folivo/PINA
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Fix Codacy Warnings (#477)

Browse Source 
---------

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
Download Patch File Download Diff File Expand all files Collapse all files

193
tests/test_model/test_average_neural_operator.py
View File

@@ -8,139 +8,166 @@ batch_size = 15
n_layers = 4
embedding_dim = 24
func = torch.nn.Tanh
coordinates_indices = ['p']
field_indices = ['v']
coordinates_indices = ["p"]
field_indices = ["v"]
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(field_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(field_indices), len(field_indices)
)
AveragingNeuralOperator(
lifting_net=lifting_net,
projecting_net=projecting_net,
coordinates_indices=coordinates_indices,
field_indices=field_indices,
n_layers=n_layers,
func=func)
func=func,
)
# not working constructor
with pytest.raises(ValueError):
AveragingNeuralOperator(
lifting_net=lifting_net,
projecting_net=projecting_net,
coordinates_indices=coordinates_indices,
field_indices=field_indices,
n_layers=3.2, # wrong
func=func)
lifting_net=lifting_net,
projecting_net=projecting_net,
coordinates_indices=coordinates_indices,
field_indices=field_indices,
n_layers=3.2, # wrong
func=func,
)
AveragingNeuralOperator(
lifting_net=lifting_net,
projecting_net=projecting_net,
coordinates_indices=coordinates_indices,
field_indices=field_indices,
n_layers=n_layers,
func=1) # wrong
AveragingNeuralOperator(
lifting_net=[0], # wrong
projecting_net=projecting_net,
coordinates_indices=coordinates_indices,
field_indices=field_indices,
n_layers=n_layers,
func=func)
AveragingNeuralOperator(
lifting_net=lifting_net,
projecting_net=[0], # wront
coordinates_indices=coordinates_indices,
field_indices=field_indices,
n_layers=n_layers,
func=func)
AveragingNeuralOperator(
lifting_net=lifting_net,
projecting_net=projecting_net,
coordinates_indices=[0], #wrong
field_indices=field_indices,
n_layers=n_layers,
func=func)
AveragingNeuralOperator(
lifting_net=lifting_net,
projecting_net=projecting_net,
coordinates_indices=coordinates_indices,
field_indices=[0], #wrong
n_layers=n_layers,
func=func)
lifting_net = torch.nn.Linear(len(coordinates_indices),
embedding_dim)
AveragingNeuralOperator(
lifting_net=lifting_net,
projecting_net=projecting_net,
coordinates_indices=coordinates_indices,
field_indices=field_indices,
n_layers=n_layers,
func=func)
lifting_net = torch.nn.Linear(len(coordinates_indices) + len(field_indices),
embedding_dim)
projecting_net = torch.nn.Linear(embedding_dim,
len(field_indices))
func=1,
) # wrong
AveragingNeuralOperator(
lifting_net=[0], # wrong
projecting_net=projecting_net,
coordinates_indices=coordinates_indices,
field_indices=field_indices,
n_layers=n_layers,
func=func,
)
AveragingNeuralOperator(
lifting_net=lifting_net,
projecting_net=[0], # wront
coordinates_indices=coordinates_indices,
field_indices=field_indices,
n_layers=n_layers,
func=func,
)
AveragingNeuralOperator(
lifting_net=lifting_net,
projecting_net=projecting_net,
coordinates_indices=[0], # wrong
field_indices=field_indices,
n_layers=n_layers,
func=func,
)
AveragingNeuralOperator(
lifting_net=lifting_net,
projecting_net=projecting_net,
coordinates_indices=coordinates_indices,
field_indices=[0], # wrong
n_layers=n_layers,
func=func,
)
lifting_net = torch.nn.Linear(len(coordinates_indices), embedding_dim)
AveragingNeuralOperator(
lifting_net=lifting_net,
projecting_net=projecting_net,
coordinates_indices=coordinates_indices,
field_indices=field_indices,
n_layers=n_layers,
func=func)
func=func,
)
lifting_net = torch.nn.Linear(
len(coordinates_indices) + len(field_indices), embedding_dim
)
projecting_net = torch.nn.Linear(embedding_dim, len(field_indices))
AveragingNeuralOperator(
lifting_net=lifting_net,
projecting_net=projecting_net,
coordinates_indices=coordinates_indices,
field_indices=field_indices,
n_layers=n_layers,
func=func,
)
def test_forward():
lifting_net = torch.nn.Linear(len(coordinates_indices) + len(field_indices),
embedding_dim)
projecting_net = torch.nn.Linear(embedding_dim + len(field_indices),
len(field_indices))
avno=AveragingNeuralOperator(
lifting_net = torch.nn.Linear(
len(coordinates_indices) + len(field_indices), embedding_dim
)
projecting_net = torch.nn.Linear(
embedding_dim + len(field_indices), len(field_indices)
)
avno = AveragingNeuralOperator(
lifting_net=lifting_net,
projecting_net=projecting_net,
coordinates_indices=coordinates_indices,
field_indices=field_indices,
n_layers=n_layers,
func=func)
func=func,
)
input_ = LabelTensor(
torch.rand(batch_size, 100,
len(coordinates_indices) + len(field_indices)), ['p', 'v'])
torch.rand(
batch_size, 100, len(coordinates_indices) + len(field_indices)
),
["p", "v"],
)
out = avno(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(field_indices),
len(field_indices))
avno=AveragingNeuralOperator(
lifting_net = torch.nn.Linear(
len(coordinates_indices) + len(field_indices), embedding_dim
)
projecting_net = torch.nn.Linear(
embedding_dim + len(field_indices), len(field_indices)
)
avno = AveragingNeuralOperator(
lifting_net=lifting_net,
projecting_net=projecting_net,
coordinates_indices=coordinates_indices,
field_indices=field_indices,
n_layers=n_layers,
func=func)
func=func,
)
input_ = LabelTensor(
torch.rand(batch_size, 100,
len(coordinates_indices) + len(field_indices)), ['p', 'v'])
torch.rand(
batch_size, 100, len(coordinates_indices) + len(field_indices)
),
["p", "v"],
)
input_ = input_.requires_grad_()
out = avno(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),
]
)