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Filippo0.2 (#361)

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* Add summation and remove deepcopy (only for tensors) in LabelTensor class
* Update operators for compatibility with updated LabelTensor implementation
* Implement labels.setter in LabelTensor class
* Update LabelTensor

---------

Co-authored-by: FilippoOlivo <filippo@filippoolivo.com>
This commit is contained in:
Dario Coscia
2024-10-04 15:59:09 +02:00
committed by Nicola Demo
parent 1d3df2a127
commit fdb8f65143
4 changed files with 212 additions and 92 deletions
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24
tests/test_operators.py
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@@ -16,28 +16,29 @@ def func_scalar(x):
return x_**2 + y_**2 + z_**2
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'])
data = torch.rand((20, 3))
inp = LabelTensor(data, ['x', 'y', 'mu']).requires_grad_(True)
labels = ['a', 'b', 'c']
tensor_v = LabelTensor(func_vec(inp), labels)
tensor_s = LabelTensor(func_scalar(inp).reshape(-1, 1), labels[0])
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 grad_tensor_s.labels[grad_tensor_s.ndim-1]['dof'] == [
f'd{tensor_s.labels[tensor_s.ndim-1]["dof"][0]}d{i}' for i in inp.labels[inp.ndim-1]['dof']
]
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 grad_tensor_s.shape == (20, 2)
assert grad_tensor_s.labels[grad_tensor_s.ndim-1]['dof'] == [
f'd{tensor_s.labels[tensor_s.ndim-1]["dof"][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(
@@ -74,7 +75,6 @@ def test_grad_vector_output():
]
assert torch.allclose(grad_tensor_v, true_val)
def test_div_vector_output():
div_tensor_v = div(tensor_v, inp)
true_val = 2*torch.sum(inp, dim=1).reshape(-1,1)
@@ -88,7 +88,6 @@ def test_div_vector_output():
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)
true_val = 6*torch.ones_like(laplace_tensor_s)
@@ -102,7 +101,6 @@ def test_laplacian_scalar_output():
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)