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# ----------------------------------------------------------------------------
# - Open3D: www.open3d.org -
# ----------------------------------------------------------------------------
# Copyright (c) 2018-2024 www.open3d.org
# SPDX-License-Identifier: MIT
# ----------------------------------------------------------------------------
import open3d as o3d
import numpy as np
from scipy.spatial import cKDTree
import pytest
import mltest
if o3d._build_config['BUILD_PYTORCH_OPS']:
import torch
if o3d._build_config['BUILD_TENSORFLOW_OPS']:
import tensorflow as tf
# skip all tests if the tf ops were not built and disable warnings caused by
# tensorflow
pytestmark = mltest.default_marks
# the supported dtypes for the point coordinates
dtypes = pytest.mark.parametrize('dtype', [np.float32, np.float64])
@dtypes
@mltest.parametrize.ml_cpu_only
@pytest.mark.parametrize('num_points_queries', [(10, 5), (31, 33), (33, 31),
(123, 345)])
@pytest.mark.parametrize('metric', ['L1', 'L2'])
@pytest.mark.parametrize('ignore_query_point', [False, True])
@pytest.mark.parametrize('return_distances', [False, True])
@pytest.mark.parametrize('normalize_distances', [False, True])
@pytest.mark.parametrize('index_dtype', ['int32', 'int64'])
def test_radius_search(dtype, ml, num_points_queries, metric,
ignore_query_point, return_distances,
normalize_distances, index_dtype):
rng = np.random.RandomState(123)
num_points, num_queries = num_points_queries
points = rng.random(size=(num_points, 3)).astype(dtype)
if ignore_query_point:
queries = points
else:
queries = rng.random(size=(num_queries, 3)).astype(dtype)
radii = rng.uniform(0.1, 0.3, size=queries.shape[:1]).astype(dtype)
# kd tree for computing the ground truth
tree = cKDTree(points, copy_data=True)
p_norm = {'L1': 1, 'L2': 2, 'Linf': np.inf}[metric]
gt_neighbors_index = [
tree.query_ball_point(q, r, p=p_norm) for q, r in zip(queries, radii)
]
if ml.module.__name__ == 'tensorflow':
index_dtype_ = {'int32': tf.int32, 'int64': tf.int64}[index_dtype]
elif ml.module.__name__ == 'torch':
index_dtype_ = {'int32': torch.int32, 'int64': torch.int64}[index_dtype]
else:
raise Exception('Unsupported ml framework')
layer = ml.layers.RadiusSearch(metric=metric,
ignore_query_point=ignore_query_point,
return_distances=return_distances,
normalize_distances=normalize_distances,
index_dtype=index_dtype_)
ans = mltest.run_op(
ml,
ml.device,
True,
layer,
points,
queries=queries,
radii=radii,
)
index_dtype_np = {'int32': np.int32, 'int64': np.int64}[index_dtype]
assert ans.neighbors_index.dtype == index_dtype_np
for i, q in enumerate(queries):
# check neighbors
start = ans.neighbors_row_splits[i]
end = ans.neighbors_row_splits[i + 1]
q_neighbors_index = ans.neighbors_index[start:end]
gt_set = set(gt_neighbors_index[i])
if ignore_query_point:
gt_set.remove(i)
assert gt_set == set(q_neighbors_index)
# check distances
if return_distances:
q_neighbors_dist = ans.neighbors_distance[start:end]
for j, dist in zip(q_neighbors_index, q_neighbors_dist):
if metric == 'L2':
gt_dist = np.sum((q - points[j])**2)
if normalize_distances:
gt_dist /= radii[i]**2
else:
gt_dist = np.linalg.norm(q - points[j], ord=p_norm)
if normalize_distances:
gt_dist /= radii[i]
np.testing.assert_allclose(dist, gt_dist, rtol=1e-7, atol=1e-7)
@mltest.parametrize.ml_cpu_only
def test_radius_search_empty_point_sets(ml):
rng = np.random.RandomState(123)
dtype = np.float32
# no query points
points = rng.random(size=(100, 3)).astype(dtype)
queries = rng.random(size=(0, 3)).astype(dtype)
radii = rng.uniform(0.1, 0.3, size=(0,)).astype(dtype)
layer = ml.layers.RadiusSearch(return_distances=True)
ans = mltest.run_op(
ml,
ml.device,
True,
layer,
points,
queries=queries,
radii=radii,
)
assert ans.neighbors_index.shape == (0,)
assert ans.neighbors_row_splits.shape == (1,)
assert ans.neighbors_distance.shape == (0,)
# no input points
points = rng.random(size=(0, 3)).astype(dtype)
queries = rng.random(size=(100, 3)).astype(dtype)
radii = rng.uniform(0.1, 0.3, size=(100,)).astype(dtype)
ans = mltest.run_op(
ml,
ml.device,
True,
layer,
points,
queries=queries,
radii=radii,
)
assert ans.neighbors_index.shape == (0,)
assert ans.neighbors_row_splits.shape == (101,)
np.testing.assert_array_equal(np.zeros_like(ans.neighbors_row_splits),
ans.neighbors_row_splits)
assert ans.neighbors_distance.shape == (0,)
@mltest.parametrize.ml_cpu_only
@pytest.mark.parametrize('batch_size', [2, 3, 8])
def test_radius_search_batches(ml, batch_size):
dtype = np.float32
metric = 'L2'
p_norm = {'L1': 1, 'L2': 2, 'Linf': np.inf}[metric]
ignore_query_point = False
return_distances = True
normalize_distances = True
rng = np.random.RandomState(123)
# create array defining start and end of each batch
points_row_splits = np.zeros(shape=(batch_size + 1,), dtype=np.int64)
queries_row_splits = np.zeros(shape=(batch_size + 1,), dtype=np.int64)
for i in range(batch_size):
points_row_splits[i + 1] = rng.randint(15) + points_row_splits[i]
queries_row_splits[i + 1] = rng.randint(15) + queries_row_splits[i]
num_points = points_row_splits[-1]
num_queries = queries_row_splits[-1]
points = rng.random(size=(num_points, 3)).astype(dtype)
if ignore_query_point:
queries = points
queries_row_splits = points_row_splits
else:
queries = rng.random(size=(num_queries, 3)).astype(dtype)
radii = rng.uniform(0.1, 0.3, size=queries.shape[:1]).astype(dtype)
# kd trees for computing the ground truth
gt_neighbors_index = []
for i in range(batch_size):
points_i = points[points_row_splits[i]:points_row_splits[i + 1]]
queries_i = queries[queries_row_splits[i]:queries_row_splits[i + 1]]
radii_i = radii[queries_row_splits[i]:queries_row_splits[i + 1]]
tree = cKDTree(points_i, copy_data=True)
gt_neighbors_index.extend([
list(tree.query_ball_point(q, r, p=p_norm) + points_row_splits[i])
for q, r in zip(queries_i, radii_i)
])
layer = ml.layers.RadiusSearch(metric=metric,
ignore_query_point=ignore_query_point,
normalize_distances=normalize_distances,
return_distances=return_distances)
ans = mltest.run_op(ml,
ml.device,
True,
layer,
points,
queries=queries,
radii=radii,
points_row_splits=points_row_splits,
queries_row_splits=queries_row_splits)
for i, q in enumerate(queries):
# check neighbors
start = ans.neighbors_row_splits[i]
end = ans.neighbors_row_splits[i + 1]
q_neighbors_index = ans.neighbors_index[start:end]
gt_set = set(gt_neighbors_index[i])
if ignore_query_point:
gt_set.remove(i)
assert gt_set == set(q_neighbors_index)
# check distances
if return_distances:
q_neighbors_dist = ans.neighbors_distance[start:end]
for j, dist in zip(q_neighbors_index, q_neighbors_dist):
if metric == 'L2':
gt_dist = np.sum((q - points[j])**2)
if normalize_distances:
gt_dist /= radii[i]**2
else:
gt_dist = np.linalg.norm(q - points[j], ord=p_norm)
if normalize_distances:
gt_dist /= radii[i]
np.testing.assert_allclose(dist, gt_dist, rtol=1e-7, atol=1e-7)
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