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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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147
tests/test_model/test_deeponet.py
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@@ -7,42 +7,50 @@ from pina.model import DeepONet
from pina.model import FeedForward
data = torch.rand((20, 3))
input_vars = ['a', 'b', 'c']
input_vars = ["a", "b", "c"]
input_ = LabelTensor(data, input_vars)
symbol_funcs_red = DeepONet._symbol_functions(dim=-1)
output_dims = [1, 5, 10, 20]
def test_constructor():
branch_net = FeedForward(input_dimensions=1, output_dimensions=10)
trunk_net = FeedForward(input_dimensions=2, output_dimensions=10)
DeepONet(branch_net=branch_net,
trunk_net=trunk_net,
input_indeces_branch_net=['a'],
input_indeces_trunk_net=['b', 'c'],
reduction='+',
aggregator='*')
DeepONet(
branch_net=branch_net,
trunk_net=trunk_net,
input_indeces_branch_net=["a"],
input_indeces_trunk_net=["b", "c"],
reduction="+",
aggregator="*",
)
def test_constructor_fails_when_invalid_inner_layer_size():
branch_net = FeedForward(input_dimensions=1, output_dimensions=10)
trunk_net = FeedForward(input_dimensions=2, output_dimensions=8)
with pytest.raises(ValueError):
DeepONet(branch_net=branch_net,
trunk_net=trunk_net,
input_indeces_branch_net=['a'],
input_indeces_trunk_net=['b', 'c'],
reduction='+',
aggregator='*')
DeepONet(
branch_net=branch_net,
trunk_net=trunk_net,
input_indeces_branch_net=["a"],
input_indeces_trunk_net=["b", "c"],
reduction="+",
aggregator="*",
)
def test_forward_extract_str():
branch_net = FeedForward(input_dimensions=1, output_dimensions=10)
trunk_net = FeedForward(input_dimensions=2, output_dimensions=10)
model = DeepONet(branch_net=branch_net,
trunk_net=trunk_net,
input_indeces_branch_net=['a'],
input_indeces_trunk_net=['b', 'c'],
reduction='+',
aggregator='*')
model = DeepONet(
branch_net=branch_net,
trunk_net=trunk_net,
input_indeces_branch_net=["a"],
input_indeces_trunk_net=["b", "c"],
reduction="+",
aggregator="*",
)
model(input_)
assert model(input_).shape[-1] == 1
@@ -50,82 +58,99 @@ def test_forward_extract_str():
def test_forward_extract_int():
branch_net = FeedForward(input_dimensions=1, output_dimensions=10)
trunk_net = FeedForward(input_dimensions=2, output_dimensions=10)
model = DeepONet(branch_net=branch_net,
trunk_net=trunk_net,
input_indeces_branch_net=[0],
input_indeces_trunk_net=[1, 2],
reduction='+',
aggregator='*')
model = DeepONet(
branch_net=branch_net,
trunk_net=trunk_net,
input_indeces_branch_net=[0],
input_indeces_trunk_net=[1, 2],
reduction="+",
aggregator="*",
)
model(data)
def test_backward_extract_int():
data = torch.rand((20, 3))
branch_net = FeedForward(input_dimensions=1, output_dimensions=10)
trunk_net = FeedForward(input_dimensions=2, output_dimensions=10)
model = DeepONet(branch_net=branch_net,
trunk_net=trunk_net,
input_indeces_branch_net=[0],
input_indeces_trunk_net=[1, 2],
reduction='+',
aggregator='*')
model = DeepONet(
branch_net=branch_net,
trunk_net=trunk_net,
input_indeces_branch_net=[0],
input_indeces_trunk_net=[1, 2],
reduction="+",
aggregator="*",
)
data.requires_grad = True
model(data)
l=torch.mean(model(data))
l = torch.mean(model(data))
l.backward()
assert data._grad.shape == torch.Size([20,3])
assert data._grad.shape == torch.Size([20, 3])
def test_forward_extract_str_wrong():
branch_net = FeedForward(input_dimensions=1, output_dimensions=10)
trunk_net = FeedForward(input_dimensions=2, output_dimensions=10)
model = DeepONet(branch_net=branch_net,
trunk_net=trunk_net,
input_indeces_branch_net=['a'],
input_indeces_trunk_net=['b', 'c'],
reduction='+',
aggregator='*')
model = DeepONet(
branch_net=branch_net,
trunk_net=trunk_net,
input_indeces_branch_net=["a"],
input_indeces_trunk_net=["b", "c"],
reduction="+",
aggregator="*",
)
with pytest.raises(RuntimeError):
model(data)
def test_backward_extract_str_wrong():
data = torch.rand((20, 3))
branch_net = FeedForward(input_dimensions=1, output_dimensions=10)
trunk_net = FeedForward(input_dimensions=2, output_dimensions=10)
model = DeepONet(branch_net=branch_net,
trunk_net=trunk_net,
input_indeces_branch_net=['a'],
input_indeces_trunk_net=['b', 'c'],
reduction='+',
aggregator='*')
model = DeepONet(
branch_net=branch_net,
trunk_net=trunk_net,
input_indeces_branch_net=["a"],
input_indeces_trunk_net=["b", "c"],
reduction="+",
aggregator="*",
)
data.requires_grad = True
with pytest.raises(RuntimeError):
model(data)
l=torch.mean(model(data))
l = torch.mean(model(data))
l.backward()
assert data._grad.shape == torch.Size([20,3])
assert data._grad.shape == torch.Size([20, 3])
@pytest.mark.parametrize('red', symbol_funcs_red)
@pytest.mark.parametrize("red", symbol_funcs_red)
def test_forward_symbol_funcs(red):
branch_net = FeedForward(input_dimensions=1, output_dimensions=10)
trunk_net = FeedForward(input_dimensions=2, output_dimensions=10)
model = DeepONet(branch_net=branch_net,
trunk_net=trunk_net,
input_indeces_branch_net=['a'],
input_indeces_trunk_net=['b', 'c'],
reduction=red,
aggregator='*')
model = DeepONet(
branch_net=branch_net,
trunk_net=trunk_net,
input_indeces_branch_net=["a"],
input_indeces_trunk_net=["b", "c"],
reduction=red,
aggregator="*",
)
model(input_)
assert model(input_).shape[-1] == 1
@pytest.mark.parametrize('out_dim', output_dims)
@pytest.mark.parametrize("out_dim", output_dims)
def test_forward_callable_reduction(out_dim):
branch_net = FeedForward(input_dimensions=1, output_dimensions=10)
trunk_net = FeedForward(input_dimensions=2, output_dimensions=10)
reduction_layer = Linear(10, out_dim)
model = DeepONet(branch_net=branch_net,
trunk_net=trunk_net,
input_indeces_branch_net=['a'],
input_indeces_trunk_net=['b', 'c'],
reduction=reduction_layer,
aggregator='*')
model = DeepONet(
branch_net=branch_net,
trunk_net=trunk_net,
input_indeces_branch_net=["a"],
input_indeces_trunk_net=["b", "c"],
reduction=reduction_layer,
aggregator="*",
)
model(input_)
assert model(input_).shape[-1] == out_dim