history_loss.values()))
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# epochs = list(solver.history_loss.keys())
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# loss = np.array(list(solver.history_loss.values()))
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# if loss.ndim != 1:
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# loss = loss[:, 0]
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# if multiple outputs, sum the loss
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if loss.ndim != 1:
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loss = np.sum(loss, axis=1)
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# plot loss
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plt.plot(epochs, loss, label=label)
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plt.legend()
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if log_scale:
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plt.yscale('log')
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plt.title('Loss function')
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plt.xlabel('Epochs')
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plt.ylabel('Loss')
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# save plot
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if filename:
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plt.savefig(filename)
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# plt.plot(epochs, loss, label=label)
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# if log_scale:
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# plt.yscale('log')
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