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replace torch.sparse.SparseTensor to torch.sparse_coo_tensor #1909

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replace torch.sparse.SparseTensor to torch.sparse_coo_tensor #1909

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10 changes: 5 additions & 5 deletions pytorch3d/ops/laplacian_matrices.py
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Original file line number Diff line number Diff line change
Expand Up @@ -48,7 +48,7 @@ def laplacian(verts: torch.Tensor, edges: torch.Tensor) -> torch.Tensor:
# 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))

# 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()
Expand All @@ -63,14 +63,14 @@ def laplacian(verts: torch.Tensor, edges: torch.Tensor) -> torch.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))

# 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))

return L

Expand Down Expand Up @@ -127,7 +127,7 @@ def cot_laplacian(
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))

# Make it symmetric; this means we are also setting
# L[v2, v1] = cota
Expand Down Expand Up @@ -176,7 +176,7 @@ def norm_laplacian(

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))
L = L + L.t()

return L