Renaming

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

tests/test_adaptive_function.py

@@ -1,19 +1,19 @@
 import torch
 import pytest
 
-from pina.adaptive_functions import (AdaptiveReLU, AdaptiveSigmoid, AdaptiveTanh,
+from pina.adaptive_function import (AdaptiveReLU, AdaptiveSigmoid, AdaptiveTanh,
                             AdaptiveSiLU, AdaptiveMish, AdaptiveELU,
                             AdaptiveCELU, AdaptiveGELU, AdaptiveSoftmin,
                             AdaptiveSoftmax, AdaptiveSIREN, AdaptiveExp)
 
 
-adaptive_functions = (AdaptiveReLU, AdaptiveSigmoid, AdaptiveTanh,
+adaptive_function = (AdaptiveReLU, AdaptiveSigmoid, AdaptiveTanh,
                       AdaptiveSiLU, AdaptiveMish, AdaptiveELU,
                       AdaptiveCELU, AdaptiveGELU, AdaptiveSoftmin,
                       AdaptiveSoftmax, AdaptiveSIREN, AdaptiveExp)
 x = torch.rand(10, requires_grad=True)
 
-@pytest.mark.parametrize("Func", adaptive_functions)
+@pytest.mark.parametrize("Func", adaptive_function)
 def test_constructor(Func):
     if Func.__name__ == 'AdaptiveExp':
         # simple
@@ -50,12 +50,12 @@ def test_constructor(Func):
         Func(alpha='s')
         Func(alpha=1)
 
-@pytest.mark.parametrize("Func", adaptive_functions)      
+@pytest.mark.parametrize("Func", adaptive_function)      
 def test_forward(Func):
     af = Func()
     af(x)
 
-@pytest.mark.parametrize("Func", adaptive_functions)
+@pytest.mark.parametrize("Func", adaptive_function)
 def test_backward(Func):
     af = Func()
     y = af(x)

tests/test_blocks/test_conv.py

@@ -1,4 +1,4 @@
-from pina.model.layers import ContinuousConvBlock
+from pina.model.block import ContinuousConvBlock
 import torch
 
 

tests/test_blocks/test_embedding.py

@@ -1,7 +1,7 @@
 import torch
 import pytest
 
-from pina.model.layers import PeriodicBoundaryEmbedding, FourierFeatureEmbedding
+from pina.model.block import PeriodicBoundaryEmbedding, FourierFeatureEmbedding
 
 # test tolerance
 tol = 1e-6

tests/test_blocks/test_fourier.py

@@ -1,4 +1,4 @@
-from pina.model.layers import FourierBlock1D, FourierBlock2D, FourierBlock3D
+from pina.model.block import FourierBlock1D, FourierBlock2D, FourierBlock3D
 import torch
 
 input_numb_fields = 3

tests/test_blocks/test_lnolayer.py

@@ -1,7 +1,7 @@
 import torch
 import pytest
 
-from pina.model.layers import LowRankBlock
+from pina.model.block import LowRankBlock
 from pina import LabelTensor
 
 

tests/test_blocks/test_orthogonal.py

@@ -1,6 +1,6 @@
 import torch
 import pytest
-from pina.model.layers import OrthogonalBlock
+from pina.model.block import OrthogonalBlock
 
 torch.manual_seed(111)
 

tests/test_blocks/test_pod.py

@@ -1,7 +1,7 @@
 import torch
 import pytest
 
-from pina.model.layers.pod import PODBlock
+from pina.model.block.pod import PODBlock
 
 x = torch.linspace(-1, 1, 100)
 toy_snapshots = torch.vstack([torch.exp(-x**2)*c for c in torch.linspace(0, 1, 10)])

tests/test_blocks/test_rbf.py

@@ -2,7 +2,7 @@ import torch
 import pytest
 import math
 
-from pina.model.layers.rbf_layer import RBFBlock
+from pina.model.block.rbf_layer import RBFBlock
 
 x = torch.linspace(-1, 1, 100)
 toy_params = torch.linspace(0, 1, 10).unsqueeze(1)

tests/test_blocks/test_residual.py

@@ -1,4 +1,4 @@
-from pina.model.layers import ResidualBlock, EnhancedLinear
+from pina.model.block import ResidualBlock, EnhancedLinear
 import torch
 import torch.nn as nn
 

tests/test_blocks/test_spectral_conv.py

@@ -1,4 +1,4 @@
-from pina.model.layers import SpectralConvBlock1D, SpectralConvBlock2D, SpectralConvBlock3D
+from pina.model.block import SpectralConvBlock1D, SpectralConvBlock2D, SpectralConvBlock3D
 import torch
 
 input_numb_fields = 3

tests/test_callback/test_adaptive_refinment_callback.py

@@ -1,8 +1,8 @@
-from pina.solvers import PINN
+from pina.solver import PINN
 from pina.trainer import Trainer
 from pina.model import FeedForward
 from pina.problem.zoo import Poisson2DSquareProblem as Poisson
-from pina.callbacks import R3Refinement
+from pina.callback import R3Refinement
 
 
 # make the problem
@@ -25,7 +25,7 @@ solver = PINN(problem=poisson_problem, model=model)
 # def test_r3refinment_routine():
 #     # make the trainer
 #     trainer = Trainer(solver=solver,
-#                       callbacks=[R3Refinement(sample_every=1)],
+#                       callback=[R3Refinement(sample_every=1)],
 #                       accelerator='cpu',
 #                       max_epochs=5)
 #     trainer.train()
@@ -35,7 +35,7 @@ solver = PINN(problem=poisson_problem, model=model)
 #                     len(poisson_problem.output_variables))
 #     solver = PINN(problem=poisson_problem, model=model)
 #     trainer = Trainer(solver=solver,
-#                       callbacks=[R3Refinement(sample_every=1)],
+#                       callback=[R3Refinement(sample_every=1)],
 #                       accelerator='cpu',
 #                       max_epochs=5)
 #     before_n_points = {loc : len(pts) for loc, pts in trainer.solver.problem.input_pts.items()}

tests/test_callback/test_metric_tracker.py

@@ -1,7 +1,7 @@
-from pina.solvers import PINN
+from pina.solver import PINN
 from pina.trainer import Trainer
 from pina.model import FeedForward
-from pina.callbacks import MetricTracker
+from pina.callback import MetricTracker
 from pina.problem.zoo import Poisson2DSquareProblem as Poisson
 
 
@@ -24,14 +24,14 @@ def test_metric_tracker_constructor():
 # def test_metric_tracker_routine(): #TODO revert
 #     # make the trainer
 #     trainer = Trainer(solver=solver,
-#                       callbacks=[
+#                       callback=[
 #                           MetricTracker()
 #                       ],
 #                       accelerator='cpu',
 #                       max_epochs=5)
 #     trainer.train()
 #     # get the tracked metrics
-#     metrics = trainer.callbacks[0].metrics
+#     metrics = trainer.callback[0].metrics
 #     # assert the logged metrics are correct
 #     logged_metrics = sorted(list(metrics.keys()))
 #     assert logged_metrics == ['train_loss_epoch', 'train_loss_step', 'val_loss']

tests/test_callback/test_optimizer_callback.py

@@ -1,8 +1,8 @@
-from pina.callbacks import SwitchOptimizer
+from pina.callback import SwitchOptimizer
 import torch
 import pytest
 
-from pina.solvers import PINN
+from pina.solver import PINN
 from pina.trainer import Trainer
 from pina.model import FeedForward
 from pina.problem.zoo import Poisson2DSquareProblem as Poisson
@@ -31,7 +31,7 @@ def test_switch_optimizer_constructor():
 #     # make the trainer
 #     switch_opt_callback = SwitchOptimizer(lbfgs_optimizer, epoch_switch=3)
 #     trainer = Trainer(solver=solver,
-#                       callbacks=[switch_opt_callback],
+#                       callback=[switch_opt_callback],
 #                       accelerator='cpu',
 #                       max_epochs=5)
 #     trainer.train()

tests/test_callback/test_progress_bar.py

@@ -1,7 +1,7 @@
-from pina.solvers import PINN
+from pina.solver import PINN
 from pina.trainer import Trainer
 from pina.model import FeedForward
-from pina.callbacks.processing_callbacks import PINAProgressBar
+from pina.callback.processing_callback import PINAProgressBar
 from pina.problem.zoo import Poisson2DSquareProblem as Poisson
 
 
@@ -24,7 +24,7 @@ from pina.problem.zoo import Poisson2DSquareProblem as Poisson
 # def test_progress_bar_routine():
 #     # make the trainer
 #     trainer = Trainer(solver=solver,
-#                       callbacks=[
+#                       callback=[
 #                           PINAProgressBar(['mean', 'laplace_D'])
 #                       ],
 #                       accelerator='cpu',

tests/test_collector.py

@@ -7,7 +7,7 @@ from pina.problem import AbstractProblem, SpatialProblem
 from pina.domain import CartesianDomain
 from pina.equation.equation import Equation
 from pina.equation.equation_factory import FixedValue
-from pina.operators import laplacian
+from pina.operator import laplacian
 from pina.collector import Collector
 
 

tests/test_data/test_datamodule.py

@@ -6,7 +6,7 @@ from pina.problem.zoo import SupervisedProblem
 from pina.graph import RadiusGraph
 from pina.data.data_module import DummyDataloader
 from pina import Trainer
-from pina.solvers import SupervisedSolver
+from pina.solver import SupervisedSolver
 from torch_geometric.data import Batch
 from torch.utils.data import DataLoader
 

tests/test_equations/test_equation.py

@@ -1,5 +1,5 @@
 from pina.equation import Equation
-from pina.operators import grad, laplacian
+from pina.operator import grad, laplacian
 from pina import LabelTensor
 import torch
 import pytest

tests/test_equations/test_systemequation.py

@@ -1,5 +1,5 @@
 from pina.equation import SystemEquation
-from pina.operators import grad, laplacian
+from pina.operator import grad, laplacian
 from pina import LabelTensor
 import torch
 import pytest

tests/test_operator.py

@@ -2,7 +2,7 @@ import torch
 import pytest
 
 from pina import LabelTensor
-from pina.operators import grad, div, laplacian
+from pina.operator import grad, div, laplacian
 
 
 def func_vector(x):

tests/test_solver/test_causal_pinn.py

@@ -3,7 +3,7 @@ import pytest
 
 from pina import LabelTensor, Condition
 from pina.problem import SpatialProblem
-from pina.solvers import CausalPINN
+from pina.solver import CausalPINN
 from pina.trainer import Trainer
 from pina.model import FeedForward
 from pina.problem.zoo import (
@@ -118,7 +118,7 @@ def test_solver_test(problem, batch_size, compile):
 
 @pytest.mark.parametrize("problem", [problem, inverse_problem])
 def test_train_load_restore(problem):
-    dir = "tests/test_solvers/tmp"
+    dir = "tests/test_solver/tmp"
     problem = problem
     solver = CausalPINN(model=model, problem=problem)
     trainer = Trainer(solver=solver,
@@ -153,4 +153,4 @@ def test_train_load_restore(problem):
 
     # rm directories
     import shutil
-    shutil.rmtree('tests/test_solvers/tmp')
+    shutil.rmtree('tests/test_solver/tmp')

tests/test_solver/test_competitive_pinn.py

@@ -2,7 +2,7 @@ import torch
 import pytest 
 
 from pina import LabelTensor, Condition
-from pina.solvers import CompetitivePINN as CompPINN
+from pina.solver import CompetitivePINN as CompPINN
 from pina.trainer import Trainer
 from pina.model import FeedForward
 from pina.problem.zoo import (
@@ -107,7 +107,7 @@ def test_solver_test(problem, batch_size, compile):
 
 @pytest.mark.parametrize("problem", [problem, inverse_problem])
 def test_train_load_restore(problem):
-    dir = "tests/test_solvers/tmp"
+    dir = "tests/test_solver/tmp"
     problem = problem
     solver = CompPINN(problem=problem, model=model)
     trainer = Trainer(solver=solver,
@@ -142,4 +142,4 @@ def test_train_load_restore(problem):
 
     # rm directories
     import shutil
-    shutil.rmtree('tests/test_solvers/tmp')
+    shutil.rmtree('tests/test_solver/tmp')

tests/test_solver/test_garom.py

@@ -3,7 +3,7 @@ import torch.nn as nn
 
 import pytest
 from pina import Condition, LabelTensor
-from pina.solvers import GAROM
+from pina.solver import GAROM
 from pina.condition import InputOutputPointsCondition
 from pina.problem import AbstractProblem
 from pina.model import FeedForward
@@ -141,7 +141,7 @@ def test_solver_test(batch_size, compile):
 
 
 def test_train_load_restore():
-    dir = "tests/test_solvers/tmp/"
+    dir = "tests/test_solver/tmp/"
     problem = TensorProblem()
     solver = GAROM(problem=TensorProblem(),
                    generator=Generator(),
@@ -174,4 +174,4 @@ def test_train_load_restore():
 
     # rm directories
     import shutil
-    shutil.rmtree('tests/test_solvers/tmp')
+    shutil.rmtree('tests/test_solver/tmp')

tests/test_solver/test_gradient_pinn.py

@@ -3,7 +3,7 @@ import torch
 
 from pina import LabelTensor, Condition
 from pina.problem import TimeDependentProblem
-from pina.solvers import GradientPINN
+from pina.solver import GradientPINN
 from pina.model import FeedForward
 from pina.trainer import Trainer
 from pina.problem.zoo import (
@@ -117,7 +117,7 @@ def test_solver_test(problem, batch_size, compile):
 
 @pytest.mark.parametrize("problem", [problem, inverse_problem])
 def test_train_load_restore(problem):
-    dir = "tests/test_solvers/tmp"
+    dir = "tests/test_solver/tmp"
     problem = problem
     solver = GradientPINN(model=model, problem=problem)
     trainer = Trainer(solver=solver,
@@ -152,4 +152,4 @@ def test_train_load_restore(problem):
 
     # rm directories
     import shutil
-    shutil.rmtree('tests/test_solvers/tmp')
+    shutil.rmtree('tests/test_solver/tmp')

tests/test_solver/test_pinn.py

@@ -4,7 +4,7 @@ import torch
 from pina import LabelTensor, Condition
 from pina.model import FeedForward
 from pina.trainer import Trainer
-from pina.solvers import PINN
+from pina.solver import PINN
 from pina.condition import (
     InputOutputPointsCondition,
     InputPointsEquationCondition,
@@ -98,7 +98,7 @@ def test_solver_test(problem, batch_size, compile):
 
 @pytest.mark.parametrize("problem", [problem, inverse_problem])
 def test_train_load_restore(problem):
-    dir = "tests/test_solvers/tmp"
+    dir = "tests/test_solver/tmp"
     problem = problem
     solver = PINN(model=model, problem=problem)
     trainer = Trainer(solver=solver,
@@ -131,4 +131,4 @@ def test_train_load_restore(problem):
 
     # rm directories
     import shutil
-    shutil.rmtree('tests/test_solvers/tmp')
+    shutil.rmtree('tests/test_solver/tmp')

tests/test_solver/test_rba_pinn.py

@@ -4,7 +4,7 @@ import torch
 from pina import LabelTensor, Condition
 from pina.model import FeedForward
 from pina.trainer import Trainer
-from pina.solvers import RBAPINN
+from pina.solver import RBAPINN
 from pina.condition import (
     InputOutputPointsCondition,
     InputPointsEquationCondition,
@@ -119,7 +119,7 @@ def test_solver_test(problem, compile):
 
 @pytest.mark.parametrize("problem", [problem, inverse_problem])
 def test_train_load_restore(problem):
-    dir = "tests/test_solvers/tmp"
+    dir = "tests/test_solver/tmp"
     problem = problem
     solver = RBAPINN(model=model, problem=problem)
     trainer = Trainer(solver=solver,
@@ -154,4 +154,4 @@ def test_train_load_restore(problem):
 
     # rm directories
     import shutil
-    shutil.rmtree('tests/test_solvers/tmp')
+    shutil.rmtree('tests/test_solver/tmp')

tests/test_solver/test_rom_solver.py

@@ -4,7 +4,7 @@ import pytest
 from pina import Condition, LabelTensor
 from pina.problem import AbstractProblem
 from pina.condition import InputOutputPointsCondition
-from pina.solvers import ReducedOrderModelSolver
+from pina.solver import ReducedOrderModelSolver
 from pina.trainer import Trainer
 from pina.model import FeedForward
 from pina.problem.zoo import Poisson2DSquareProblem
@@ -149,7 +149,7 @@ def test_solver_test(use_lt, compile):
 
 
 def test_train_load_restore():
-    dir = "tests/test_solvers/tmp/"
+    dir = "tests/test_solver/tmp/"
     problem = LabelTensorProblem()
     solver = ReducedOrderModelSolver(problem=problem,
 
@@ -184,4 +184,4 @@ def test_train_load_restore():
         solver.forward(test_pts))
     # rm directories
     import shutil
-    shutil.rmtree('tests/test_solvers/tmp')
+    shutil.rmtree('tests/test_solver/tmp')

tests/test_solver/test_self_adaptive_pinn.py

@@ -2,7 +2,7 @@ import torch
 import pytest
 
 from pina import LabelTensor, Condition
-from pina.solvers import SelfAdaptivePINN as SAPINN
+from pina.solver import SelfAdaptivePINN as SAPINN
 from pina.trainer import Trainer
 from pina.model import FeedForward
 from pina.problem.zoo import (
@@ -122,7 +122,7 @@ def test_solver_test(problem, compile):
 
 @pytest.mark.parametrize("problem", [problem, inverse_problem])
 def test_train_load_restore(problem):
-    dir = "tests/test_solvers/tmp"
+    dir = "tests/test_solver/tmp"
     problem = problem
     solver = SAPINN(model=model, problem=problem)
     trainer = Trainer(solver=solver,
@@ -156,4 +156,4 @@ def test_train_load_restore(problem):
 
     # rm directories
     import shutil
-    shutil.rmtree('tests/test_solvers/tmp')
+    shutil.rmtree('tests/test_solver/tmp')

tests/test_solver/test_supervised_solver.py

@@ -3,7 +3,7 @@ import pytest
 from pina import Condition, LabelTensor
 from pina.condition import InputOutputPointsCondition
 from pina.problem import AbstractProblem
-from pina.solvers import SupervisedSolver
+from pina.solver import SupervisedSolver
 from pina.model import FeedForward
 from pina.trainer import Trainer
 from torch._dynamo.eval_frame import OptimizedModule
@@ -97,7 +97,7 @@ def test_solver_test(use_lt, compile):
 
 
 def test_train_load_restore():
-    dir = "tests/test_solvers/tmp/"
+    dir = "tests/test_solver/tmp/"
     problem = LabelTensorProblem()
     solver = SupervisedSolver(problem=problem, model=model)
     trainer = Trainer(solver=solver,
@@ -130,4 +130,4 @@ def test_train_load_restore():
 
     # rm directories
     import shutil
-    shutil.rmtree('tests/test_solvers/tmp')
+    shutil.rmtree('tests/test_solver/tmp')

tests/test_weighting/test_standard_weighting.py

@@ -2,7 +2,7 @@ import pytest
 import torch
 
 from pina import Trainer
-from pina.solvers import PINN
+from pina.solver import PINN
 from pina.model import FeedForward
 from pina.problem.zoo import Poisson2DSquareProblem
 from pina.loss import ScalarWeighting