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Use sparse_coo_tensor in laplacian_matrices.py (#1991)

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Summary:
update obsolete torch.sparse.FloatTensor to torch.sparse_coo_tensor

Pull Request resolved: https://github.com/facebookresearch/pytorch3d/pull/1991

Reviewed By: MichaelRamamonjisoa

Differential Revision: D80084359

Pulled By: bottler

fbshipit-source-id: dc6c7a90211113d1ce5338a92c8c0030bfe12e65
This commit is contained in:
Antoine Dumoulin 2025-08-13 07:55:57 -07:00 committed by Facebook GitHub Bot
parent 5043d15361
commit 50f8efa1cb
1 changed files with 6 additions and 11 deletions
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17
pytorch3d/ops/laplacian_matrices.py
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@ -47,8 +47,7 @@ def laplacian(verts: torch.Tensor, edges: torch.Tensor) -> torch.Tensor:
# i.e. A[i, j] = 1 if (i,j) is an edge, or
# A[e0, e1] = 1 & A[e1, e0] = 1
ones = torch.ones(idx.shape[1], dtype=torch.float32, device=verts.device)
# pyre-fixme[16]: Module `sparse` has no attribute `FloatTensor`.
A = torch.sparse.FloatTensor(idx, ones, (V, V))
A = torch.sparse_coo_tensor(idx, ones, (V, V), dtype=torch.float32)
# the sum of i-th row of A gives the degree of the i-th vertex
deg = torch.sparse.sum(A, dim=1).to_dense()
@ -62,15 +61,13 @@ def laplacian(verts: torch.Tensor, edges: torch.Tensor) -> torch.Tensor:
# pyre-fixme[58]: `/` is not supported for operand types `float` and `Tensor`.
deg1 = torch.where(deg1 > 0.0, 1.0 / deg1, deg1)
val = torch.cat([deg0, deg1])
# pyre-fixme[16]: Module `sparse` has no attribute `FloatTensor`.
L = torch.sparse.FloatTensor(idx, val, (V, V))
L = torch.sparse_coo_tensor(idx, val, (V, V), dtype=torch.float32)
# Then we add the diagonal values L[i, i] = -1.
idx = torch.arange(V, device=verts.device)
idx = torch.stack([idx, idx], dim=0)
ones = torch.ones(idx.shape[1], dtype=torch.float32, device=verts.device)
# pyre-fixme[16]: Module `sparse` has no attribute `FloatTensor`.
L -= torch.sparse.FloatTensor(idx, ones, (V, V))
L -= torch.sparse_coo_tensor(idx, ones, (V, V), dtype=torch.float32)
return L
@ -126,8 +123,7 @@ def cot_laplacian(
ii = faces[:, [1, 2, 0]]
jj = faces[:, [2, 0, 1]]
idx = torch.stack([ii, jj], dim=0).view(2, F * 3)
# pyre-fixme[16]: Module `sparse` has no attribute `FloatTensor`.
L = torch.sparse.FloatTensor(idx, cot.view(-1), (V, V))
L = torch.sparse_coo_tensor(idx, cot.view(-1), (V, V), dtype=torch.float32)
# Make it symmetric; this means we are also setting
# L[v2, v1] = cota
@ -167,7 +163,7 @@ def norm_laplacian(
v0, v1 = edge_verts[:, 0], edge_verts[:, 1]
# Side lengths of each edge, of shape (E,)
w01 = 1.0 / ((v0 - v1).norm(dim=1) + eps)
w01 = torch.reciprocal((v0 - v1).norm(dim=1) + eps)
# Construct a sparse matrix by basically doing:
# L[v0, v1] = w01
@ -175,8 +171,7 @@ def norm_laplacian(
e01 = edges.t() # (2, E)
V = verts.shape[0]
# pyre-fixme[16]: Module `sparse` has no attribute `FloatTensor`.
L = torch.sparse.FloatTensor(e01, w01, (V, V))
L = torch.sparse_coo_tensor(e01, w01, (V, V), dtype=torch.float32)
L = L + L.t()
return L