Extend FPS with an extra ptr argument (#180)

* Extend FPS with an extra ptr argument

* update

* update

* update

---------
Co-authored-by: rusty1s <matthias.fey@tu-dortmund.de>

.github/workflows/testing.yml

@@ -31,6 +31,7 @@ jobs:
 
       - name: Install main package
         run: |
+          pip install scipy==1.10.1  # Python 3.8 support
           python setup.py develop
 
       - name: Run test-suite

test/test_fps.py

@@ -25,6 +25,8 @@ def test_fps(dtype, device):
         [+2, -2],
     ], dtype, device)
     batch = tensor([0, 0, 0, 0, 1, 1, 1, 1], torch.long, device)
+    ptr_list = [0, 4, 8]
+    ptr = torch.tensor(ptr_list, device=device)
 
     out = fps(x, batch, random_start=False)
     assert out.tolist() == [0, 2, 4, 6]
@@ -32,12 +34,18 @@ def test_fps(dtype, device):
     out = fps(x, batch, ratio=0.5, random_start=False)
     assert out.tolist() == [0, 2, 4, 6]
 
-    out = fps(x, batch, ratio=torch.tensor(0.5, device=device),
-              random_start=False)
+    ratio = torch.tensor(0.5, device=device)
+    out = fps(x, batch, ratio=ratio, random_start=False)
     assert out.tolist() == [0, 2, 4, 6]
 
-    out = fps(x, batch, ratio=torch.tensor([0.5, 0.5], device=device),
-              random_start=False)
+    out = fps(x, ptr=ptr_list, ratio=0.5, random_start=False)
+    assert out.tolist() == [0, 2, 4, 6]
+
+    out = fps(x, ptr=ptr, ratio=0.5, random_start=False)
+    assert out.tolist() == [0, 2, 4, 6]
+
+    ratio = torch.tensor([0.5, 0.5], device=device)
+    out = fps(x, batch, ratio=ratio, random_start=False)
     assert out.tolist() == [0, 2, 4, 6]
 
     out = fps(x, random_start=False)

torch_cluster/fps.py

-from typing import Optional, Union
+from typing import List, Optional, Union
 
 import torch
 from torch import Tensor
 
+import torch_cluster.typing
+
+
+@torch.jit._overload  # noqa
+def fps(src, batch, ratio, random_start, batch_size, ptr):  # noqa
+    # type: (Tensor, Optional[Tensor], Optional[float], bool, Optional[int], Optional[Tensor]) -> Tensor  # noqa
+    pass  # pragma: no cover
+
+
+@torch.jit._overload  # noqa
+def fps(src, batch, ratio, random_start, batch_size, ptr):  # noqa
+    # type: (Tensor, Optional[Tensor], Optional[Tensor], bool, Optional[int], Optional[Tensor]) -> Tensor  # noqa
+    pass  # pragma: no cover
+
 
 @torch.jit._overload  # noqa
-def fps(src, batch, ratio, random_start, batch_size):  # noqa
-    # type: (Tensor, Optional[Tensor], Optional[float], bool, Optional[int]) -> Tensor  # noqa
+def fps(src, batch, ratio, random_start, batch_size, ptr):  # noqa
+    # type: (Tensor, Optional[Tensor], Optional[float], bool, Optional[int], Optional[List[int]]) -> Tensor  # noqa
     pass  # pragma: no cover
 
 
 @torch.jit._overload  # noqa
-def fps(src, batch, ratio, random_start, batch_size):  # noqa
-    # type: (Tensor, Optional[Tensor], Optional[Tensor], bool, Optional[int]) -> Tensor  # noqa
+def fps(src, batch, ratio, random_start, batch_size, ptr):  # noqa
+    # type: (Tensor, Optional[Tensor], Optional[Tensor], bool, Optional[int], Optional[List[int]]) -> Tensor  # noqa
     pass  # pragma: no cover
 
 
 def fps(  # noqa
     src: torch.Tensor,
     batch: Optional[Tensor] = None,
-    ratio: Optional[Union[torch.Tensor, float]] = None,
+    ratio: Optional[Union[Tensor, float]] = None,
     random_start: bool = True,
     batch_size: Optional[int] = None,
+    ptr: Optional[Union[Tensor, List[int]]] = None,
 ):
     r""""A sampling algorithm from the `"PointNet++: Deep Hierarchical Feature
     Learning on Point Sets in a Metric Space"
@@ -40,6 +55,10 @@ def fps(  # noqa
             node in :math:`\mathbf{X}` as starting node. (default: obj:`True`)
         batch_size (int, optional): The number of examples :math:`B`.
             Automatically calculated if not given. (default: :obj:`None`)
+        ptr (torch.Tensor or [int], optional): If given, batch assignment will
+            be determined based on boundaries in CSR representation, *e.g.*,
+            :obj:`batch=[0,0,1,1,1,2]` translates to :obj:`ptr=[0,2,5,6]`.
+            (default: :obj:`None`)
 
     :rtype: :class:`LongTensor`
 
@@ -52,7 +71,6 @@ def fps(  # noqa
         batch = torch.tensor([0, 0, 0, 0])
         index = fps(src, batch, ratio=0.5)
     """
-
     r: Optional[Tensor] = None
     if ratio is None:
         r = torch.tensor(0.5, dtype=src.dtype, device=src.device)
@@ -62,6 +80,17 @@ def fps(  # noqa
         r = ratio
     assert r is not None
 
+    if ptr is not None:
+        if isinstance(ptr, list) and torch_cluster.typing.WITH_PTR_LIST:
+            return torch.ops.torch_cluster.fps_ptr_list(
+                src, ptr, r, random_start)
+
+        if isinstance(ptr, list):
+            return torch.ops.torch_cluster.fps(
+                src, torch.tensor(ptr, device=src.device), r, random_start)
+        else:
+            return torch.ops.torch_cluster.fps(src, ptr, r, random_start)
+
     if batch is not None:
         assert src.size(0) == batch.numel()
         if batch_size is None:
@@ -70,9 +99,9 @@ def fps(  # noqa
         deg = src.new_zeros(batch_size, dtype=torch.long)
         deg.scatter_add_(0, batch, torch.ones_like(batch))
 
-        ptr = deg.new_zeros(batch_size + 1)
-        torch.cumsum(deg, 0, out=ptr[1:])
+        ptr_vec = deg.new_zeros(batch_size + 1)
+        torch.cumsum(deg, 0, out=ptr_vec[1:])
     else:
-        ptr = torch.tensor([0, src.size(0)], device=src.device)
+        ptr_vec = torch.tensor([0, src.size(0)], device=src.device)
 
-    return torch.ops.torch_cluster.fps(src, ptr, r, random_start)
+    return torch.ops.torch_cluster.fps(src, ptr_vec, r, random_start)

torch_cluster/typing.py

+import torch
+
+WITH_PTR_LIST = hasattr(torch.ops.torch_cluster, 'fps_ptr_list')