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import torch
from torch_geometric.utils import get_mesh_laplacian
def test_get_mesh_laplacian_of_cube():
pos = torch.tensor([
[1.0, 1.0, 1.0],
[1.0, -1.0, 1.0],
[-1.0, -1.0, 1.0],
[-1.0, 1.0, 1.0],
[1.0, 1.0, -1.0],
[1.0, -1.0, -1.0],
[-1.0, -1.0, -1.0],
[-1.0, 1.0, -1.0],
])
face = torch.tensor([
[0, 1, 2],
[0, 3, 2],
[4, 5, 1],
[4, 0, 1],
[7, 6, 5],
[7, 4, 5],
[3, 2, 6],
[3, 7, 6],
[4, 0, 3],
[4, 7, 3],
[1, 5, 6],
[1, 2, 6],
])
edge_index, edge_weight = get_mesh_laplacian(pos, face.t(),
normalization='rw')
assert edge_index.tolist() == [
[
0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 3, 4, 4, 4, 4,
4, 5, 5, 5, 5, 6, 6, 6, 6, 6, 7, 7, 7, 7, 0, 1, 2, 3, 4, 5, 6, 7
],
[
1, 2, 3, 4, 0, 2, 4, 5, 6, 0, 1, 3, 6, 0, 2, 4, 6, 7, 0, 1, 3, 5,
7, 1, 4, 6, 7, 1, 2, 3, 5, 7, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 7
],
]
assert torch.allclose(
edge_weight,
torch.tensor([
0.375, 0.0, 0.375, 0.375, 0.3, 0.3, 0.0, 0.3, 0.0, 0.0, 0.375,
0.375, 0.375, 0.3, 0.3, 0.0, 0.0, 0.3, 0.3, 0.0, 0.0, 0.3, 0.3,
0.375, 0.375, 0.375, 0.0, 0.0, 0.3, 0.0, 0.3, 0.3, 0.375, 0.375,
0.0, 0.375, -1.125, -0.9, -1.125, -0.9, -0.9, -1.125, -0.9, -1.125
]))
def test_get_mesh_laplacian_of_irregular_triangular_prism():
pos = torch.tensor([
[0.0, 0.0, 0.0],
[4.0, 0.0, 0.0],
[0.0, 0.0, -3.0],
[1.0, 5.0, -1.0],
[3.0, 5.0, -1.0],
[2.0, 5.0, -2.0],
])
face = torch.tensor([
[0, 1, 2],
[3, 4, 5],
[0, 1, 4],
[0, 3, 4],
[1, 2, 5],
[1, 4, 5],
[2, 0, 3],
[2, 5, 3],
])
edge_index, edge_weight = get_mesh_laplacian(pos, face.t(),
normalization='rw')
assert edge_index.tolist() == [
[
0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 5, 5,
5, 5, 0, 1, 2, 3, 4, 5
],
[
1, 2, 3, 4, 0, 2, 4, 5, 0, 1, 3, 5, 0, 2, 4, 5, 0, 1, 3, 5, 1, 2,
3, 4, 0, 1, 2, 3, 4, 5
],
]
assert torch.allclose(
edge_weight,
torch.tensor([
0.09730332, 0.15039921, 0.05081503, 0.00000000, 0.08726977,
0.03521059, 0.05363689, 0.00723919, 0.14497279, 0.03784235,
0.01629947, 0.03438699, 0.08362866, 0.02782887, 0.24252312,
0.40727590, 0.00000000, 0.08728313, 0.21507657, 0.38582093,
0.01117009, 0.04936920, 0.34247482, 0.36583540, -0.29851755,
-0.18335645, -0.23350160, -0.76125660, -0.68818060, -0.76884955
]))
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