radius cpu version

test/test_radius.py

@@ -4,12 +4,9 @@ import pytest
 import torch
 from torch_cluster import radius
 
-from .utils import tensor, grad_dtypes
+from .utils import grad_dtypes, devices, tensor
 
-devices = [torch.device('cuda')]
 
-
-@pytest.mark.skipif(not torch.cuda.is_available(), reason='CUDA not available')
 @pytest.mark.parametrize('dtype,device', product(grad_dtypes, devices))
 def test_radius(dtype, device):
     x = tensor([

torch_cluster/knn.py

@@ -46,8 +46,7 @@ def knn(x, y, k, batch_x=None, batch_y=None):
     assert y.size(0) == batch_y.size(0)
 
     if x.is_cuda:
-        assign_index = knn_cuda.knn(x, y, k, batch_x, batch_y)
-        return assign_index
+        return knn_cuda.knn(x, y, k, batch_x, batch_y)
 
     # Rescale x and y.
     min_xy = min(x.min().item(), y.min().item())

torch_cluster/radius.py

 import torch
+import scipy.spatial
 
 if torch.cuda.is_available():
     import radius_cuda
@@ -40,17 +41,25 @@ def radius(x, y, r, batch_x=None, batch_y=None, max_num_neighbors=32):
     x = x.view(-1, 1) if x.dim() == 1 else x
     y = y.view(-1, 1) if y.dim() == 1 else y
 
-    assert x.is_cuda
     assert x.dim() == 2 and batch_x.dim() == 1
     assert y.dim() == 2 and batch_y.dim() == 1
     assert x.size(1) == y.size(1)
     assert x.size(0) == batch_x.size(0)
     assert y.size(0) == batch_y.size(0)
 
-    op = radius_cuda.radius if x.is_cuda else None
-    assign_index = op(x, y, r, batch_x, batch_y, max_num_neighbors)
+    if x.is_cuda:
+        return radius_cuda.radius(x, y, r, batch_x, batch_y, max_num_neighbors)
 
-    return assign_index
+    x = torch.cat([x, 2 * r * batch_x.view(-1, 1).to(x.dtype)], dim=-1)
+    y = torch.cat([y, 2 * r * batch_y.view(-1, 1).to(y.dtype)], dim=-1)
+
+    tree = scipy.spatial.cKDTree(x)
+    col = tree.query_ball_point(y, r)
+    col = [torch.tensor(c) for c in col]
+    row = [torch.full_like(c, i) for i, c in enumerate(col)]
+    row, col = torch.cat(row, dim=0), torch.cat(col, dim=0)
+
+    return torch.stack([row, col], dim=0)
 
 
 def radius_graph(x, r, batch=None, loop=False, max_num_neighbors=32):