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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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161
tests/test_model/test_low_rank_neural_operator.py
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@@ -10,132 +10,157 @@ embedding_dim = 24
func = torch.nn.Tanh
rank = 4
n_kernel_layers = 3
field_indices = ['u']
coordinates_indices = ['x', 'y']
field_indices = ["u"]
coordinates_indices = ["x", "y"]
def test_constructor():
# working constructor
lifting_net = torch.nn.Linear(len(coordinates_indices) + len(field_indices),
embedding_dim)
projecting_net = torch.nn.Linear(embedding_dim + len(coordinates_indices),
len(field_indices))
lifting_net = torch.nn.Linear(
len(coordinates_indices) + len(field_indices), embedding_dim
)
projecting_net = torch.nn.Linear(
embedding_dim + len(coordinates_indices), len(field_indices)
)
LowRankNeuralOperator(
lifting_net=lifting_net,
projecting_net=projecting_net,
coordinates_indices=coordinates_indices,
field_indices=field_indices,
n_kernel_layers=n_kernel_layers,
rank=rank)
rank=rank,
)
# not working constructor
with pytest.raises(ValueError):
LowRankNeuralOperator(
lifting_net=lifting_net,
projecting_net=projecting_net,
coordinates_indices=coordinates_indices,
field_indices=field_indices,
n_kernel_layers=3.2, # wrong
rank=rank)
lifting_net=lifting_net,
projecting_net=projecting_net,
coordinates_indices=coordinates_indices,
field_indices=field_indices,
n_kernel_layers=3.2, # wrong
rank=rank,
)
LowRankNeuralOperator(
lifting_net=[0], # wrong
projecting_net=projecting_net,
coordinates_indices=coordinates_indices,
field_indices=field_indices,
n_kernel_layers=n_kernel_layers,
rank=rank)
lifting_net=[0], # wrong
projecting_net=projecting_net,
coordinates_indices=coordinates_indices,
field_indices=field_indices,
n_kernel_layers=n_kernel_layers,
rank=rank,
)
LowRankNeuralOperator(
lifting_net=lifting_net,
projecting_net=[0], # wront
coordinates_indices=coordinates_indices,
field_indices=field_indices,
n_kernel_layers=n_kernel_layers,
rank=rank)
lifting_net=lifting_net,
projecting_net=[0], # wront
coordinates_indices=coordinates_indices,
field_indices=field_indices,
n_kernel_layers=n_kernel_layers,
rank=rank,
)
LowRankNeuralOperator(
lifting_net=lifting_net,
projecting_net=projecting_net,
coordinates_indices=[0], #wrong
field_indices=field_indices,
n_kernel_layers=n_kernel_layers,
rank=rank)
lifting_net=lifting_net,
projecting_net=projecting_net,
coordinates_indices=[0], # wrong
field_indices=field_indices,
n_kernel_layers=n_kernel_layers,
rank=rank,
)
LowRankNeuralOperator(
lifting_net=lifting_net,
projecting_net=projecting_net,
coordinates_indices=coordinates_indices,
field_indices=[0], #wrong
n_kernel_layers=n_kernel_layers,
rank=rank)
lifting_net=lifting_net,
projecting_net=projecting_net,
coordinates_indices=coordinates_indices,
field_indices=[0], # wrong
n_kernel_layers=n_kernel_layers,
rank=rank,
)
lifting_net = torch.nn.Linear(len(coordinates_indices),
embedding_dim)
lifting_net = torch.nn.Linear(len(coordinates_indices), embedding_dim)
LowRankNeuralOperator(
lifting_net=lifting_net,
projecting_net=projecting_net,
coordinates_indices=coordinates_indices,
field_indices=field_indices,
n_kernel_layers=n_kernel_layers,
rank=rank)
lifting_net = torch.nn.Linear(len(coordinates_indices) + len(field_indices),
embedding_dim)
projecting_net = torch.nn.Linear(embedding_dim,
len(field_indices))
rank=rank,
)
lifting_net = torch.nn.Linear(
len(coordinates_indices) + len(field_indices), embedding_dim
)
projecting_net = torch.nn.Linear(embedding_dim, len(field_indices))
LowRankNeuralOperator(
lifting_net=lifting_net,
projecting_net=projecting_net,
coordinates_indices=coordinates_indices,
field_indices=field_indices,
n_kernel_layers=n_kernel_layers,
rank=rank)
rank=rank,
)
def test_forward():
lifting_net = torch.nn.Linear(len(coordinates_indices) + len(field_indices),
embedding_dim)
projecting_net = torch.nn.Linear(embedding_dim + len(coordinates_indices),
len(field_indices))
lifting_net = torch.nn.Linear(
len(coordinates_indices) + len(field_indices), embedding_dim
)
projecting_net = torch.nn.Linear(
embedding_dim + len(coordinates_indices), len(field_indices)
)
lno = LowRankNeuralOperator(
lifting_net=lifting_net,
projecting_net=projecting_net,
coordinates_indices=coordinates_indices,
field_indices=field_indices,
n_kernel_layers=n_kernel_layers,
rank=rank)
rank=rank,
)
input_ = LabelTensor(
torch.rand(batch_size, 100,
len(coordinates_indices) + len(field_indices)),
coordinates_indices + field_indices)
torch.rand(
batch_size, 100, len(coordinates_indices) + len(field_indices)
),
coordinates_indices + field_indices,
)
out = lno(input_)
assert out.shape == torch.Size(
[batch_size, input_.shape[1], len(field_indices)])
[batch_size, input_.shape[1], len(field_indices)]
)
def test_backward():
lifting_net = torch.nn.Linear(len(coordinates_indices) + len(field_indices),
embedding_dim)
projecting_net = torch.nn.Linear(embedding_dim + len(coordinates_indices),
len(field_indices))
lno=LowRankNeuralOperator(
lifting_net = torch.nn.Linear(
len(coordinates_indices) + len(field_indices), embedding_dim
)
projecting_net = torch.nn.Linear(
embedding_dim + len(coordinates_indices), len(field_indices)
)
lno = LowRankNeuralOperator(
lifting_net=lifting_net,
projecting_net=projecting_net,
coordinates_indices=coordinates_indices,
field_indices=field_indices,
n_kernel_layers=n_kernel_layers,
rank=rank)
rank=rank,
)
input_ = LabelTensor(
torch.rand(batch_size, 100,
len(coordinates_indices) + len(field_indices)),
coordinates_indices + field_indices)
torch.rand(
batch_size, 100, len(coordinates_indices) + len(field_indices)
),
coordinates_indices + field_indices,
)
input_ = input_.requires_grad_()
out = lno(input_)
tmp = torch.linalg.norm(out)
tmp.backward()
grad = input_.grad
assert grad.shape == torch.Size(
[batch_size, input_.shape[1],
len(coordinates_indices) + len(field_indices)])
[
batch_size,
input_.shape[1],
len(coordinates_indices) + len(field_indices),
]
)