remove warnings filter

torch_sparse/storage.py

@@ -7,9 +7,6 @@ from torch_sparse.utils import Final
 
 layouts: Final[List[str]] = ['coo', 'csr', 'csc']
 
-# FIXME: Remove once `/` on `LongTensors` is officially removed from PyTorch.
-warnings.filterwarnings("ignore", category=UserWarning)
-
 
 def get_layout(layout: Optional[str] = None) -> str:
     if layout is None:
@@ -130,7 +127,9 @@ class SparseStorage(object):
 
         if not is_sorted:
             idx = self._col.new_zeros(self._col.numel() + 1)
-            idx[1:] = self._sparse_sizes[1] * self.row() + self._col
+            idx[1:] = self.row()
+            idx[1:] *= self._sparse_sizes[1]
+            idx[1:] += self._col
             if (idx[1:] < idx[:-1]).any():
                 perm = idx[1:].argsort()
                 self._row = self.row()[perm]
@@ -238,7 +237,7 @@ class SparseStorage(object):
                 rowptr = torch.cat([rowptr, rowptr.new_full((diff_0, ), nnz)])
             if rowcount is not None:
                 rowcount = torch.cat([rowcount, rowcount.new_zeros(diff_0)])
-        else:
+        elif diff_0 < 0:
             if rowptr is not None:
                 rowptr = rowptr[:-diff_0]
             if rowcount is not None:
@@ -251,7 +250,7 @@ class SparseStorage(object):
                 colptr = torch.cat([colptr, colptr.new_full((diff_1, ), nnz)])
             if colcount is not None:
                 colcount = torch.cat([colcount, colcount.new_zeros(diff_1)])
-        else:
+        elif diff_1 < 0:
             if colptr is not None:
                 colptr = colptr[:-diff_1]
             if colcount is not None:
@@ -280,7 +279,7 @@ class SparseStorage(object):
 
         idx = self.sparse_size(1) * self.row() + self.col()
 
-        row = idx / num_cols
+        row = idx // num_cols
         col = idx % num_cols
         assert row.dtype == torch.long and col.dtype == torch.long
 

torch_sparse/tensor.py

@@ -397,8 +397,8 @@ class SparseTensor(object):
 
         return mat
 
-    def to_torch_sparse_coo_tensor(
-            self, dtype: Optional[int] = None) -> torch.Tensor:
+    def to_torch_sparse_coo_tensor(self, dtype: Optional[int] = None
+                                   ) -> torch.Tensor:
         row, col, value = self.coo()
         index = torch.stack([row, col], dim=0)
 
@@ -503,8 +503,8 @@ SparseTensor.__repr__ = __repr__
 
 # Scipy Conversions ###########################################################
 
-ScipySparseMatrix = Union[scipy.sparse.coo_matrix, scipy.sparse.csr_matrix,
-                          scipy.sparse.csc_matrix]
+ScipySparseMatrix = Union[scipy.sparse.coo_matrix, scipy.sparse.
+                          csr_matrix, scipy.sparse.csc_matrix]
 
 
 @torch.jit.ignore