rename

test/test_grid.py

@@ -14,7 +14,7 @@ def test_grid_cluster_cpu(tensor):
     assert output.tolist() == expected.tolist()
 
     expected = torch.LongTensor([0, 1])
-    output, _ = grid_cluster(position, size, offset=0)
+    output, _ = grid_cluster(position, size, origin=0)
     assert output.tolist() == expected.tolist()
 
     position = Tensor(tensor, [0, 17, 2, 8, 3])
@@ -63,7 +63,7 @@ def test_grid_cluster_gpu(tensor):  # pragma: no cover
     assert output.cpu().tolist() == expected.tolist()
 
     expected = torch.LongTensor([0, 1])
-    output, _ = grid_cluster(position, size, offset=0)
+    output, _ = grid_cluster(position, size, origin=0)
     assert output.cpu().tolist() == expected.tolist()
 
     position = Tensor(tensor, [0, 17, 2, 8, 3]).cuda()

torch_cluster/functions/grid.py

@@ -3,7 +3,7 @@ import torch
 from .utils import get_func, consecutive
 
 
-def grid_cluster(position, size, batch=None, offset=None, fake_nodes=False):
+def grid_cluster(position, size, batch=None, origin=None, fake_nodes=False):
     # Allow one-dimensional positions.
     if position.dim() == 1:
         position = position.unsqueeze(-1)
@@ -21,14 +21,14 @@ def grid_cluster(position, size, batch=None, offset=None, fake_nodes=False):
         position = torch.cat([batch, position], dim=-1)
         size = torch.cat([size.new(1).fill_(1), size], dim=-1)
 
-    # Translate to minimal positive positions if no offset is passed.
-    if offset is None:
+    # Translate to minimal positive positions if no origin was passed.
+    if origin is None:
         min = position.min(dim=-2, keepdim=True)[0]
         position = position - min
     else:
-        position = position + offset
+        position = position + origin
         assert position.min() >= 0, (
-            'Passed offset resulting in unallowed negative positions')
+            'Passed origin resulting in unallowed negative positions')
 
     # Compute cluster count for each dimension.
     max = position.max(dim=0)[0]