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from itertools import product
import pytest
import scipy.spatial
import torch
from torch_cluster import knn, knn_graph
from torch_cluster.testing import devices, grad_dtypes, tensor
def to_set(edge_index):
return set([(i, j) for i, j in edge_index.t().tolist()])
@pytest.mark.parametrize('dtype,device', product(grad_dtypes, devices))
def test_knn(dtype, device):
x = tensor([
[-1, -1],
[-1, +1],
[+1, +1],
[+1, -1],
[-1, -1],
[-1, +1],
[+1, +1],
[+1, -1],
], dtype, device)
y = tensor([
[1, 0],
[-1, 0],
], dtype, device)
batch_x = tensor([0, 0, 0, 0, 1, 1, 1, 1], torch.long, device)
batch_y = tensor([0, 1], torch.long, device)
edge_index = knn(x, y, 2)
assert to_set(edge_index) == set([(0, 2), (0, 3), (1, 0), (1, 1)])
jit = torch.jit.script(knn)
edge_index = jit(x, y, 2)
assert to_set(edge_index) == set([(0, 2), (0, 3), (1, 0), (1, 1)])
edge_index = knn(x, y, 2, batch_x, batch_y)
assert to_set(edge_index) == set([(0, 2), (0, 3), (1, 4), (1, 5)])
if x.is_cuda:
edge_index = knn(x, y, 2, batch_x, batch_y, cosine=True)
assert to_set(edge_index) == set([(0, 2), (0, 3), (1, 4), (1, 5)])
# Skipping a batch
batch_x = tensor([0, 0, 0, 0, 2, 2, 2, 2], torch.long, device)
batch_y = tensor([0, 2], torch.long, device)
edge_index = knn(x, y, 2, batch_x, batch_y)
assert to_set(edge_index) == set([(0, 2), (0, 3), (1, 4), (1, 5)])
@pytest.mark.parametrize('dtype,device', product(grad_dtypes, devices))
def test_knn_graph(dtype, device):
x = tensor([
[-1, -1],
[-1, +1],
[+1, +1],
[+1, -1],
], dtype, device)
edge_index = knn_graph(x, k=2, flow='target_to_source')
assert to_set(edge_index) == set([(0, 1), (0, 3), (1, 0), (1, 2), (2, 1),
(2, 3), (3, 0), (3, 2)])
edge_index = knn_graph(x, k=2, flow='source_to_target')
assert to_set(edge_index) == set([(1, 0), (3, 0), (0, 1), (2, 1), (1, 2),
(3, 2), (0, 3), (2, 3)])
jit = torch.jit.script(knn_graph)
edge_index = jit(x, k=2, flow='source_to_target')
assert to_set(edge_index) == set([(1, 0), (3, 0), (0, 1), (2, 1), (1, 2),
(3, 2), (0, 3), (2, 3)])
@pytest.mark.parametrize('dtype,device', product([torch.float], devices))
def test_knn_graph_large(dtype, device):
x = torch.randn(1000, 3, dtype=dtype, device=device)
edge_index = knn_graph(x, k=5, flow='target_to_source', loop=True)
tree = scipy.spatial.cKDTree(x.cpu().numpy())
_, col = tree.query(x.cpu(), k=5)
truth = set([(i, j) for i, ns in enumerate(col) for j in ns])
assert to_set(edge_index.cpu()) == truth
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