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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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58
tests/test_label_tensor.py
View File

@@ -17,12 +17,14 @@ labels_row = {
"dof": range(20)
}
}
labels_list = ['x', 'y', 'z']
labels_all = labels_column | labels_row
@pytest.mark.parametrize("labels", [labels_column, labels_row, labels_all])
@pytest.mark.parametrize("labels", [labels_column, labels_row, labels_all, labels_list])
def test_constructor(labels):
LabelTensor(data, labels)
def test_wrong_constructor():
with pytest.raises(ValueError):
LabelTensor(data, ['a', 'b'])
@@ -61,7 +63,6 @@ def test_extract_2D(labels_te):
assert torch.all(torch.isclose(data[2,2].reshape(1, 1), new))
def test_extract_3D():
labels = labels_all
data = torch.rand(20, 3, 4)
labels = {
1: {
@@ -80,6 +81,7 @@ def test_extract_3D():
tensor = LabelTensor(data, labels)
new = tensor.extract(labels_te)
tensor2 = LabelTensor(data, labels)
assert new.ndim == tensor.ndim
assert new.shape[0] == 20
assert new.shape[1] == 2
@@ -88,6 +90,10 @@ def test_extract_3D():
data[:, 0::2, 1:4].reshape(20, 2, 3),
new
))
assert tensor2.ndim == tensor.ndim
assert tensor2.shape == tensor.shape
assert tensor.labels == tensor2.labels
assert new.shape != tensor.shape
def test_concatenation_3D():
data_1 = torch.rand(20, 3, 4)
@@ -146,3 +152,51 @@ def test_concatenation_3D():
assert lt_cat.labels[2]['dof'] == range(5)
assert lt_cat.labels[0]['dof'] == range(20)
assert lt_cat.labels[1]['dof'] == range(3)
def test_summation():
lt1 = LabelTensor(torch.ones(20,3), labels_all)
lt2 = LabelTensor(torch.ones(30,3), ['x', 'y', 'z'])
with pytest.raises(RuntimeError):
LabelTensor.summation([lt1, lt2])
lt1 = LabelTensor(torch.ones(20,3), labels_all)
lt2 = LabelTensor(torch.ones(20,3), labels_all)
lt_sum = LabelTensor.summation([lt1, lt2])
assert lt_sum.ndim == lt_sum.ndim
assert lt_sum.shape[0] == 20
assert lt_sum.shape[1] == 3
assert lt_sum.labels == labels_all
assert torch.eq(lt_sum.tensor, torch.ones(20,3)*2).all()
lt1 = LabelTensor(torch.ones(20,3), labels_all)
lt2 = LabelTensor(torch.ones(20,3), labels_all)
lt3 = LabelTensor(torch.zeros(20, 3), labels_all)
lt_sum = LabelTensor.summation([lt1, lt2, lt3])
assert lt_sum.ndim == lt_sum.ndim
assert lt_sum.shape[0] == 20
assert lt_sum.shape[1] == 3
assert lt_sum.labels == labels_all
assert torch.eq(lt_sum.tensor, torch.ones(20,3)*2).all()
def test_append_3D():
data_1 = torch.rand(20, 3, 4)
labels_1 = ['x', 'y', 'z', 'w']
lt1 = LabelTensor(data_1, labels_1)
data_2 = torch.rand(50, 3, 4)
labels_2 = ['x', 'y', 'z', 'w']
lt2 = LabelTensor(data_2, labels_2)
lt1 = lt1.append(lt2)
assert lt1.shape == (70, 3, 4)
assert lt1.labels[0]['dof'] == range(70)
assert lt1.labels[1]['dof'] == range(3)
assert lt1.labels[2]['dof'] == ['x', 'y', 'z', 'w']
data_1 = torch.rand(20, 3, 2)
labels_1 = ['x', 'y']
lt1 = LabelTensor(data_1, labels_1)
data_2 = torch.rand(20, 3, 2)
labels_2 = ['z', 'w']
lt2 = LabelTensor(data_2, labels_2)
lt1 = lt1.append(lt2, mode='cross')
assert lt1.shape == (20, 3, 4)
assert lt1.labels[0]['dof'] == range(20)
assert lt1.labels[1]['dof'] == range(3)
assert lt1.labels[2]['dof'] == ['x', 'y', 'z', 'w']

24
tests/test_operators.py
View File

@@ -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)