Fixing self adaptive pinns (#469)

* fix self adaptive pinn

* clean competitive pinn

Author: GiovanniCanali

pina/solver/physic_informed_solver/competitive_pinn.py

@@ -160,22 +160,6 @@ def loss_phys(self, samples, equation):
         self._train_discriminator(samples, equation, discriminator_bets)
         return loss_val
 
-    def loss_data(self, input_pts, output_pts):
-        """
-        The data loss for the CompetitivePINN solver. It computes the loss
-        between the network output against the true solution.
-
-        :param LabelTensor input_tensor: The input to the neural networks.
-        :param LabelTensor output_tensor: The true solution to compare the
-            network solution.
-        :return: The computed data loss.
-        :rtype: torch.Tensor
-        """
-        loss_val = super().loss_data(input_pts, output_pts)
-        # prepare for optimizer step called in training step
-        loss_val.backward()
-        return loss_val
-
     def configure_optimizers(self):
         """
         Optimizer configuration for the Competitive PINN solver.
@@ -252,7 +236,6 @@ def _train_discriminator(self, samples, equation, discriminator_bets):
         )
         # prepare for optimizer step called in training step
         self.manual_backward(loss_val)
-        return
 
     def _train_model(self, samples, equation, discriminator_bets):
         """

pina/solver/physic_informed_solver/self_adaptive_pinn.py

@@ -178,62 +178,19 @@ def training_step(self, batch):
         :return: The sum of the loss functions.
         :rtype: LabelTensor
         """
-        self.optimizer_model.instance.zero_grad()
+        # Weights optimization
         self.optimizer_weights.instance.zero_grad()
         loss = super().training_step(batch)
-        self.optimizer_model.instance.step()
+        self.manual_backward(-loss)
         self.optimizer_weights.instance.step()
-        return loss
-
-    def loss_phys(self, samples, equation):
-        """
-        Computes the physics loss for the SAPINN solver based on given
-        samples and equation.
-
-        :param LabelTensor samples: The samples to evaluate the physics loss.
-        :param EquationInterface equation: The governing equation
-            representing the physics.
-        :return: The physics loss calculated based on given
-            samples and equation.
-        :rtype: torch.Tensor
-        """
-        # Train the weights
-        weighted_loss = self._loss_phys(samples, equation)
-        loss_value = -weighted_loss.as_subclass(torch.Tensor)
-        self.manual_backward(loss_value)
-
-        # Detach samples from the existing computational graph and
-        # create a new one by setting requires_grad to True.
-        # In alternative set `retain_graph=True`.
-        samples = samples.detach()
-        samples.requires_grad_()  # = True
-
-        # Train the model
-        weighted_loss = self._loss_phys(samples, equation)
-        loss_value = weighted_loss.as_subclass(torch.Tensor)
-        self.manual_backward(loss_value)
 
-        return loss_value
-
-    def loss_data(self, input_pts, output_pts):
-        """
-        Computes the data loss for the SAPINN solver based on input and
-        output. It computes the loss between the
-        network output against the true solution.
+        # Model optimization
+        self.optimizer_model.instance.zero_grad()
+        loss = super().training_step(batch)
+        self.manual_backward(loss)
+        self.optimizer_model.instance.step()
 
-        :param LabelTensor input_pts: The input to the neural networks.
-        :param LabelTensor output_pts: The true solution to compare the
-            network solution.
-        :return: The computed data loss.
-        :rtype: torch.Tensor
-        """
-        residual = self.forward(input_pts) - output_pts
-        loss = self._vectorial_loss(
-            torch.zeros_like(residual, requires_grad=True), residual
-        )
-        loss_value = self._vect_to_scalar(loss).as_subclass(torch.Tensor)
-        self.manual_backward(loss_value)
-        return loss_value
+        return loss
 
     def configure_optimizers(self):
         """
@@ -330,7 +287,7 @@ def on_load_checkpoint(self, checkpoint):
             )
         return super().on_load_checkpoint(checkpoint)
 
-    def _loss_phys(self, samples, equation):
+    def loss_phys(self, samples, equation):
         """
         Computation of the physical loss for SelfAdaptive PINN solver.