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Correct codacy warnings

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FilippoOlivo
2024-10-22 14:26:39 +02:00
committed by Nicola Demo
parent c9304fb9bb
commit 1bc1b3a580
15 changed files with 252 additions and 210 deletions
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98
tests/test_dataset.py
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@@ -29,34 +29,49 @@ class Poisson(SpatialProblem):
spatial_domain = CartesianDomain({'x': [0, 1], 'y': [0, 1]})
conditions = {
'gamma1': Condition(
domain=CartesianDomain({'x': [0, 1], 'y': 1}),
equation=FixedValue(0.0)),
'gamma2': Condition(
domain=CartesianDomain({'x': [0, 1], 'y': 0}),
equation=FixedValue(0.0)),
'gamma3': Condition(
domain=CartesianDomain({'x': 1, 'y': [0, 1]}),
equation=FixedValue(0.0)),
'gamma4': Condition(
domain=CartesianDomain({'x': 0, 'y': [0, 1]}),
equation=FixedValue(0.0)),
'D': Condition(
input_points=LabelTensor(torch.rand(size=(100, 2)), ['x', 'y']),
equation=my_laplace),
'data': Condition(
input_points=in_,
output_points=out_),
'data2': Condition(
input_points=in2_,
output_points=out2_),
'unsupervised': Condition(
'gamma1':
Condition(domain=CartesianDomain({
'x': [0, 1],
'y': 1
}),
equation=FixedValue(0.0)),
'gamma2':
Condition(domain=CartesianDomain({
'x': [0, 1],
'y': 0
}),
equation=FixedValue(0.0)),
'gamma3':
Condition(domain=CartesianDomain({
'x': 1,
'y': [0, 1]
}),
equation=FixedValue(0.0)),
'gamma4':
Condition(domain=CartesianDomain({
'x': 0,
'y': [0, 1]
}),
equation=FixedValue(0.0)),
'D':
Condition(input_points=LabelTensor(torch.rand(size=(100, 2)),
['x', 'y']),
equation=my_laplace),
'data':
Condition(input_points=in_, output_points=out_),
'data2':
Condition(input_points=in2_, output_points=out2_),
'unsupervised':
Condition(
input_points=LabelTensor(torch.rand(size=(45, 2)), ['x', 'y']),
conditional_variables=LabelTensor(torch.ones(size=(45, 1)), ['alpha']),
conditional_variables=LabelTensor(torch.ones(size=(45, 1)),
['alpha']),
),
'unsupervised2': Condition(
'unsupervised2':
Condition(
input_points=LabelTensor(torch.rand(size=(90, 2)), ['x', 'y']),
conditional_variables=LabelTensor(torch.ones(size=(90, 1)), ['alpha']),
conditional_variables=LabelTensor(torch.ones(size=(90, 1)),
['alpha']),
)
}
@@ -113,32 +128,49 @@ def test_data_module():
assert isinstance(loader, PinaDataLoader)
assert isinstance(loader, PinaDataLoader)
data_module = PinaDataModule(poisson, device='cpu', batch_size=10, shuffle=False)
data_module = PinaDataModule(poisson,
device='cpu',
batch_size=10,
shuffle=False)
data_module.setup()
loader = data_module.train_dataloader()
assert len(loader) == 24
for i in loader:
assert len(i) <= 10
len_ref = sum([math.ceil(len(dataset) * 0.7) for dataset in data_module.datasets])
len_real = sum([len(dataset) for dataset in data_module.splits['train'].values()])
len_ref = sum(
[math.ceil(len(dataset) * 0.7) for dataset in data_module.datasets])
len_real = sum(
[len(dataset) for dataset in data_module.splits['train'].values()])
assert len_ref == len_real
supervised_dataset = SupervisedDataset(poisson, device='cpu')
data_module = PinaDataModule(poisson, device='cpu', batch_size=10, shuffle=False, datasets=[supervised_dataset])
data_module = PinaDataModule(poisson,
device='cpu',
batch_size=10,
shuffle=False,
datasets=[supervised_dataset])
data_module.setup()
loader = data_module.train_dataloader()
for batch in loader:
assert len(batch) <= 10
physics_dataset = SamplePointDataset(poisson, device='cpu')
data_module = PinaDataModule(poisson, device='cpu', batch_size=10, shuffle=False, datasets=[physics_dataset])
data_module = PinaDataModule(poisson,
device='cpu',
batch_size=10,
shuffle=False,
datasets=[physics_dataset])
data_module.setup()
loader = data_module.train_dataloader()
for batch in loader:
assert len(batch) <= 10
unsupervised_dataset = UnsupervisedDataset(poisson, device='cpu')
data_module = PinaDataModule(poisson, device='cpu', batch_size=10, shuffle=False, datasets=[unsupervised_dataset])
data_module = PinaDataModule(poisson,
device='cpu',
batch_size=10,
shuffle=False,
datasets=[unsupervised_dataset])
data_module.setup()
loader = data_module.train_dataloader()
for batch in loader:
@@ -159,4 +191,6 @@ def test_loader():
assert i.supervised.input_points.requires_grad == True
assert i.physics.input_points.requires_grad == True
assert i.unsupervised.input_points.requires_grad == True
test_loader()
test_loader()