knn cpu implementation

setup.py

@@ -24,7 +24,7 @@ if CUDA_HOME is not None:
 __version__ = '1.2.1'
 url = 'https://github.com/rusty1s/pytorch_cluster'
 
-install_requires = []
+install_requires = ['scipy']
 setup_requires = ['pytest-runner']
 tests_require = ['pytest', 'pytest-cov']
 
@@ -43,5 +43,4 @@ setup(
     tests_require=tests_require,
     ext_modules=ext_modules,
     cmdclass=cmdclass,
-    packages=find_packages(),
-)
+    packages=find_packages(), )

test/test_knn.py

@@ -4,13 +4,9 @@ import pytest
 import torch
 from torch_cluster import knn
 
-from .utils import tensor
+from .utils import grad_dtypes, devices, tensor
 
-devices = [torch.device('cuda')]
-grad_dtypes = [torch.float]
 
-
-@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([
@@ -32,4 +28,8 @@ def test_radius(dtype, device):
     batch_y = tensor([0, 1], torch.long, device)
 
     out = knn(x, y, 2, batch_x, batch_y)
-    assert out.tolist() == [[0, 0, 1, 1], [2, 3, 4, 5]]
+    assert out[0].tolist() == [0, 0, 1, 1]
+    col = out[1][:2].tolist()
+    assert col == [2, 3] or col == [3, 2]
+    col = out[1][2:].tolist()
+    assert col == [4, 5] or col == [5, 4]

torch_cluster/knn.py

 import torch
+import scipy.spatial
 
 if torch.cuda.is_available():
     import knn_cuda
@@ -38,17 +39,35 @@ def knn(x, y, k, batch_x=None, batch_y=None):
     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 = knn_cuda.knn if x.is_cuda else None
-    assign_index = op(x, y, k, batch_x, batch_y)
+    if x.is_cuda:
+        assign_index = knn_cuda.knn(x, y, k, batch_x, batch_y)
+        return assign_index
 
-    return assign_index
+    # Rescale x and y.
+    min_xy = min(x.min().item(), y.min().item())
+    x, y = x - min_xy, y - min_xy
+
+    max_xy = max(x.max().item(), y.max().item())
+    x, y, = x / max_xy, y / max_xy
+
+    # Concat batch/features to ensure no cross-links between examples exist.
+    x = torch.cat([x, 2 * x.size(1) * batch_x.view(-1, 1).to(x.dtype)], dim=-1)
+    y = torch.cat([y, 2 * y.size(1) * batch_y.view(-1, 1).to(y.dtype)], dim=-1)
+
+    tree = scipy.spatial.cKDTree(x)
+    dist, col = tree.query(y, k=k, distance_upper_bound=x.size(1))
+    dist, col = torch.tensor(dist), torch.tensor(col)
+    row = torch.arange(col.size(0)).view(-1, 1).repeat(1, k)
+    mask = 1 - torch.isinf(dist).view(-1)
+    row, col = row.view(-1)[mask], col.view(-1)[mask]
+
+    return torch.stack([row, col], dim=0)
 
 
 def knn_graph(x, k, batch=None, loop=False):