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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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165
tests/test_model/test_fourier_neural_operator.py
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@@ -13,36 +13,44 @@ def test_constructor():
projecting_net = torch.nn.Linear(60, output_channels)
# simple constructor
FNO(lifting_net=lifting_net,
FNO(
lifting_net=lifting_net,
projecting_net=projecting_net,
n_modes=5,
dimensions=3,
inner_size=60,
n_layers=5)
n_layers=5,
)
# simple constructor with n_modes list
FNO(lifting_net=lifting_net,
FNO(
lifting_net=lifting_net,
projecting_net=projecting_net,
n_modes=[5, 3, 2],
dimensions=3,
inner_size=60,
n_layers=5)
n_layers=5,
)
# simple constructor with n_modes list of list
FNO(lifting_net=lifting_net,
FNO(
lifting_net=lifting_net,
projecting_net=projecting_net,
n_modes=[[5, 3, 2], [5, 3, 2]],
dimensions=3,
inner_size=60,
n_layers=2)
n_layers=2,
)
# simple constructor with n_modes list of list
projecting_net = torch.nn.Linear(50, output_channels)
FNO(lifting_net=lifting_net,
FNO(
lifting_net=lifting_net,
projecting_net=projecting_net,
n_modes=5,
dimensions=3,
layers=[50, 50])
layers=[50, 50],
)
def test_1d_forward():
@@ -50,12 +58,14 @@ def test_1d_forward():
input_ = torch.rand(batch_size, resolution[0], input_channels)
lifting_net = torch.nn.Linear(input_channels, lifting_dim)
projecting_net = torch.nn.Linear(60, output_channels)
fno = FNO(lifting_net=lifting_net,
projecting_net=projecting_net,
n_modes=5,
dimensions=1,
inner_size=60,
n_layers=2)
fno = FNO(
lifting_net=lifting_net,
projecting_net=projecting_net,
n_modes=5,
dimensions=1,
inner_size=60,
n_layers=2,
)
out = fno(input_)
assert out.shape == torch.Size([batch_size, resolution[0], output_channels])
@@ -65,91 +75,120 @@ def test_1d_backward():
input_ = torch.rand(batch_size, resolution[0], input_channels)
lifting_net = torch.nn.Linear(input_channels, lifting_dim)
projecting_net = torch.nn.Linear(60, output_channels)
fno = FNO(lifting_net=lifting_net,
projecting_net=projecting_net,
n_modes=5,
dimensions=1,
inner_size=60,
n_layers=2)
fno = FNO(
lifting_net=lifting_net,
projecting_net=projecting_net,
n_modes=5,
dimensions=1,
inner_size=60,
n_layers=2,
)
input_.requires_grad = True
out = fno(input_)
l = torch.mean(out)
l.backward()
assert input_.grad.shape == torch.Size([batch_size, resolution[0], input_channels])
assert input_.grad.shape == torch.Size(
[batch_size, resolution[0], input_channels]
)
def test_2d_forward():
input_channels = 2
input_ = torch.rand(batch_size, resolution[0], resolution[1],
input_channels)
input_ = torch.rand(
batch_size, resolution[0], resolution[1], input_channels
)
lifting_net = torch.nn.Linear(input_channels, lifting_dim)
projecting_net = torch.nn.Linear(60, output_channels)
fno = FNO(lifting_net=lifting_net,
projecting_net=projecting_net,
n_modes=5,
dimensions=2,
inner_size=60,
n_layers=2)
fno = FNO(
lifting_net=lifting_net,
projecting_net=projecting_net,
n_modes=5,
dimensions=2,
inner_size=60,
n_layers=2,
)
out = fno(input_)
assert out.shape == torch.Size(
[batch_size, resolution[0], resolution[1], output_channels])
[batch_size, resolution[0], resolution[1], output_channels]
)
def test_2d_backward():
input_channels = 2
input_ = torch.rand(batch_size, resolution[0], resolution[1],
input_channels)
input_ = torch.rand(
batch_size, resolution[0], resolution[1], input_channels
)
lifting_net = torch.nn.Linear(input_channels, lifting_dim)
projecting_net = torch.nn.Linear(60, output_channels)
fno = FNO(lifting_net=lifting_net,
projecting_net=projecting_net,
n_modes=5,
dimensions=2,
inner_size=60,
n_layers=2)
fno = FNO(
lifting_net=lifting_net,
projecting_net=projecting_net,
n_modes=5,
dimensions=2,
inner_size=60,
n_layers=2,
)
input_.requires_grad = True
out = fno(input_)
l = torch.mean(out)
l.backward()
assert input_.grad.shape == torch.Size([
batch_size, resolution[0], resolution[1], input_channels
])
assert input_.grad.shape == torch.Size(
[batch_size, resolution[0], resolution[1], input_channels]
)
def test_3d_forward():
input_channels = 3
input_ = torch.rand(batch_size, resolution[0], resolution[1], resolution[2],
input_channels)
input_ = torch.rand(
batch_size, resolution[0], resolution[1], resolution[2], input_channels
)
lifting_net = torch.nn.Linear(input_channels, lifting_dim)
projecting_net = torch.nn.Linear(60, output_channels)
fno = FNO(lifting_net=lifting_net,
projecting_net=projecting_net,
n_modes=5,
dimensions=3,
inner_size=60,
n_layers=2)
fno = FNO(
lifting_net=lifting_net,
projecting_net=projecting_net,
n_modes=5,
dimensions=3,
inner_size=60,
n_layers=2,
)
out = fno(input_)
assert out.shape == torch.Size([
batch_size, resolution[0], resolution[1], resolution[2], output_channels
])
assert out.shape == torch.Size(
[
batch_size,
resolution[0],
resolution[1],
resolution[2],
output_channels,
]
)
def test_3d_backward():
input_channels = 3
input_ = torch.rand(batch_size, resolution[0], resolution[1], resolution[2],
input_channels)
input_ = torch.rand(
batch_size, resolution[0], resolution[1], resolution[2], input_channels
)
lifting_net = torch.nn.Linear(input_channels, lifting_dim)
projecting_net = torch.nn.Linear(60, output_channels)
fno = FNO(lifting_net=lifting_net,
projecting_net=projecting_net,
n_modes=5,
dimensions=3,
inner_size=60,
n_layers=2)
fno = FNO(
lifting_net=lifting_net,
projecting_net=projecting_net,
n_modes=5,
dimensions=3,
inner_size=60,
n_layers=2,
)
input_.requires_grad = True
out = fno(input_)
l = torch.mean(out)
l.backward()
assert input_.grad.shape == torch.Size([
batch_size, resolution[0], resolution[1], resolution[2], input_channels
])
assert input_.grad.shape == torch.Size(
[
batch_size,
resolution[0],
resolution[1],
resolution[2],
input_channels,
]
)