Improve conditions and refactor dataset classes (#475)

* Reimplement conditions

* Refactor datasets and implement LabelBatch

---------

Co-authored-by: Dario Coscia <dariocos99@gmail.com>

pina/solver/physic_informed_solver/pinn_interface.py

@@ -9,8 +9,8 @@ from ...utils import check_consistency
 from ...loss.loss_interface import LossInterface
 from ...problem import InverseProblem
 from ...condition import (
-    InputOutputPointsCondition,
-    InputPointsEquationCondition,
+    InputTargetCondition,
+    InputEquationCondition,
     DomainEquationCondition,
 )
 
@@ -28,8 +28,8 @@ class PINNInterface(SolverInterface, metaclass=ABCMeta):
     """
 
     accepted_conditions_types = (
-        InputOutputPointsCondition,
-        InputPointsEquationCondition,
+        InputTargetCondition,
+        InputEquationCondition,
         DomainEquationCondition,
     )
 
@@ -138,16 +138,16 @@ class PINNInterface(SolverInterface, metaclass=ABCMeta):
         for condition_name, points in batch:
             self.__metric = condition_name
             # if equations are passed
-            if "output_points" not in points:
-                input_pts = points["input_points"]
+            if "target" not in points:
+                input_pts = points["input"]
                 condition = self.problem.conditions[condition_name]
                 loss = loss_residuals(
                     input_pts.requires_grad_(), condition.equation
                 )
             # if data are passed
             else:
-                input_pts = points["input_points"]
-                output_pts = points["output_points"]
+                input_pts = points["input"]
+                output_pts = points["target"]
                 loss = self.loss_data(
                     input_pts=input_pts.requires_grad_(), output_pts=output_pts
                 )

pina/solver/physic_informed_solver/self_adaptive_pinn.py

@@ -262,7 +262,7 @@ class SelfAdaptivePINN(PINNInterface, MultiSolverInterface):
         for (
             condition_name,
             tensor,
-        ) in self.trainer.data_module.train_dataset.input_points.items():
+        ) in self.trainer.data_module.train_dataset.input.items():
             self.weights_dict[condition_name].sa_weights.data = torch.rand(
                 (tensor.shape[0], 1), device=device
             )