Add num_points to fps as an alternative to ratio

csrc/cluster.h

@@ -11,7 +11,7 @@ CLUSTER_INLINE_VARIABLE int64_t _cuda_version = cuda_version();
 } // namespace cluster
 
 CLUSTER_API torch::Tensor fps(torch::Tensor src, torch::Tensor ptr, double ratio,
-                  bool random_start);
+                  int64_t num_points, bool random_start);
 
 CLUSTER_API torch::Tensor graclus(torch::Tensor rowptr, torch::Tensor col,
                       torch::optional<torch::Tensor> optional_weight);

csrc/cpu/fps_cpu.cpp

@@ -9,21 +9,30 @@ inline torch::Tensor get_dist(torch::Tensor x, int64_t idx) {
 }
 
 torch::Tensor fps_cpu(torch::Tensor src, torch::Tensor ptr, torch::Tensor ratio,
-                      bool random_start) {
+                      torch::Tensor num_points, bool random_start) {
 
   CHECK_CPU(src);
   CHECK_CPU(ptr);
   CHECK_CPU(ratio);
+  CHECK_CPU(num_points);
   CHECK_INPUT(ptr.dim() == 1);
 
   src = src.view({src.size(0), -1}).contiguous();
   ptr = ptr.contiguous();
   auto batch_size = ptr.numel() - 1;
 
   auto deg = ptr.narrow(0, 1, batch_size) - ptr.narrow(0, 0, batch_size);
-  auto out_ptr = deg.toType(torch::kFloat) * ratio;
-  out_ptr = out_ptr.ceil().toType(torch::kLong).cumsum(0);
-
+  torch::Tensor out_ptr;
+  if (num_points.sum().item<int64_t>() == 0) {
+    out_ptr = deg.toType(torch::kFloat) * ratio;
+    out_ptr = out_ptr.ceil().toType(torch::kLong).cumsum(0);
+  } else {
+    TORCH_CHECK((deg.toType(torch::kLong) >= num_points.toType(torch::kLong)).all().item<int64_t>(),
+      "Passed tensor has fewer elements than requested number of returned points.")
+    out_ptr = deg.toType(torch::kLong)
+                  .minimum(num_points.toType(torch::kLong))
+                  .cumsum(0);
+  }
   auto out = torch::empty({out_ptr[-1].data_ptr<int64_t>()[0]}, ptr.options());
 
   auto ptr_data = ptr.data_ptr<int64_t>();

csrc/cpu/fps_cpu.h

@@ -3,4 +3,4 @@
 #include "../extensions.h"
 
 torch::Tensor fps_cpu(torch::Tensor src, torch::Tensor ptr, torch::Tensor ratio,
-                      bool random_start);
+                      torch::Tensor num_points, bool random_start);

csrc/cuda/fps_cuda.cu

@@ -65,11 +65,13 @@ __global__ void fps_kernel(const scalar_t *src, const int64_t *ptr,
 }
 
 torch::Tensor fps_cuda(torch::Tensor src, torch::Tensor ptr,
-                       torch::Tensor ratio, bool random_start) {
+                       torch::Tensor ratio, torch::Tensor num_points,
+                       bool random_start) {
 
   CHECK_CUDA(src);
   CHECK_CUDA(ptr);
   CHECK_CUDA(ratio);
+  CHECK_CUDA(num_points);
   CHECK_INPUT(ptr.dim() == 1);
   c10::cuda::MaybeSetDevice(src.get_device());
 
@@ -78,8 +80,18 @@ torch::Tensor fps_cuda(torch::Tensor src, torch::Tensor ptr,
   auto batch_size = ptr.numel() - 1;
 
   auto deg = ptr.narrow(0, 1, batch_size) - ptr.narrow(0, 0, batch_size);
-  auto out_ptr = deg.toType(ratio.scalar_type()) * ratio;
-  out_ptr = out_ptr.ceil().toType(torch::kLong).cumsum(0);
+  torch::Tensor out_ptr;
+  if (num_points.sum().item<int64_t>() == 0) {
+    out_ptr = deg.toType(ratio.scalar_type()) * ratio;
+    out_ptr = out_ptr.ceil().toType(torch::kLong).cumsum(0);
+  } else {
+    TORCH_CHECK((deg.toType(torch::kLong) >= num_points.toType(torch::kLong)).all().item<int64_t>(),
+      "Passed tensor has fewer elements than requested number of returned points.")
+    out_ptr = deg.toType(torch::kLong)
+                  .minimum(num_points.toType(torch::kLong))
+                  .cumsum(0);
+  }
+
   out_ptr = torch::cat({torch::zeros({1}, ptr.options()), out_ptr}, 0);
 
   torch::Tensor start;

csrc/cuda/fps_cuda.h

@@ -3,4 +3,5 @@
 #include "../extensions.h"
 
 torch::Tensor fps_cuda(torch::Tensor src, torch::Tensor ptr,
-                       torch::Tensor ratio, bool random_start);
+                       torch::Tensor ratio, torch::Tensor num_points,
+                       bool random_start);

csrc/fps.cpp

@@ -19,16 +19,17 @@ PyMODINIT_FUNC PyInit__fps_cpu(void) { return NULL; }
 #endif
 #endif
 
-CLUSTER_API torch::Tensor fps(torch::Tensor src, torch::Tensor ptr, torch::Tensor ratio,
-                  bool random_start) {
+CLUSTER_API torch::Tensor fps(torch::Tensor src, torch::Tensor ptr,
+                              torch::Tensor ratio, torch::Tensor num_points,
+                              bool random_start) {
   if (src.device().is_cuda()) {
 #ifdef WITH_CUDA
-    return fps_cuda(src, ptr, ratio, random_start);
+    return fps_cuda(src, ptr, ratio, num_points, random_start);
 #else
     AT_ERROR("Not compiled with CUDA support");
 #endif
   } else {
-    return fps_cpu(src, ptr, ratio, random_start);
+    return fps_cpu(src, ptr, ratio, num_points, random_start);
   }
 }
 

test/test_fps.py

@@ -9,7 +9,7 @@
 
 @torch.jit.script
 def fps2(x: Tensor, ratio: Tensor) -> Tensor:
-    return fps(x, None, ratio, False)
+    return fps(x, None, ratio, None, False)
 
 
 @pytest.mark.parametrize('dtype,device', product(grad_dtypes, devices))
@@ -33,26 +33,36 @@ 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, num_points=2, random_start=False)
+    assert out.tolist() == [0, 2, 4, 6]
+
+    out = fps(x, batch, num_points=4, random_start=False)
+    assert out.tolist() == [0, 2, 1, 3, 4, 6, 5, 7]
 
     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, 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]
 
+    num = torch.tensor([2, 2], device=device)
+    out = fps(x, batch, num_points=num, random_start=False)
+    assert out.tolist() == [0, 2, 4, 6]
+
     out = fps(x, random_start=False)
     assert out.sort()[0].tolist() == [0, 5, 6, 7]
 
     out = fps(x, ratio=0.5, random_start=False)
     assert out.sort()[0].tolist() == [0, 5, 6, 7]
+    out = fps(x, num_points=4, random_start=False)
+    assert out.sort()[0].tolist() == [0, 5, 6, 7]
 
     out = fps(x, ratio=torch.tensor(0.5, device=device), random_start=False)
     assert out.sort()[0].tolist() == [0, 5, 6, 7]
@@ -63,6 +73,17 @@ def test_fps(dtype, device):
     out = fps2(x, torch.tensor([0.5], device=device))
     assert out.sort()[0].tolist() == [0, 5, 6, 7]
 
+    # requesting too many points
+    with pytest.raises(RuntimeError):
+        out = fps(x, batch, num_points=100, random_start=False)
+
+    with pytest.raises(RuntimeError):
+        out = fps(x, batch, num_points=5, random_start=False)
+
+    # invalid argument combination
+    with pytest.raises(ValueError):
+        out = fps(x, batch, ratio=0.0, num_points=0, random_start=False)
+
 
 @pytest.mark.parametrize('device', devices)
 def test_random_fps(device):

torch_cluster/fps.py

@@ -7,33 +7,57 @@
 
 
 @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
+def fps(src, batch, ratio, num_points, random_start, batch_size, ptr):  # noqa
+    # type: (Tensor, Optional[Tensor], Optional[float], Optional[int], 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
+def fps(src, batch, ratio, num_points, random_start, batch_size, ptr):  # noqa
+    # type: (Tensor, Optional[Tensor], Optional[Tensor], Optional[int], 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[float], bool, Optional[int], Optional[List[int]]) -> Tensor  # noqa
+def fps(src, batch, ratio, num_points, random_start, batch_size, ptr):  # noqa
+    # type: (Tensor, Optional[Tensor], Optional[float], Optional[int], 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, ptr):  # noqa
-    # type: (Tensor, Optional[Tensor], Optional[Tensor], bool, Optional[int], Optional[List[int]]) -> Tensor  # noqa
+def fps(src, batch, ratio, num_points, random_start, batch_size, ptr):  # noqa
+    # type: (Tensor, Optional[Tensor], Optional[Tensor], Optional[int], bool, Optional[int], Optional[List[int]]) -> Tensor  # noqa
+    pass  # pragma: no cover
+
+@torch.jit._overload  # noqa
+def fps(src, batch, ratio, num_points, random_start, batch_size, ptr):  # noqa
+    # type: (Tensor, Optional[Tensor], Optional[float], Optional[Tensor], bool, Optional[int], Optional[Tensor]) -> Tensor  # noqa
+    pass  # pragma: no cover
+
+
+@torch.jit._overload  # noqa
+def fps(src, batch, ratio, num_points, random_start, batch_size, ptr):  # noqa
+    # type: (Tensor, Optional[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, num_points, random_start, batch_size, ptr):  # noqa
+    # type: (Tensor, Optional[Tensor], Optional[float], Optional[Tensor], bool, Optional[int], Optional[List[int]]) -> Tensor  # noqa
+    pass  # pragma: no cover
+
+
+@torch.jit._overload  # noqa
+def fps(src, batch, ratio, num_points, random_start, batch_size, ptr):  # noqa
+    # type: (Tensor, Optional[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[Tensor, float]] = None,
+    num_points: Optional[Union[Tensor, int]] = None,
     random_start: bool = True,
     batch_size: Optional[int] = None,
     ptr: Optional[Union[Tensor, List[int]]] = None,
@@ -50,7 +74,11 @@ def fps(  # noqa
             :math:`\mathbf{b} \in {\{ 0, \ldots, B-1\}}^N`, which assigns each
             node to a specific example. (default: :obj:`None`)
         ratio (float or Tensor, optional): Sampling ratio.
+            Only ratio or num_points can be specified.
             (default: :obj:`0.5`)
+        num_points (int, optional): Number of returned points.
+            Only ratio or num_points can be specified.
+            (default: :obj:`None`)
         random_start (bool, optional): If set to :obj:`False`, use the first
             node in :math:`\mathbf{X}` as starting node. (default: obj:`True`)
         batch_size (int, optional): The number of examples :math:`B`.
@@ -71,25 +99,45 @@ def fps(  # noqa
         batch = torch.tensor([0, 0, 0, 0])
         index = fps(src, batch, ratio=0.5)
     """
+    # check if only of of ratio or num_points is set
+    # if no one is set, fallback to ratio = 0.5
+    if ratio is not None and num_points is not None:
+        raise ValueError("Only one of ratio and num_points can be specified.")
+
     r: Optional[Tensor] = None
     if ratio is None:
-        r = torch.tensor(0.5, dtype=src.dtype, device=src.device)
+        if num_points is None:
+            r = torch.tensor(0.5, dtype=src.dtype, device=src.device)
+        else:
+            r = torch.tensor(0.0, dtype=src.dtype, device=src.device)
     elif isinstance(ratio, float):
         r = torch.tensor(ratio, dtype=src.dtype, device=src.device)
     else:
         r = ratio
-    assert r is not None
+
+    num: Optional[Tensor] = None
+    if num_points is None:
+        num = torch.tensor(0, dtype=torch.long, device=src.device)
+    elif isinstance(num_points, int):
+        num = torch.tensor(num_points, dtype=torch.long, device=src.device)
+    else:
+        num = num_points
+
+    assert r is not None and num is not None
+
+    if r.sum() == 0 and num.sum() == 0:
+        raise ValueError("At least one of ratio or num_points should be > 0")
 
     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)
+            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)
+                src, torch.tensor(ptr, device=src.device), r, num, random_start
+            )
         else:
-            return torch.ops.torch_cluster.fps(src, ptr, r, random_start)
+            return torch.ops.torch_cluster.fps(src, ptr, r, num, random_start)
 
     if batch is not None:
         assert src.size(0) == batch.numel()
@@ -104,4 +152,4 @@ def fps(  # noqa
     else:
         ptr_vec = torch.tensor([0, src.size(0)], device=src.device)
 
-    return torch.ops.torch_cluster.fps(src, ptr_vec, r, random_start)
+    return torch.ops.torch_cluster.fps(src, ptr_vec, r, num, random_start)