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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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49
tests/test_model/test_kernel_neural_operator.py
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@@ -10,29 +10,46 @@ data = torch.rand(size=(batch_size, numb, input_dim), requires_grad=True)
output_shape = torch.Size([batch_size, numb, output_dim])
lifting_operator = FeedForward(input_dimensions=input_dim, output_dimensions=embedding_dim)
projection_operator = FeedForward(input_dimensions=embedding_dim, output_dimensions=output_dim)
integral_kernels = torch.nn.Sequential(FeedForward(input_dimensions=embedding_dim,
output_dimensions=embedding_dim),
FeedForward(input_dimensions=embedding_dim,
output_dimensions=embedding_dim),)
lifting_operator = FeedForward(
input_dimensions=input_dim, output_dimensions=embedding_dim
)
projection_operator = FeedForward(
input_dimensions=embedding_dim, output_dimensions=output_dim
)
integral_kernels = torch.nn.Sequential(
FeedForward(
input_dimensions=embedding_dim, output_dimensions=embedding_dim
),
FeedForward(
input_dimensions=embedding_dim, output_dimensions=embedding_dim
),
)
def test_constructor():
KernelNeuralOperator(lifting_operator=lifting_operator,
integral_kernels=integral_kernels,
projection_operator=projection_operator)
KernelNeuralOperator(
lifting_operator=lifting_operator,
integral_kernels=integral_kernels,
projection_operator=projection_operator,
)
def test_forward():
operator = KernelNeuralOperator(lifting_operator=lifting_operator,
integral_kernels=integral_kernels,
projection_operator=projection_operator)
operator = KernelNeuralOperator(
lifting_operator=lifting_operator,
integral_kernels=integral_kernels,
projection_operator=projection_operator,
)
out = operator(data)
assert out.shape == output_shape
def test_backward():
operator = KernelNeuralOperator(lifting_operator=lifting_operator,
integral_kernels=integral_kernels,
projection_operator=projection_operator)
operator = KernelNeuralOperator(
lifting_operator=lifting_operator,
integral_kernels=integral_kernels,
projection_operator=projection_operator,
)
out = operator(data)
loss = torch.nn.functional.mse_loss(out, torch.zeros_like(out))
loss.backward()