test_knn.py

from itertools import product

import pytest
import torch
from torch_cluster import knn

from .utils import grad_dtypes, devices, tensor


@pytest.mark.parametrize('dtype,device', product(grad_dtypes, devices))
def test_radius(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)

    out = knn(x, y, 2, batch_x, batch_y)
    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]