Formatting

* Adding black as dev dependency
* Formatting pina code
* Formatting tests

pina/solver/physic_informed_solver/competitive_pinn.py

@@ -1,4 +1,4 @@
-""" Module for Competitive PINN. """
+"""Module for Competitive PINN."""
 
 import torch
 import copy
@@ -55,16 +55,18 @@ class CompetitivePINN(PINNInterface, MultiSolverInterface):
         ``extra_feature``.
     """
 
-    def __init__(self,
-                 problem,
-                 model,
-                 discriminator=None,
-                 optimizer_model=None,
-                 optimizer_discriminator=None,
-                 scheduler_model=None,
-                 scheduler_discriminator=None,
-                 weighting=None,
-                 loss=None):
+    def __init__(
+        self,
+        problem,
+        model,
+        discriminator=None,
+        optimizer_model=None,
+        optimizer_discriminator=None,
+        scheduler_model=None,
+        scheduler_discriminator=None,
+        weighting=None,
+        loss=None,
+    ):
         """
         :param AbstractProblem problem: The formulation of the problem.
         :param torch.nn.Module model: The neural network model to use
@@ -72,13 +74,13 @@ class CompetitivePINN(PINNInterface, MultiSolverInterface):
         :param torch.nn.Module discriminator: The neural network model to use
             for the discriminator. If ``None``, the discriminator network will
             have the same architecture as the model network.
-        :param torch.optim.Optimizer optimizer_model: The neural network 
+        :param torch.optim.Optimizer optimizer_model: The neural network
             optimizer to use for the model network; default `None`.
         :param torch.optim.Optimizer optimizer_discriminator: The neural network
             optimizer to use for the discriminator network; default `None`.
-        :param torch.optim.LRScheduler scheduler_model: Learning rate scheduler 
+        :param torch.optim.LRScheduler scheduler_model: Learning rate scheduler
             for the model; default `None`.
-        :param torch.optim.LRScheduler scheduler_discriminator: Learning rate 
+        :param torch.optim.LRScheduler scheduler_discriminator: Learning rate
             scheduler for the discriminator; default `None`.
         :param WeightingInterface weighting: The weighting schema to use;
             default `None`.
@@ -88,12 +90,14 @@ class CompetitivePINN(PINNInterface, MultiSolverInterface):
         if discriminator is None:
             discriminator = copy.deepcopy(model)
 
-        super().__init__(models=[model, discriminator],
-                         problem=problem,
-                         optimizers=[optimizer_model, optimizer_discriminator],
-                         schedulers=[scheduler_model, scheduler_discriminator],
-                         weighting=weighting,
-                         loss=loss)
+        super().__init__(
+            models=[model, discriminator],
+            problem=problem,
+            optimizers=[optimizer_model, optimizer_discriminator],
+            schedulers=[scheduler_model, scheduler_discriminator],
+            weighting=weighting,
+            loss=loss,
+        )
 
         # Set automatic optimization to False
         self.automatic_optimization = False
@@ -158,7 +162,7 @@ class CompetitivePINN(PINNInterface, MultiSolverInterface):
 
     def loss_data(self, input_pts, output_pts):
         """
-        The data loss for the CompetitivePINN solver. It computes the loss 
+        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.
@@ -167,7 +171,7 @@ class CompetitivePINN(PINNInterface, MultiSolverInterface):
         :return: The computed data loss.
         :rtype: torch.Tensor
         """
-        loss_val = (super().loss_data(input_pts, output_pts))
+        loss_val = super().loss_data(input_pts, output_pts)
         # prepare for optimizer step called in training step
         loss_val.backward()
         return loss_val
@@ -195,10 +199,14 @@ class CompetitivePINN(PINNInterface, MultiSolverInterface):
         self.scheduler_model.hook(self.optimizer_model)
         self.scheduler_discriminator.hook(self.optimizer_discriminator)
         return (
-            [self.optimizer_model.instance,
-             self.optimizer_discriminator.instance],
-            [self.scheduler_model.instance,
-             self.scheduler_discriminator.instance]
+            [
+                self.optimizer_model.instance,
+                self.optimizer_discriminator.instance,
+            ],
+            [
+                self.scheduler_model.instance,
+                self.scheduler_discriminator.instance,
+            ],
         )
 
     def on_train_batch_end(self, outputs, batch, batch_idx):
@@ -216,8 +224,7 @@ class CompetitivePINN(PINNInterface, MultiSolverInterface):
         """
         # increase by one the counter of optimization to save loggers
         (
-            self.trainer.fit_loop.epoch_loop.manual_optimization
-            .optim_step_progress.total.completed
+            self.trainer.fit_loop.epoch_loop.manual_optimization.optim_step_progress.total.completed
         ) += 1
 
         return super().on_train_batch_end(outputs, batch, batch_idx)