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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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229
tests/test_solver/test_reduced_order_model_solver.py
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@@ -12,22 +12,21 @@ from torch._dynamo.eval_frame import OptimizedModule
class LabelTensorProblem(AbstractProblem):
input_variables = ['u_0', 'u_1']
output_variables = ['u']
input_variables = ["u_0", "u_1"]
output_variables = ["u"]
conditions = {
'data': Condition(
input=LabelTensor(torch.randn(20, 2), ['u_0', 'u_1']),
target=LabelTensor(torch.randn(20, 1), ['u'])),
"data": Condition(
input=LabelTensor(torch.randn(20, 2), ["u_0", "u_1"]),
target=LabelTensor(torch.randn(20, 1), ["u"]),
),
}
class TensorProblem(AbstractProblem):
input_variables = ['u_0', 'u_1']
output_variables = ['u']
input_variables = ["u_0", "u_1"]
output_variables = ["u"]
conditions = {
'data': Condition(
input=torch.randn(20, 2),
target=torch.randn(20, 1))
"data": Condition(input=torch.randn(20, 2), target=torch.randn(20, 1))
}
@@ -35,23 +34,27 @@ class AE(torch.nn.Module):
def __init__(self, input_dimensions, rank):
super().__init__()
self.encode = FeedForward(
input_dimensions, rank, layers=[input_dimensions//4])
input_dimensions, rank, layers=[input_dimensions // 4]
)
self.decode = FeedForward(
rank, input_dimensions, layers=[input_dimensions//4])
rank, input_dimensions, layers=[input_dimensions // 4]
)
class AE_missing_encode(torch.nn.Module):
def __init__(self, input_dimensions, rank):
super().__init__()
self.encode = FeedForward(
input_dimensions, rank, layers=[input_dimensions//4])
input_dimensions, rank, layers=[input_dimensions // 4]
)
class AE_missing_decode(torch.nn.Module):
def __init__(self, input_dimensions, rank):
super().__init__()
self.decode = FeedForward(
rank, input_dimensions, layers=[input_dimensions//4])
rank, input_dimensions, layers=[input_dimensions // 4]
)
rank = 10
@@ -62,26 +65,41 @@ reduction_net = AE(1, rank)
def test_constructor():
problem = TensorProblem()
ReducedOrderModelSolver(problem=problem,
interpolation_network=interpolation_net,
reduction_network=reduction_net)
ReducedOrderModelSolver(problem=LabelTensorProblem(),
reduction_network=reduction_net,
interpolation_network=interpolation_net)
assert ReducedOrderModelSolver.accepted_conditions_types == InputTargetCondition
ReducedOrderModelSolver(
problem=problem,
interpolation_network=interpolation_net,
reduction_network=reduction_net,
)
ReducedOrderModelSolver(
problem=LabelTensorProblem(),
reduction_network=reduction_net,
interpolation_network=interpolation_net,
)
assert (
ReducedOrderModelSolver.accepted_conditions_types
== InputTargetCondition
)
with pytest.raises(SyntaxError):
ReducedOrderModelSolver(problem=problem,
reduction_network=AE_missing_encode(
len(problem.output_variables), rank),
interpolation_network=interpolation_net)
ReducedOrderModelSolver(problem=problem,
reduction_network=AE_missing_decode(
len(problem.output_variables), rank),
interpolation_network=interpolation_net)
ReducedOrderModelSolver(
problem=problem,
reduction_network=AE_missing_encode(
len(problem.output_variables), rank
),
interpolation_network=interpolation_net,
)
ReducedOrderModelSolver(
problem=problem,
reduction_network=AE_missing_decode(
len(problem.output_variables), rank
),
interpolation_network=interpolation_net,
)
with pytest.raises(ValueError):
ReducedOrderModelSolver(problem=Poisson2DSquareProblem(),
reduction_network=reduction_net,
interpolation_network=interpolation_net)
ReducedOrderModelSolver(
problem=Poisson2DSquareProblem(),
reduction_network=reduction_net,
interpolation_network=interpolation_net,
)
@pytest.mark.parametrize("batch_size", [None, 1, 5, 20])
@@ -89,99 +107,122 @@ def test_constructor():
@pytest.mark.parametrize("compile", [True, False])
def test_solver_train(use_lt, batch_size, compile):
problem = LabelTensorProblem() if use_lt else TensorProblem()
solver = ReducedOrderModelSolver(problem=problem,
reduction_network=reduction_net,
interpolation_network=interpolation_net, use_lt=use_lt)
trainer = Trainer(solver=solver,
max_epochs=2,
accelerator='cpu',
batch_size=batch_size,
train_size=1.,
test_size=0.,
val_size=0.,
compile=compile)
solver = ReducedOrderModelSolver(
problem=problem,
reduction_network=reduction_net,
interpolation_network=interpolation_net,
use_lt=use_lt,
)
trainer = Trainer(
solver=solver,
max_epochs=2,
accelerator="cpu",
batch_size=batch_size,
train_size=1.0,
test_size=0.0,
val_size=0.0,
compile=compile,
)
trainer.train()
if trainer.compile:
for v in solver.model.values():
assert (isinstance(v, OptimizedModule))
assert isinstance(v, OptimizedModule)
@pytest.mark.parametrize("use_lt", [True, False])
@pytest.mark.parametrize("compile", [True, False])
def test_solver_validation(use_lt, compile):
problem = LabelTensorProblem() if use_lt else TensorProblem()
solver = ReducedOrderModelSolver(problem=problem,
reduction_network=reduction_net,
interpolation_network=interpolation_net, use_lt=use_lt)
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)
solver = ReducedOrderModelSolver(
problem=problem,
reduction_network=reduction_net,
interpolation_network=interpolation_net,
use_lt=use_lt,
)
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:
for v in solver.model.values():
assert (isinstance(v, OptimizedModule))
assert isinstance(v, OptimizedModule)
@pytest.mark.parametrize("use_lt", [True, False])
@pytest.mark.parametrize("compile", [True, False])
def test_solver_test(use_lt, compile):
problem = LabelTensorProblem() if use_lt else TensorProblem()
solver = ReducedOrderModelSolver(problem=problem,
reduction_network=reduction_net,
interpolation_network=interpolation_net, use_lt=use_lt)
trainer = Trainer(solver=solver,
max_epochs=2,
accelerator='cpu',
batch_size=None,
train_size=0.8,
val_size=0.1,
test_size=0.1,
compile=compile)
solver = ReducedOrderModelSolver(
problem=problem,
reduction_network=reduction_net,
interpolation_network=interpolation_net,
use_lt=use_lt,
)
trainer = Trainer(
solver=solver,
max_epochs=2,
accelerator="cpu",
batch_size=None,
train_size=0.8,
val_size=0.1,
test_size=0.1,
compile=compile,
)
trainer.train()
if trainer.compile:
for v in solver.model.values():
assert (isinstance(v, OptimizedModule))
assert isinstance(v, OptimizedModule)
def test_train_load_restore():
dir = "tests/test_solver/tmp/"
problem = LabelTensorProblem()
solver = ReducedOrderModelSolver(problem=problem,
reduction_network=reduction_net,
interpolation_network=interpolation_net)
trainer = Trainer(solver=solver,
max_epochs=5,
accelerator='cpu',
batch_size=None,
train_size=0.9,
test_size=0.1,
val_size=0.,
default_root_dir=dir)
trainer.train()
# restore
ntrainer = Trainer(solver=solver,
max_epochs=5,
accelerator='cpu',)
ntrainer.train(
ckpt_path=f'{dir}/lightning_logs/version_0/checkpoints/epoch=4-step=5.ckpt')
# loading
new_solver = ReducedOrderModelSolver.load_from_checkpoint(
f'{dir}/lightning_logs/version_0/checkpoints/epoch=4-step=5.ckpt',
solver = ReducedOrderModelSolver(
problem=problem,
reduction_network=reduction_net,
interpolation_network=interpolation_net)
interpolation_network=interpolation_net,
)
trainer = Trainer(
solver=solver,
max_epochs=5,
accelerator="cpu",
batch_size=None,
train_size=0.9,
test_size=0.1,
val_size=0.0,
default_root_dir=dir,
)
trainer.train()
# restore
ntrainer = Trainer(
solver=solver,
max_epochs=5,
accelerator="cpu",
)
ntrainer.train(
ckpt_path=f"{dir}/lightning_logs/version_0/checkpoints/epoch=4-step=5.ckpt"
)
# loading
new_solver = ReducedOrderModelSolver.load_from_checkpoint(
f"{dir}/lightning_logs/version_0/checkpoints/epoch=4-step=5.ckpt",
problem=problem,
reduction_network=reduction_net,
interpolation_network=interpolation_net,
)
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))
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")