sample adj

torch_sparse/__init__.py

@@ -7,7 +7,7 @@ __version__ = '0.6.3'
 
 for library in [
         '_version', '_convert', '_diag', '_spmm', '_spspmm', '_metis', '_rw',
-        '_saint', '_padding'
+        '_saint', '_padding', '_sample'
 ]:
     torch.ops.load_library(importlib.machinery.PathFinder().find_spec(
         library, [osp.dirname(__file__)]).origin)
@@ -50,7 +50,7 @@ from .metis import partition  # noqa
 from .bandwidth import reverse_cuthill_mckee  # noqa
 from .saint import saint_subgraph  # noqa
 from .padding import padded_index, padded_index_select  # noqa
-from .sample import sample  # noqa
+from .sample import sample, sample_adj  # noqa
 
 from .convert import to_torch_sparse, from_torch_sparse  # noqa
 from .convert import to_scipy, from_scipy  # noqa

torch_sparse/sample.py

-from typing import Optional
+from typing import Optional, Tuple
 
 import torch
 from torch_sparse.tensor import SparseTensor
@@ -22,4 +22,21 @@ def sample(src: SparseTensor, num_neighbors: int,
     return col[rand]
 
 
+def sample_adj(src: SparseTensor, subset: torch.Tensor, num_neighbors: int,
+               replace: bool = False) -> Tuple[SparseTensor, torch.Tensor]:
+
+    rowptr, col, _ = src.csr()
+    rowcount = src.storage.rowcount()
+
+    rowptr, col, n_id, e_id = torch.ops.torch_sparse.sample_adj(
+        rowptr, col, rowcount, subset, num_neighbors, replace)
+
+    out = SparseTensor(rowptr=rowptr, row=None, col=col, value=e_id,
+                       sparse_sizes=(subset.size(0), n_id.size(0)),
+                       is_sorted=True)
+
+    return out, n_id
+
+
 SparseTensor.sample = sample
+SparseTensor.sample_adj = sample_adj