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Fix Codacy Warnings (#477)

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---------

Co-authored-by: Dario Coscia <dariocos99@gmail.com>
This commit is contained in:
Filippo Olivo
2025-03-10 15:38:45 +01:00
committed by Nicola Demo
parent e3790e049a
commit 4177bfbb50
157 changed files with 3473 additions and 3839 deletions
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148
tests/test_solver/test_self_adaptive_pinn.py
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@@ -7,12 +7,12 @@ from pina.trainer import Trainer
from pina.model import FeedForward
from pina.problem.zoo import (
Poisson2DSquareProblem as Poisson,
InversePoisson2DSquareProblem as InversePoisson
InversePoisson2DSquareProblem as InversePoisson,
)
from pina.condition import (
InputTargetCondition,
InputEquationCondition,
DomainEquationCondition
DomainEquationCondition,
)
from torch._dynamo.eval_frame import OptimizedModule
@@ -22,20 +22,14 @@ problem = Poisson()
problem.discretise_domain(50)
inverse_problem = InversePoisson()
inverse_problem.discretise_domain(50)
model = FeedForward(
len(problem.input_variables),
len(problem.output_variables)
)
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=input_pts,
target=output_pts
)
problem.conditions["data"] = Condition(input=input_pts, target=output_pts)
@pytest.mark.parametrize("problem", [problem, inverse_problem])
@@ -48,7 +42,7 @@ def test_constructor(problem, weight_fn):
assert solver.accepted_conditions_types == (
InputTargetCondition,
InputEquationCondition,
DomainEquationCondition
DomainEquationCondition,
)
@@ -56,13 +50,15 @@ def test_constructor(problem, weight_fn):
def test_wrong_batch(problem):
with pytest.raises(NotImplementedError):
solver = SAPINN(model=model, problem=problem)
trainer = Trainer(solver=solver,
max_epochs=2,
accelerator='cpu',
batch_size=10,
train_size=1.,
val_size=0.,
test_size=0.)
trainer = Trainer(
solver=solver,
max_epochs=2,
accelerator="cpu",
batch_size=10,
train_size=1.0,
val_size=0.0,
test_size=0.0,
)
trainer.train()
@@ -70,54 +66,72 @@ def test_wrong_batch(problem):
@pytest.mark.parametrize("compile", [True, False])
def test_solver_train(problem, compile):
solver = SAPINN(model=model, problem=problem)
trainer = Trainer(solver=solver,
max_epochs=2,
accelerator='cpu',
batch_size=None,
train_size=1.,
val_size=0.,
test_size=0.,
compile=compile)
trainer = Trainer(
solver=solver,
max_epochs=2,
accelerator="cpu",
batch_size=None,
train_size=1.0,
val_size=0.0,
test_size=0.0,
compile=compile,
)
trainer.train()
if trainer.compile:
assert (all([isinstance(model, (OptimizedModule, torch.nn.ModuleDict))
for model in solver.models]))
assert all(
[
isinstance(model, (OptimizedModule, torch.nn.ModuleDict))
for model in solver.models
]
)
@pytest.mark.parametrize("problem", [problem, inverse_problem])
@pytest.mark.parametrize("compile", [True, False])
def test_solver_validation(problem, compile):
solver = SAPINN(model=model, problem=problem)
trainer = Trainer(solver=solver,
max_epochs=2,
accelerator='cpu',
batch_size=None,
train_size=0.9,
val_size=0.1,
test_size=0.,
compile=compile)
trainer = Trainer(
solver=solver,
max_epochs=2,
accelerator="cpu",
batch_size=None,
train_size=0.9,
val_size=0.1,
test_size=0.0,
compile=compile,
)
trainer.train()
if trainer.compile:
assert (all([isinstance(model, (OptimizedModule, torch.nn.ModuleDict))
for model in solver.models]))
assert all(
[
isinstance(model, (OptimizedModule, torch.nn.ModuleDict))
for model in solver.models
]
)
@pytest.mark.parametrize("problem", [problem, inverse_problem])
@pytest.mark.parametrize("compile", [True, False])
def test_solver_test(problem, compile):
solver = SAPINN(model=model, problem=problem)
trainer = Trainer(solver=solver,
max_epochs=2,
accelerator='cpu',
batch_size=None,
train_size=0.7,
val_size=0.2,
test_size=0.1,
compile=compile)
trainer = Trainer(
solver=solver,
max_epochs=2,
accelerator="cpu",
batch_size=None,
train_size=0.7,
val_size=0.2,
test_size=0.1,
compile=compile,
)
trainer.test()
if trainer.compile:
assert (all([isinstance(model, (OptimizedModule, torch.nn.ModuleDict))
for model in solver.models]))
assert all(
[
isinstance(model, (OptimizedModule, torch.nn.ModuleDict))
for model in solver.models
]
)
@pytest.mark.parametrize("problem", [problem, inverse_problem])
@@ -125,25 +139,30 @@ def test_train_load_restore(problem):
dir = "tests/test_solver/tmp"
problem = problem
solver = SAPINN(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 = 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 = 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')
ckpt_path=f"{dir}/lightning_logs/version_0/checkpoints/"
+ "epoch=4-step=5.ckpt"
)
# loading
new_solver = SAPINN.load_from_checkpoint(
f'{dir}/lightning_logs/version_0/checkpoints/epoch=4-step=5.ckpt',
problem=problem, model=model)
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)
@@ -151,9 +170,10 @@ def test_train_load_restore(problem):
solver.forward(test_pts).shape
)
torch.testing.assert_close(
new_solver.forward(test_pts),
solver.forward(test_pts))
new_solver.forward(test_pts), solver.forward(test_pts)
)
# rm directories
import shutil
shutil.rmtree('tests/test_solver/tmp')
shutil.rmtree("tests/test_solver/tmp")