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Additional tests for operators #384

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Nov 25, 2024
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Additional tests for operators #384

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Additional tests for operators
gc031298 Nov 22, 2024
43bf90c
Additional tests for operators
gc031298 Nov 23, 2024
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85 changes: 66 additions & 19 deletions tests/test_operators.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,73 +5,120 @@
from pina.operators import grad, div, laplacian


def func_vec(x):
def func_vector(x):
return x**2


def func_scalar(x):
print('X')
x_ = x.extract(['x'])
y_ = x.extract(['y'])
mu_ = x.extract(['mu'])
return x_**2 + y_**2 + mu_**3
z_ = x.extract(['z'])
return x_**2 + y_**2 + z_**2


data = torch.rand((20, 3), requires_grad=True)
inp = LabelTensor(data, ['x', 'y', 'mu'])
labels = ['a', 'b', 'c']
tensor_v = LabelTensor(func_vec(inp), labels)
tensor_s = LabelTensor(func_scalar(inp).reshape(-1, 1), labels[0])

inp = LabelTensor(torch.rand((20, 3), requires_grad=True), ['x', 'y', 'z'])
tensor_v = LabelTensor(func_vector(inp), ['a', 'b', 'c'])
tensor_s = LabelTensor(func_scalar(inp).reshape(-1, 1), ['a'])

def test_grad_scalar_output():
grad_tensor_s = grad(tensor_s, inp)
true_val = 2*inp
assert grad_tensor_s.shape == inp.shape
assert grad_tensor_s.labels == [
f'd{tensor_s.labels[0]}d{i}' for i in inp.labels
]
assert torch.allclose(grad_tensor_s, true_val)

grad_tensor_s = grad(tensor_s, inp, d=['x', 'y'])
true_val = 2*inp.extract(['x', 'y'])
assert grad_tensor_s.shape == (inp.shape[0], 2)
assert grad_tensor_s.labels == [
f'd{tensor_s.labels[0]}d{i}' for i in ['x', 'y']
]
assert torch.allclose(grad_tensor_s, true_val)


def test_grad_vector_output():
grad_tensor_v = grad(tensor_v, inp)
true_val = torch.cat(
(2*inp.extract(['x']),
torch.zeros_like(inp.extract(['y'])),
torch.zeros_like(inp.extract(['z'])),
torch.zeros_like(inp.extract(['x'])),
2*inp.extract(['y']),
torch.zeros_like(inp.extract(['z'])),
torch.zeros_like(inp.extract(['x'])),
torch.zeros_like(inp.extract(['y'])),
2*inp.extract(['z'])
), dim=1
)
assert grad_tensor_v.shape == (20, 9)
grad_tensor_v = grad(tensor_v, inp, d=['x', 'mu'])
assert grad_tensor_v.labels == [
f'd{j}d{i}' for j in tensor_v.labels for i in inp.labels
]
assert torch.allclose(grad_tensor_v, true_val)

grad_tensor_v = grad(tensor_v, inp, d=['x', 'y'])
true_val = torch.cat(
(2*inp.extract(['x']),
torch.zeros_like(inp.extract(['y'])),
torch.zeros_like(inp.extract(['x'])),
2*inp.extract(['y']),
torch.zeros_like(inp.extract(['x'])),
torch.zeros_like(inp.extract(['y']))
), dim=1
)
assert grad_tensor_v.shape == (inp.shape[0], 6)
assert grad_tensor_v.labels == [
f'd{j}d{i}' for j in tensor_v.labels for i in ['x', 'y']
]
assert torch.allclose(grad_tensor_v, true_val)


def test_div_vector_output():
grad_tensor_v = div(tensor_v, inp)
assert grad_tensor_v.shape == (20, 1)
grad_tensor_v = div(tensor_v, inp, components=['a', 'b'], d=['x', 'mu'])
assert grad_tensor_v.shape == (inp.shape[0], 1)
div_tensor_v = div(tensor_v, inp)
true_val = 2*torch.sum(inp, dim=1).reshape(-1,1)
assert div_tensor_v.shape == (20, 1)
assert div_tensor_v.labels == [f'dadx+dbdy+dcdz']
assert torch.allclose(div_tensor_v, true_val)

div_tensor_v = div(tensor_v, inp, components=['a', 'b'], d=['x', 'y'])
true_val = 2*torch.sum(inp.extract(['x', 'y']), dim=1).reshape(-1,1)
assert div_tensor_v.shape == (inp.shape[0], 1)
assert div_tensor_v.labels == [f'dadx+dbdy']
assert torch.allclose(div_tensor_v, true_val)


def test_laplacian_scalar_output():
laplace_tensor_s = laplacian(tensor_s, inp, components=['a'], d=['x', 'y'])
laplace_tensor_s = laplacian(tensor_s, inp)
true_val = 6*torch.ones_like(laplace_tensor_s)
assert laplace_tensor_s.shape == tensor_s.shape
assert laplace_tensor_s.labels == [f"dd{tensor_s.labels[0]}"]
assert torch.allclose(laplace_tensor_s, true_val)

laplace_tensor_s = laplacian(tensor_s, inp, components=['a'], d=['x', 'y'])
true_val = 4*torch.ones_like(laplace_tensor_s)
assert all((laplace_tensor_s - true_val == 0).flatten())
assert laplace_tensor_s.shape == tensor_s.shape
assert laplace_tensor_s.labels == [f"dd{tensor_s.labels[0]}"]
assert torch.allclose(laplace_tensor_s, true_val)


def test_laplacian_vector_output():
laplace_tensor_v = laplacian(tensor_v, inp)
true_val = 2*torch.ones_like(tensor_v)
assert laplace_tensor_v.shape == tensor_v.shape
assert laplace_tensor_v.labels == [
f'dd{i}' for i in tensor_v.labels
]
assert torch.allclose(laplace_tensor_v, true_val)

laplace_tensor_v = laplacian(tensor_v,
inp,
components=['a', 'b'],
d=['x', 'y'])
true_val = 2*torch.ones_like(tensor_v.extract(['a', 'b']))
assert laplace_tensor_v.shape == tensor_v.extract(['a', 'b']).shape
assert laplace_tensor_v.labels == [
f'dd{i}' for i in ['a', 'b']
]
true_val = 2*torch.ones_like(tensor_v.extract(['a', 'b']))
assert all((laplace_tensor_v - true_val == 0).flatten())
assert torch.allclose(laplace_tensor_v, true_val)
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