Skip to content

Fix device conversion #54

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
Pseudomanifold merged 2 commits into from
Dec 11, 2025
Merged

Fix device conversion #54

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
8a7b778
Fix device conversion
martin-carrasco Dec 11, 2025
c9ab746
Ruff'd and Black'd
martin-carrasco Dec 11, 2025
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
30 changes: 11 additions & 19 deletions mantra/representations/simplicial_connectivity.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -31,7 +31,8 @@ def forward(self, data: Data):
return data


class AbstractSimplicialComplexConnectivity(BaseTransform, ABCMeta):
class AbstractSimplicialComplexConnectivity(BaseTransform):
__metaclass__ = ABCMeta
"""Base class for connectivity transforms.

Parent class for implementing a transform that adds a
Expand Down Expand Up @@ -108,28 +109,19 @@ def forward(self, data: Data):
self.generate_matrix(
data.simplex_trie, rank_idx, max_rank
),
device=data.triangulation.device,
)
except ValueError:
idx_low_simp = rank_idx - 1 if rank_idx > 0 else rank_idx
if "incidence" in self.connectivity_name:
data[connectivity_name] = (
torch.zeros(
[shape[idx_low_simp], shape[rank_idx]],
layout=torch.sparse_coo,
)
.coalesce()
.to(data.triangulations.device)
)
data[connectivity_name] = torch.zeros(
[shape[idx_low_simp], shape[rank_idx]],
layout=torch.sparse_coo,
).coalesce()
elif "adjacency" in self.connectivity_name:
data[connectivity_name] = (
torch.zeros(
[shape[rank_idx], shape[rank_idx]],
layout=torch.sparse_coo,
)
.coalesce()
.to(data.triangulations.device)
)
data[connectivity_name] = torch.zeros(
[shape[rank_idx], shape[rank_idx]],
layout=torch.sparse_coo,
).coalesce()

return data

Expand Down Expand Up @@ -328,7 +320,7 @@ def _from_sparse(data: scipy.sparse.csc_matrix, device=None) -> torch.Tensor:
input data converted to tensor.
"""
if device is None:
device = data.device("cpu")
device = torch.device("cpu")
# cast from csc_matrix to coo format for compatibility
coo = data.tocoo()

Expand Down