# ---------------------------------------------------------------------------- # - Open3D: www.open3d.org - # ---------------------------------------------------------------------------- # The MIT License (MIT) # # Copyright (c) 2018-2021 www.open3d.org # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING # FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS # IN THE SOFTWARE. # ---------------------------------------------------------------------------- import itertools import operator 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__)) + "/..") sys.path.append( os.path.dirname(os.path.realpath(__file__)) + "/../../../examples/python/utility") from open3d_benchmark import list_devices, list_float_dtypes, to_numpy_dtype class NNSOps: @staticmethod def knn_setup(datasets, nns_opt): index = o3c.nns.NearestNeighborSearch(datasets) index.knn_index() return index @staticmethod def radius_setup(datasets, nns_opt): radius = nns_opt["radius"] index = o3c.nns.NearestNeighborSearch(datasets) index.fixed_radius_index(radius) return index @staticmethod def hybrid_setup(datasets, nns_opt): radius = nns_opt["radius"] index = o3c.nns.NearestNeighborSearch(datasets) index.hybrid_index(radius) return index @staticmethod def knn_search(index, queries, nns_opt): knn = nns_opt["knn"] result = index.knn_search(queries, knn) return result @staticmethod def radius_search(index, queries, nns_opt): radius = nns_opt["radius"] result = index.fixed_radius_search(queries, radius) return result @staticmethod def hybrid_search(index, queries, nns_opt): radius, knn = nns_opt["radius"], nns_opt["knn"] result = index.hybrid_search(queries, radius, knn) return result def list_sizes(): num_points = (10000,) return num_points def list_dimensions(): dimensions = (3, 8, 16, 32) return dimensions @pytest.mark.parametrize("size", list_sizes()) @pytest.mark.parametrize("dim", list_dimensions()) @pytest.mark.parametrize("dtype", list_float_dtypes()) @pytest.mark.parametrize("device", list_devices()) def test_knn_setup(benchmark, size, dim, dtype, device): nns_opt = dict(knn=1, radius=0.01) np_a = np.array(np.random.rand(size, dim), dtype=to_numpy_dtype(dtype)) a = o3c.Tensor(np_a, dtype=dtype, device=device) benchmark(NNSOps.knn_setup, a, nns_opt) @pytest.mark.parametrize("size", list_sizes()) @pytest.mark.parametrize("dim", list_dimensions()) @pytest.mark.parametrize("dtype", list_float_dtypes()) @pytest.mark.parametrize("device", list_devices()) def test_knn_search(benchmark, size, dim, dtype, device): nns_opt = dict(knn=1, radius=0.01) np_a = np.array(np.random.rand(size, dim), dtype=to_numpy_dtype(dtype)) np_b = np.array(np.random.rand(size, dim), dtype=to_numpy_dtype(dtype)) a = o3c.Tensor(np_a, dtype=dtype, device=device) b = o3c.Tensor(np_b, dtype=dtype, device=device) index = NNSOps.knn_setup(a, nns_opt) benchmark(NNSOps.knn_search, index, b, nns_opt) @pytest.mark.parametrize("size", list_sizes()) @pytest.mark.parametrize("dtype", list_float_dtypes()) @pytest.mark.parametrize("device", list_devices()) def test_radius_setup(benchmark, size, dtype, device): nns_opt = dict(knn=1, radius=0.01) np_a = np.array(np.random.rand(size, 3), dtype=to_numpy_dtype(dtype)) a = o3c.Tensor(np_a, dtype=dtype, device=device) benchmark(NNSOps.radius_setup, a, nns_opt) @pytest.mark.parametrize("size", list_sizes()) @pytest.mark.parametrize("dtype", list_float_dtypes()) @pytest.mark.parametrize("device", list_devices()) def test_radius_search(benchmark, size, dtype, device): nns_opt = dict(knn=1, radius=0.01) np_a = np.array(np.random.rand(size, 3), dtype=to_numpy_dtype(dtype)) np_b = np.array(np.random.rand(size, 3), dtype=to_numpy_dtype(dtype)) a = o3c.Tensor(np_a, dtype=dtype, device=device) b = o3c.Tensor(np_b, dtype=dtype, device=device) index = NNSOps.radius_setup(a, nns_opt) benchmark(NNSOps.radius_search, index, b, nns_opt) @pytest.mark.parametrize("size", list_sizes()) @pytest.mark.parametrize("dtype", list_float_dtypes()) @pytest.mark.parametrize("device", list_devices()) def test_hybrid_setup(benchmark, size, dtype, device): nns_opt = dict(knn=1, radius=0.01) np_a = np.array(np.random.rand(size, 3), dtype=to_numpy_dtype(dtype)) a = o3c.Tensor(np_a, dtype=dtype, device=device) benchmark(NNSOps.hybrid_setup, a, nns_opt) @pytest.mark.parametrize("size", list_sizes()) @pytest.mark.parametrize("dtype", list_float_dtypes()) @pytest.mark.parametrize("device", list_devices()) def test_hybrid_search(benchmark, size, dtype, device): nns_opt = dict(knn=1, radius=0.01) np_a = np.array(np.random.rand(size, 3), dtype=to_numpy_dtype(dtype)) np_b = np.array(np.random.rand(size, 3), dtype=to_numpy_dtype(dtype)) a = o3c.Tensor(np_a, dtype=dtype, device=device) b = o3c.Tensor(np_b, dtype=dtype, device=device) index = NNSOps.hybrid_setup(a, nns_opt) benchmark(NNSOps.hybrid_search, index, b, nns_opt)