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# ----------------------------------------------------------------------------
# - Open3D: www.open3d.org -
# ----------------------------------------------------------------------------
# Copyright (c) 2018-2024 www.open3d.org
# SPDX-License-Identifier: MIT
# ----------------------------------------------------------------------------
import os
import sys
import numpy as np
import open3d as o3d
import open3d.core as o3c
import pytest
sys.path.append(os.path.dirname(os.path.realpath(__file__)) + "/..")
from open3d_test import list_devices
np.random.seed(0)
@pytest.mark.parametrize("device", list_devices())
def test_knn_index(device):
dtype = o3c.float32
t = o3c.Tensor.zeros((10, 3), dtype, device=device)
nns = o3c.nns.NearestNeighborSearch(t)
assert nns.knn_index()
assert nns.fixed_radius_index(0.1)
assert nns.hybrid_index(0.1)
# Multi radii search is only supported on CPU.
if device.get_type() == o3c.Device.DeviceType.CPU:
assert nns.multi_radius_index()
@pytest.mark.parametrize("device", list_devices())
def test_knn_search(device):
dtype = o3c.float32
dataset_points = o3c.Tensor(
[[0.0, 0.0, 0.0], [0.0, 0.0, 0.1], [0.0, 0.0, 0.2], [0.0, 0.1, 0.0],
[0.0, 0.1, 0.1], [0.0, 0.1, 0.2], [0.0, 0.2, 0.0], [0.0, 0.2, 0.1],
[0.0, 0.2, 0.2], [0.1, 0.0, 0.0]],
dtype=dtype,
device=device)
nns = o3c.nns.NearestNeighborSearch(dataset_points)
nns.knn_index()
# Single query point.
query_points = o3c.Tensor([[0.064705, 0.043921, 0.087843]],
dtype=dtype,
device=device)
indices, distances = nns.knn_search(query_points, 3)
np.testing.assert_equal(indices.cpu().numpy(),
np.array([[1, 4, 9]], dtype=np.int64))
np.testing.assert_allclose(distances.cpu().numpy(),
np.array([[0.00626358, 0.00747938, 0.0108912]],
dtype=np.float64),
rtol=1e-5,
atol=0)
# Multiple query points.
query_points = o3c.Tensor(
[[0.064705, 0.043921, 0.087843], [0.064705, 0.043921, 0.087843]],
dtype=dtype,
device=device)
indices, distances = nns.knn_search(query_points, 3)
np.testing.assert_equal(indices.cpu().numpy(),
np.array([[1, 4, 9], [1, 4, 9]], dtype=np.int64))
np.testing.assert_allclose(distances.cpu().numpy(),
np.array([[0.00626358, 0.00747938, 0.0108912],
[0.00626358, 0.00747938, 0.0108912]],
dtype=np.float64),
rtol=1e-5,
atol=0)
@pytest.mark.parametrize("device", list_devices())
@pytest.mark.parametrize("dtype", [o3c.float32, o3c.float64])
def test_fixed_radius_search(device, dtype):
dataset_points = o3c.Tensor(
[[0.0, 0.0, 0.0], [0.0, 0.0, 0.1], [0.0, 0.0, 0.2], [0.0, 0.1, 0.0],
[0.0, 0.1, 0.1], [0.0, 0.1, 0.2], [0.0, 0.2, 0.0], [0.0, 0.2, 0.1],
[0.0, 0.2, 0.2], [0.1, 0.0, 0.0]],
dtype=dtype,
device=device)
nns = o3c.nns.NearestNeighborSearch(dataset_points)
nns.fixed_radius_index(0.1)
# Single query point.
query_points = o3c.Tensor([[0.064705, 0.043921, 0.087843]],
dtype=dtype,
device=device)
indices, distances, neighbors_row_splits = nns.fixed_radius_search(
query_points, 0.1)
np.testing.assert_equal(indices.cpu().numpy(),
np.array([1, 4], dtype=np.int64))
np.testing.assert_allclose(distances.cpu().numpy(),
np.array([0.00626358, 0.00747938],
dtype=np.float64),
rtol=1e-5,
atol=0)
np.testing.assert_equal(neighbors_row_splits.cpu().numpy(),
np.array([0, 2], dtype=np.int64))
# Multiple query points.
query_points = o3c.Tensor(
[[0.064705, 0.043921, 0.087843], [0.064705, 0.043921, 0.087843]],
dtype=dtype,
device=device)
indices, distances, neighbors_row_splits = nns.fixed_radius_search(
query_points, 0.1)
np.testing.assert_equal(indices.cpu().numpy(),
np.array([1, 4, 1, 4], dtype=np.int64))
np.testing.assert_allclose(
distances.cpu().numpy(),
np.array([0.00626358, 0.00747938, 0.00626358, 0.00747938],
dtype=np.float64),
rtol=1e-5,
atol=0)
np.testing.assert_equal(neighbors_row_splits.cpu().numpy(),
np.array([0, 2, 4], dtype=np.int64))
@pytest.mark.parametrize("dtype", [o3c.float32, o3c.float64])
def test_hybrid_search_random(dtype):
if o3c.cuda.device_count() > 0:
dataset_size, query_size = 1000, 100
radius, k = 0.1, 10
dataset_np = np.random.rand(dataset_size, 3)
dataset_points = o3c.Tensor(dataset_np, dtype=dtype)
dataset_points_cuda = dataset_points.cuda()
nns = o3c.nns.NearestNeighborSearch(dataset_points)
nns_cuda = o3c.nns.NearestNeighborSearch(dataset_points_cuda)
for _ in range(10):
query_np = np.random.rand(query_size, 3)
query_points = o3c.Tensor(query_np, dtype=dtype)
query_points_cuda = query_points.cuda()
nns.hybrid_index(radius)
indices, distances, counts = nns.hybrid_search(
query_points, radius, k)
nns_cuda.hybrid_index(radius)
indices_cuda, distances_cuda, counts_cuda = nns_cuda.hybrid_search(
query_points_cuda, radius, k)
np.testing.assert_allclose(distances.numpy(),
distances_cuda.cpu().numpy(),
rtol=1e-5,
atol=0)
np.testing.assert_equal(indices.numpy(), indices_cuda.cpu().numpy())
np.testing.assert_equal(counts.numpy(), counts_cuda.cpu().numpy())
@pytest.mark.parametrize("dtype", [o3c.float32, o3c.float64])
def test_fixed_radius_search_random(dtype):
if o3c.cuda.device_count() > 0:
dataset_size, query_size = 1000, 100
radius = 0.1
dataset_np = np.random.rand(dataset_size, 3)
dataset_points = o3c.Tensor(dataset_np, dtype=dtype)
dataset_points_cuda = dataset_points.cuda()
nns = o3c.nns.NearestNeighborSearch(dataset_points)
nns_cuda = o3c.nns.NearestNeighborSearch(dataset_points_cuda)
for _ in range(10):
query_np = np.random.rand(query_size, 3)
query_points = o3c.Tensor(query_np, dtype=dtype)
query_points_cuda = query_points.cuda()
nns.fixed_radius_index(radius)
indices, distances, neighbors_row_splits = nns.fixed_radius_search(
query_points, radius)
nns_cuda.fixed_radius_index(radius)
indices_cuda, distances_cuda, neighbors_row_splits_cuda = nns_cuda.fixed_radius_search(
query_points_cuda, radius)
np.testing.assert_equal(neighbors_row_splits.numpy(),
neighbors_row_splits_cuda.cpu().numpy())
np.testing.assert_allclose(distances.numpy(),
distances_cuda.cpu().numpy(),
rtol=1e-5,
atol=0)
np.testing.assert_equal(indices.numpy(), indices_cuda.cpu().numpy())
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