typos

torch_sparse/reduce.py

@@ -4,12 +4,13 @@ from torch_scatter import segment_csr
 
 
 def __reduce__(src, dim=None, reduce='add', deterministic=False):
+    assert reduce in ['add', 'mean', 'min', 'max']
+
     if dim is None and src.has_value():
         func = getattr(torch, 'sum' if reduce == 'add' else reduce)
         return func(src.storage.value)
 
     if dim is None and not src.has_value():
-        assert reduce in ['add', 'mean', 'min', 'max']
         value = src.nnz() if reduce == 'add' else 1
         return torch.tensor(value, device=src.device)
 
@@ -26,7 +27,6 @@ def __reduce__(src, dim=None, reduce='add', deterministic=False):
         return func(value, dim=(0, ) + dense_dims)
 
     if len(sparse_dims) == 2 and not src.has_value():
-        assert reduce in ['add', 'mean', 'min', 'max']
         value = src.nnz() if reduce == 'add' else 1
         return torch.tensor(value, device=src.device)
 
@@ -50,15 +50,15 @@ def __reduce__(src, dim=None, reduce='add', deterministic=False):
         return out
 
     if sparse_dims[0] == 1 and not src.has_value():
-        assert reduce in ['add', 'mean', 'min', 'max']
         if reduce == 'add':
             return src.storage.rowcount.to(torch.get_default_dtype())
         elif reduce == 'min' or 'max':
+            # Return an additional `None` arg(min|max) tensor for consistency.
             return torch.ones(src.size(0), device=src.device), None
         else:
             return torch.ones(src.size(0), device=src.device)
 
-    deterministic = src.storage._csr2csc is not None or deterministic
+    deterministic = src.storage.has_csr2csc() or deterministic
 
     if sparse_dims[0] == 0 and deterministic and src.has_value():
         csr2csc = src.storage.csr2csc
@@ -73,10 +73,10 @@ def __reduce__(src, dim=None, reduce='add', deterministic=False):
         return out
 
     if sparse_dims[0] == 0 and not src.has_value():
-        assert reduce in ['add', 'mean', 'min', 'max']
         if reduce == 'add':
             return src.storage.colcount.to(torch.get_default_dtype())
         elif reduce == 'min' or 'max':
+            # Return an additional `None` arg(min|max) tensor for consistency.
             return torch.ones(src.size(1), device=src.device), None
         else:
             return torch.ones(src.size(1), device=src.device)

torch_sparse/storage.py

@@ -212,7 +212,7 @@ class SparseStorage(object):
 
     @cached_property
     def colcount(self):
-        if self._colptr is not None:
+        if self.has_colptr():
             colptr = self.colptr
             return colptr[1:] - colptr[:-1]
         else: