fixing competitive pinn (#470)

pina/solver/physic_informed_solver/competitive_pinn.py

@@ -3,7 +3,7 @@
 import torch
 import copy
 
-from pina.problem import InverseProblem
+from ...problem import InverseProblem
 from .pinn_interface import PINNInterface
 from ..solver import MultiSolverInterface
 
@@ -125,10 +125,15 @@ class CompetitivePINN(PINNInterface, MultiSolverInterface):
         :return: The sum of the loss functions.
         :rtype: LabelTensor
         """
+        # train model
         self.optimizer_model.instance.zero_grad()
+        loss = super().training_step(batch)
+        self.manual_backward(loss)
+        self.optimizer_model.instance.step()
+        # train discriminator
         self.optimizer_discriminator.instance.zero_grad()
         loss = super().training_step(batch)
-        self.optimizer_model.instance.step()
+        self.manual_backward(-loss)
         self.optimizer_discriminator.instance.step()
         return loss
 
@@ -144,20 +149,18 @@ class CompetitivePINN(PINNInterface, MultiSolverInterface):
             samples and equation.
         :rtype: LabelTensor
         """
-        # Train the model for one step
-        with torch.no_grad():
+        # Compute discriminator bets
         discriminator_bets = self.discriminator(samples)
-        loss_val = self._train_model(samples, equation, discriminator_bets)
 
-        # 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_()
+        # Compute residual and multiply discriminator_bets
+        residual = self.compute_residual(samples=samples, equation=equation)
+        residual = residual * discriminator_bets
 
-        # Train the discriminator for one step
-        discriminator_bets = self.discriminator(samples)
-        self._train_discriminator(samples, equation, discriminator_bets)
+        # Compute competitive residual.
+        loss_val = self.loss(
+            torch.zeros_like(residual, requires_grad=True),
+            residual,
+        )
         return loss_val
 
     def configure_optimizers(self):
@@ -213,58 +216,6 @@ class CompetitivePINN(PINNInterface, MultiSolverInterface):
 
         return super().on_train_batch_end(outputs, batch, batch_idx)
 
-    def _train_discriminator(self, samples, equation, discriminator_bets):
-        """
-        Trains the discriminator network of the Competitive PINN.
-
-        :param LabelTensor samples: Input samples to evaluate the physics loss.
-        :param EquationInterface equation: The governing equation representing
-            the physics.
-        :param Tensor discriminator_bets: Predictions made by the discriminator
-            network.
-        """
-        # Compute residual. Detach since discriminator weights are fixed
-        residual = self.compute_residual(
-            samples=samples, equation=equation
-        ).detach()
-
-        # Compute competitive residual, then maximise the loss
-        competitive_residual = residual * discriminator_bets
-        loss_val = -self.loss(
-            torch.zeros_like(competitive_residual, requires_grad=True),
-            competitive_residual,
-        )
-        # prepare for optimizer step called in training step
-        self.manual_backward(loss_val)
-
-    def _train_model(self, samples, equation, discriminator_bets):
-        """
-        Trains the model network of the Competitive PINN.
-
-        :param LabelTensor samples: Input samples to evaluate the physics loss.
-        :param EquationInterface equation: The governing equation representing
-            the physics.
-        :param Tensor discriminator_bets: Predictions made by the discriminator.
-            network.
-        :return: The computed data loss.
-        :rtype: torch.Tensor
-        """
-        # Compute residual
-        residual = self.compute_residual(samples=samples, equation=equation)
-        with torch.no_grad():
-            loss_residual = self.loss(torch.zeros_like(residual), residual)
-
-        # Compute competitive residual. Detach discriminator_bets
-        # to optimize only the generator model
-        competitive_residual = residual * discriminator_bets.detach()
-        loss_val = self.loss(
-            torch.zeros_like(competitive_residual, requires_grad=True),
-            competitive_residual,
-        )
-        # prepare for optimizer step called in training step
-        self.manual_backward(loss_val)
-        return loss_residual
-
     @property
     def neural_net(self):
         """