Renaming

* solvers -> solver
* adaptive_functions -> adaptive_function
* callbacks -> callback
* operators -> operator
* pinns -> physics_informed_solver
* layers -> block

tests/test_solver/test_gradient_pinn.py

@@ -0,0 +1,155 @@
+import pytest
+import torch
+
+from pina import LabelTensor, Condition
+from pina.problem import TimeDependentProblem
+from pina.solver import GradientPINN
+from pina.model import FeedForward
+from pina.trainer import Trainer
+from pina.problem.zoo import (
+    Poisson2DSquareProblem as Poisson,
+    InversePoisson2DSquareProblem as InversePoisson
+)
+from pina.condition import (
+    InputOutputPointsCondition,
+    InputPointsEquationCondition,
+    DomainEquationCondition
+)
+from torch._dynamo.eval_frame import OptimizedModule
+
+
+class DummyTimeProblem(TimeDependentProblem):
+    """
+    A mock time-dependent problem for testing purposes.
+    """
+    output_variables = ['u']
+    temporal_domain = None
+    conditions = {}
+
+
+# define problems and model
+problem = Poisson()
+problem.discretise_domain(50)
+inverse_problem = InversePoisson()
+inverse_problem.discretise_domain(50)
+model = FeedForward(
+    len(problem.input_variables),
+    len(problem.output_variables)
+)
+
+# add input-output condition to test supervised learning
+input_pts = torch.rand(50, len(problem.input_variables))
+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
+)
+
+
+@pytest.mark.parametrize("problem", [problem, inverse_problem])
+def test_constructor(problem):
+    with pytest.raises(ValueError):
+        GradientPINN(model=model, problem=DummyTimeProblem())
+    solver = GradientPINN(model=model, problem=problem)
+
+    assert solver.accepted_conditions_types == (
+        InputOutputPointsCondition,
+        InputPointsEquationCondition,
+        DomainEquationCondition
+    )
+
+
+@pytest.mark.parametrize("problem", [problem, inverse_problem])
+@pytest.mark.parametrize("batch_size", [None, 1, 5, 20])
+@pytest.mark.parametrize("compile", [True, False])
+def test_solver_train(problem, batch_size, compile):
+    solver = GradientPINN(model=model, problem=problem)
+    trainer = Trainer(solver=solver,
+                      max_epochs=2,
+                      accelerator='cpu',
+                      batch_size=batch_size,
+                      train_size=1.,
+                      val_size=0.,
+                      test_size=0.,
+                      compile=compile)
+    trainer.train()
+    if trainer.compile:
+        assert (isinstance(solver.model, OptimizedModule))
+
+
+@pytest.mark.parametrize("problem", [problem, inverse_problem])
+@pytest.mark.parametrize("batch_size", [None, 1, 5, 20])
+@pytest.mark.parametrize("compile", [True, False])
+def test_solver_validation(problem, batch_size, compile):
+    solver = GradientPINN(model=model, problem=problem)
+    trainer = Trainer(solver=solver,
+                      max_epochs=2,
+                      accelerator='cpu',
+                      batch_size=batch_size,
+                      train_size=0.9,
+                      val_size=0.1,
+                      test_size=0.,
+                      compile=compile)
+    trainer.train()
+    if trainer.compile:
+        assert (isinstance(solver.model, OptimizedModule))
+
+
+@pytest.mark.parametrize("problem", [problem, inverse_problem])
+@pytest.mark.parametrize("batch_size", [None, 1, 5, 20])
+@pytest.mark.parametrize("compile", [True, False])
+def test_solver_test(problem, batch_size, compile):
+    solver = GradientPINN(model=model, problem=problem)
+    trainer = Trainer(solver=solver,
+                      max_epochs=2,
+                      accelerator='cpu',
+                      batch_size=batch_size,
+                      train_size=0.7,
+                      val_size=0.2,
+                      test_size=0.1,
+                      compile=compile)
+    trainer.test()
+    if trainer.compile:
+        assert (isinstance(solver.model, OptimizedModule))
+
+
+@pytest.mark.parametrize("problem", [problem, inverse_problem])
+def test_train_load_restore(problem):
+    dir = "tests/test_solver/tmp"
+    problem = problem
+    solver = GradientPINN(model=model, problem=problem)
+    trainer = Trainer(solver=solver,
+                      max_epochs=5,
+                      accelerator='cpu',
+                      batch_size=None,
+                      train_size=0.7,
+                      val_size=0.2,
+                      test_size=0.1,
+                      default_root_dir=dir)
+    trainer.train()
+
+    # restore
+    new_trainer = Trainer(solver=solver, max_epochs=5, accelerator='cpu')
+    new_trainer.train(
+        ckpt_path=f'{dir}/lightning_logs/version_0/checkpoints/' +
+        'epoch=4-step=5.ckpt')
+
+    # loading
+    new_solver = GradientPINN.load_from_checkpoint(
+        f'{dir}/lightning_logs/version_0/checkpoints/epoch=4-step=5.ckpt',
+        problem=problem, model=model)
+
+    test_pts = LabelTensor(torch.rand(20, 2), problem.input_variables)
+    assert new_solver.forward(test_pts).shape == (20, 1)
+    assert new_solver.forward(test_pts).shape == (
+        solver.forward(test_pts).shape
+    )
+    torch.testing.assert_close(
+        new_solver.forward(test_pts),
+        solver.forward(test_pts))
+
+    # rm directories
+    import shutil
+    shutil.rmtree('tests/test_solver/tmp')