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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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84
tests/test_blocks/test_embedding.py
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@@ -6,55 +6,66 @@ from pina.model.block import PeriodicBoundaryEmbedding, FourierFeatureEmbedding
# test tolerance
tol = 1e-6
def check_same_columns(tensor):
# Get the first column and compute residual
residual = tensor - tensor[0]
zeros = torch.zeros_like(residual)
# Compare each column with the first column
all_same = torch.allclose(input=residual,other=zeros,atol=tol)
all_same = torch.allclose(input=residual, other=zeros, atol=tol)
return all_same
def grad(u, x):
"""
Compute the first derivative of u with respect to x.
"""
return torch.autograd.grad(u, x, grad_outputs=torch.ones_like(u),
create_graph=True, allow_unused=True,
retain_graph=True)[0]
return torch.autograd.grad(
u,
x,
grad_outputs=torch.ones_like(u),
create_graph=True,
allow_unused=True,
retain_graph=True,
)[0]
def test_constructor_PeriodicBoundaryEmbedding():
PeriodicBoundaryEmbedding(input_dimension=1, periods=2)
PeriodicBoundaryEmbedding(input_dimension=1, periods={'x': 3, 'y' : 4})
PeriodicBoundaryEmbedding(input_dimension=1, periods={0: 3, 1 : 4})
PeriodicBoundaryEmbedding(input_dimension=1, periods={"x": 3, "y": 4})
PeriodicBoundaryEmbedding(input_dimension=1, periods={0: 3, 1: 4})
PeriodicBoundaryEmbedding(input_dimension=1, periods=2, output_dimension=10)
with pytest.raises(TypeError):
PeriodicBoundaryEmbedding()
with pytest.raises(ValueError):
PeriodicBoundaryEmbedding(input_dimension=1., periods=1)
PeriodicBoundaryEmbedding(input_dimension=1, periods=1,
output_dimension=1.)
PeriodicBoundaryEmbedding(input_dimension=1, periods={'x':'x'})
PeriodicBoundaryEmbedding(input_dimension=1, periods={0:'x'})
PeriodicBoundaryEmbedding(input_dimension=1.0, periods=1)
PeriodicBoundaryEmbedding(
input_dimension=1, periods=1, output_dimension=1.0
)
PeriodicBoundaryEmbedding(input_dimension=1, periods={"x": "x"})
PeriodicBoundaryEmbedding(input_dimension=1, periods={0: "x"})
@pytest.mark.parametrize("period", [1, 4, 10])
@pytest.mark.parametrize("input_dimension", [1, 2, 3])
def test_forward_backward_same_period_PeriodicBoundaryEmbedding(input_dimension,
period):
def test_forward_backward_same_period_PeriodicBoundaryEmbedding(
input_dimension, period
):
func = torch.nn.Sequential(
PeriodicBoundaryEmbedding(input_dimension=input_dimension,
output_dimension=60, periods=period),
PeriodicBoundaryEmbedding(
input_dimension=input_dimension, output_dimension=60, periods=period
),
torch.nn.Tanh(),
torch.nn.Linear(60, 60),
torch.nn.Tanh(),
torch.nn.Linear(60, 1)
torch.nn.Linear(60, 1),
)
# coordinates
x = period * torch.tensor([[0.],[1.]])
x = period * torch.tensor([[0.0], [1.0]])
if input_dimension == 2:
x = torch.cartesian_prod(x.flatten(),x.flatten())
x = torch.cartesian_prod(x.flatten(), x.flatten())
elif input_dimension == 3:
x = torch.cartesian_prod(x.flatten(),x.flatten(),x.flatten())
x = torch.cartesian_prod(x.flatten(), x.flatten(), x.flatten())
x.requires_grad = True
# output
f = func(x)
@@ -63,29 +74,32 @@ def test_forward_backward_same_period_PeriodicBoundaryEmbedding(input_dimension,
loss = f.mean()
loss.backward()
def test_constructor_FourierFeatureEmbedding():
FourierFeatureEmbedding(input_dimension=1, output_dimension=20,
sigma=1)
with pytest.raises(TypeError):
FourierFeatureEmbedding(input_dimension=1, output_dimension=20, sigma=1)
with pytest.raises(TypeError):
FourierFeatureEmbedding()
with pytest.raises(RuntimeError):
with pytest.raises(RuntimeError):
FourierFeatureEmbedding(input_dimension=1, output_dimension=3, sigma=1)
with pytest.raises(ValueError):
FourierFeatureEmbedding(input_dimension='x', output_dimension=20,
sigma=1)
FourierFeatureEmbedding(input_dimension=1, output_dimension='x',
sigma=1)
FourierFeatureEmbedding(input_dimension=1, output_dimension=20,
sigma='x')
FourierFeatureEmbedding(
input_dimension="x", output_dimension=20, sigma=1
)
FourierFeatureEmbedding(
input_dimension=1, output_dimension="x", sigma=1
)
FourierFeatureEmbedding(
input_dimension=1, output_dimension=20, sigma="x"
)
@pytest.mark.parametrize("output_dimension", [2, 4, 6])
@pytest.mark.parametrize("input_dimension", [1, 2, 3])
@pytest.mark.parametrize("sigma", [10, 1, 0.1])
def test_forward_backward_FourierFeatureEmbedding(input_dimension,
output_dimension,
sigma):
func = FourierFeatureEmbedding(input_dimension, output_dimension,
sigma)
def test_forward_backward_FourierFeatureEmbedding(
input_dimension, output_dimension, sigma
):
func = FourierFeatureEmbedding(input_dimension, output_dimension, sigma)
# coordinates
x = torch.rand((10, input_dimension), requires_grad=True)
# output
@@ -93,4 +107,4 @@ def test_forward_backward_FourierFeatureEmbedding(input_dimension,
assert f.shape[-1] == output_dimension
# compute backward
loss = f.mean()
loss.backward()
loss.backward()