Improve conditions and refactor dataset classes (#475)

* Reimplement conditions

* Refactor datasets and implement LabelBatch

---------

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

tests/test_solver/test_causal_pinn.py

@@ -11,8 +11,8 @@ from pina.problem.zoo import (
     InverseDiffusionReactionProblem
 )
 from pina.condition import (
-    InputOutputPointsCondition,
-    InputPointsEquationCondition,
+    InputTargetCondition,
+    InputEquationCondition,
     DomainEquationCondition
 )
 from torch._dynamo.eval_frame import OptimizedModule
@@ -43,8 +43,8 @@ input_pts = LabelTensor(input_pts, problem.input_variables)
 output_pts = torch.rand(50, len(problem.output_variables))
 output_pts = LabelTensor(output_pts, problem.output_variables)
 problem.conditions['data'] = Condition(
-    input_points=input_pts,
-    output_points=output_pts
+    input=input_pts,
+    target=output_pts
 )
 
 
@@ -56,8 +56,8 @@ def test_constructor(problem, eps):
     solver = CausalPINN(model=model, problem=problem, eps=eps)
 
     assert solver.accepted_conditions_types == (
-        InputOutputPointsCondition,
-        InputPointsEquationCondition,
+        InputTargetCondition,
+        InputEquationCondition,
         DomainEquationCondition
     )
 

tests/test_solver/test_competitive_pinn.py

@@ -10,8 +10,8 @@ from pina.problem.zoo import (
     InversePoisson2DSquareProblem as InversePoisson
 )
 from pina.condition import (
-    InputOutputPointsCondition,
-    InputPointsEquationCondition,
+    InputTargetCondition,
+    InputEquationCondition,
     DomainEquationCondition
 )
 from torch._dynamo.eval_frame import OptimizedModule
@@ -33,8 +33,8 @@ input_pts = LabelTensor(input_pts, problem.input_variables)
 output_pts = torch.rand(50, len(problem.output_variables))
 output_pts = LabelTensor(output_pts, problem.output_variables)
 problem.conditions['data'] = Condition(
-    input_points=input_pts,
-    output_points=output_pts
+    input=input_pts,
+    target=output_pts
 )
 
 @pytest.mark.parametrize("problem", [problem, inverse_problem])
@@ -44,8 +44,8 @@ def test_constructor(problem, discr):
     solver = CompPINN(problem=problem, model=model, discriminator=discr)
 
     assert solver.accepted_conditions_types == (
-        InputOutputPointsCondition,
-        InputPointsEquationCondition,
+        InputTargetCondition,
+        InputEquationCondition,
         DomainEquationCondition
     )
 

tests/test_solver/test_garom.py

@@ -4,7 +4,7 @@ import torch.nn as nn
 import pytest
 from pina import Condition, LabelTensor
 from pina.solver import GAROM
-from pina.condition import InputOutputPointsCondition
+from pina.condition import InputTargetCondition
 from pina.problem import AbstractProblem
 from pina.model import FeedForward
 from pina.trainer import Trainer
@@ -16,8 +16,8 @@ class TensorProblem(AbstractProblem):
     output_variables = ['u']
     conditions = {
         'data': Condition(
-            output_points=torch.randn(50, 2),
-            input_points=torch.randn(50, 1))
+            target=torch.randn(50, 2),
+            input=torch.randn(50, 1))
     }
 
 
@@ -74,7 +74,7 @@ def test_constructor():
           generator=Generator(),
           discriminator=Discriminator())
     assert GAROM.accepted_conditions_types == (
-        InputOutputPointsCondition
+        InputTargetCondition
     )
 
 

tests/test_solver/test_gradient_pinn.py

@@ -11,8 +11,8 @@ from pina.problem.zoo import (
     InversePoisson2DSquareProblem as InversePoisson
 )
 from pina.condition import (
-    InputOutputPointsCondition,
-    InputPointsEquationCondition,
+    InputTargetCondition,
+    InputEquationCondition,
     DomainEquationCondition
 )
 from torch._dynamo.eval_frame import OptimizedModule
@@ -43,8 +43,8 @@ input_pts = LabelTensor(input_pts, problem.input_variables)
 output_pts = torch.rand(50, len(problem.output_variables))
 output_pts = LabelTensor(output_pts, problem.output_variables)
 problem.conditions['data'] = Condition(
-    input_points=input_pts,
-    output_points=output_pts
+    input=input_pts,
+    target=output_pts
 )
 
 
@@ -55,8 +55,8 @@ def test_constructor(problem):
     solver = GradientPINN(model=model, problem=problem)
 
     assert solver.accepted_conditions_types == (
-        InputOutputPointsCondition,
-        InputPointsEquationCondition,
+        InputTargetCondition,
+        InputEquationCondition,
         DomainEquationCondition
     )
 

tests/test_solver/test_pinn.py

@@ -6,8 +6,8 @@ from pina.model import FeedForward
 from pina.trainer import Trainer
 from pina.solver import PINN
 from pina.condition import (
-    InputOutputPointsCondition,
-    InputPointsEquationCondition,
+    InputTargetCondition,
+    InputEquationCondition,
     DomainEquationCondition
 )
 from pina.problem.zoo import (
@@ -33,8 +33,8 @@ input_pts = LabelTensor(input_pts, problem.input_variables)
 output_pts = torch.rand(50, len(problem.output_variables))
 output_pts = LabelTensor(output_pts, problem.output_variables)
 problem.conditions['data'] = Condition(
-    input_points=input_pts,
-    output_points=output_pts
+    input=input_pts,
+    target=output_pts
 )
 
 @pytest.mark.parametrize("problem", [problem, inverse_problem])
@@ -42,8 +42,8 @@ def test_constructor(problem):
     solver = PINN(problem=problem, model=model)
 
     assert solver.accepted_conditions_types == (
-        InputOutputPointsCondition,
-        InputPointsEquationCondition,
+        InputTargetCondition,
+        InputEquationCondition,
         DomainEquationCondition
     )
 

tests/test_solver/test_rba_pinn.py

@@ -6,8 +6,8 @@ from pina.model import FeedForward
 from pina.trainer import Trainer
 from pina.solver import RBAPINN
 from pina.condition import (
-    InputOutputPointsCondition,
-    InputPointsEquationCondition,
+    InputTargetCondition,
+    InputEquationCondition,
     DomainEquationCondition
 )
 from pina.problem.zoo import (
@@ -32,8 +32,8 @@ input_pts = LabelTensor(input_pts, problem.input_variables)
 output_pts = torch.rand(50, len(problem.output_variables))
 output_pts = LabelTensor(output_pts, problem.output_variables)
 problem.conditions['data'] = Condition(
-    input_points=input_pts,
-    output_points=output_pts
+    input=input_pts,
+    target=output_pts
 )
 
 
@@ -46,8 +46,8 @@ def test_constructor(problem, eta, gamma):
     solver = RBAPINN(model=model, problem=problem, eta=eta, gamma=gamma)
 
     assert solver.accepted_conditions_types == (
-        InputOutputPointsCondition,
-        InputPointsEquationCondition,
+        InputTargetCondition,
+        InputEquationCondition,
         DomainEquationCondition
     )
 

tests/test_solver/test_reduced_order_model_solver.py

@@ -3,7 +3,7 @@ import pytest
 
 from pina import Condition, LabelTensor
 from pina.problem import AbstractProblem
-from pina.condition import InputOutputPointsCondition
+from pina.condition import InputTargetCondition
 from pina.solver import ReducedOrderModelSolver
 from pina.trainer import Trainer
 from pina.model import FeedForward
@@ -16,8 +16,8 @@ class LabelTensorProblem(AbstractProblem):
     output_variables = ['u']
     conditions = {
         'data': Condition(
-            input_points=LabelTensor(torch.randn(20, 2), ['u_0', 'u_1']),
-            output_points=LabelTensor(torch.randn(20, 1), ['u'])),
+            input=LabelTensor(torch.randn(20, 2), ['u_0', 'u_1']),
+            target=LabelTensor(torch.randn(20, 1), ['u'])),
     }
 
 
@@ -26,8 +26,8 @@ class TensorProblem(AbstractProblem):
     output_variables = ['u']
     conditions = {
         'data': Condition(
-            input_points=torch.randn(20, 2),
-            output_points=torch.randn(20, 1))
+            input=torch.randn(20, 2),
+            target=torch.randn(20, 1))
     }
 
 
@@ -68,7 +68,7 @@ def test_constructor():
     ReducedOrderModelSolver(problem=LabelTensorProblem(),
                             reduction_network=reduction_net,
                             interpolation_network=interpolation_net)
-    assert ReducedOrderModelSolver.accepted_conditions_types == InputOutputPointsCondition
+    assert ReducedOrderModelSolver.accepted_conditions_types == InputTargetCondition
     with pytest.raises(SyntaxError):
         ReducedOrderModelSolver(problem=problem,
                                 reduction_network=AE_missing_encode(

tests/test_solver/test_self_adaptive_pinn.py

@@ -10,8 +10,8 @@ from pina.problem.zoo import (
     InversePoisson2DSquareProblem as InversePoisson
 )
 from pina.condition import (
-    InputOutputPointsCondition,
-    InputPointsEquationCondition,
+    InputTargetCondition,
+    InputEquationCondition,
     DomainEquationCondition
 )
 from torch._dynamo.eval_frame import OptimizedModule
@@ -33,8 +33,8 @@ input_pts = LabelTensor(input_pts, problem.input_variables)
 output_pts = torch.rand(50, len(problem.output_variables))
 output_pts = LabelTensor(output_pts, problem.output_variables)
 problem.conditions['data'] = Condition(
-    input_points=input_pts,
-    output_points=output_pts
+    input=input_pts,
+    target=output_pts
 )
 
 
@@ -46,8 +46,8 @@ def test_constructor(problem, weight_fn):
     solver = SAPINN(problem=problem, model=model, weight_function=weight_fn)
 
     assert solver.accepted_conditions_types == (
-        InputOutputPointsCondition,
-        InputPointsEquationCondition,
+        InputTargetCondition,
+        InputEquationCondition,
         DomainEquationCondition
     )
 

tests/test_solver/test_supervised_solver.py

@@ -1,7 +1,7 @@
 import torch
 import pytest
 from pina import Condition, LabelTensor
-from pina.condition import InputOutputPointsCondition
+from pina.condition import InputTargetCondition
 from pina.problem import AbstractProblem
 from pina.solver import SupervisedSolver
 from pina.model import FeedForward
@@ -14,8 +14,8 @@ class LabelTensorProblem(AbstractProblem):
     output_variables = ['u']
     conditions = {
         'data': Condition(
-            input_points=LabelTensor(torch.randn(20, 2), ['u_0', 'u_1']),
-            output_points=LabelTensor(torch.randn(20, 1), ['u'])),
+            input=LabelTensor(torch.randn(20, 2), ['u_0', 'u_1']),
+            target=LabelTensor(torch.randn(20, 1), ['u'])),
     }
 
 
@@ -24,8 +24,8 @@ class TensorProblem(AbstractProblem):
     output_variables = ['u']
     conditions = {
         'data': Condition(
-            input_points=torch.randn(20, 2),
-            output_points=torch.randn(20, 1))
+            input=torch.randn(20, 2),
+            target=torch.randn(20, 1))
     }
 
 
@@ -36,7 +36,7 @@ def test_constructor():
     SupervisedSolver(problem=TensorProblem(), model=model)
     SupervisedSolver(problem=LabelTensorProblem(), model=model)
     assert SupervisedSolver.accepted_conditions_types == (
-        InputOutputPointsCondition
+        InputTargetCondition
     )