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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 ml ops were not built
pytestmark = mltest.default_marks
# the supported dtypes for the point coordinates
dtypes = pytest.mark.parametrize('dtype', [np.float32, np.float64])
# the GPU only supports single precision float
gpu_dtypes = [np.float32]
@dtypes
@mltest.parametrize.ml
@pytest.mark.parametrize('num_points_queries', [(10, 5), (31, 33), (33, 31),
(123, 345)])
@pytest.mark.parametrize('radius', [0.1, 0.3])
@pytest.mark.parametrize('hash_table_size_factor', [1 / 8, 1 / 64])
@pytest.mark.parametrize('metric', ['L1', 'L2', 'Linf'])
@pytest.mark.parametrize('ignore_query_point', [False, True])
@pytest.mark.parametrize('return_distances', [False, True])
@pytest.mark.parametrize('index_dtype', ['int32', 'int64'])
def test_fixed_radius_search(dtype, ml, num_points_queries, radius,
hash_table_size_factor, metric, ignore_query_point,
return_distances, index_dtype):
# skip dtype not supported on GPU
if mltest.is_gpu_device_name(ml.device) and not dtype in gpu_dtypes:
return
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)
# 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(queries, radius, p=p_norm)
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.FixedRadiusSearch(metric=metric,
ignore_query_point=ignore_query_point,
return_distances=return_distances,
index_dtype=index_dtype_)
ans = mltest.run_op(
ml,
ml.device,
True,
layer,
points,
queries=queries,
radius=radius,
hash_table_size_factor=hash_table_size_factor,
)
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)
else:
gt_dist = np.linalg.norm(q - points[j], ord=p_norm)
np.testing.assert_allclose(dist, gt_dist, rtol=1e-7, atol=1e-8)
@mltest.parametrize.ml
def test_fixed_radius_search_empty_point_sets(ml):
rng = np.random.RandomState(123)
dtype = np.float32
radius = 1
hash_table_size_factor = 1 / 64
# no query points
points = rng.random(size=(100, 3)).astype(dtype)
queries = rng.random(size=(0, 3)).astype(dtype)
layer = ml.layers.FixedRadiusSearch(return_distances=True)
ans = mltest.run_op(
ml,
ml.device,
True,
layer,
points,
queries=queries,
radius=radius,
hash_table_size_factor=hash_table_size_factor,
)
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)
ans = mltest.run_op(
ml,
ml.device,
True,
layer,
points,
queries=queries,
radius=radius,
hash_table_size_factor=hash_table_size_factor,
)
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,)
@dtypes
@mltest.parametrize.ml
@pytest.mark.parametrize('batch_size', [2, 3, 8])
@pytest.mark.parametrize('radius', [0.1, 0.3])
@pytest.mark.parametrize('hash_table_size_factor', [1 / 8, 1 / 64])
@pytest.mark.parametrize('metric', ['L1', 'L2', 'Linf'])
@pytest.mark.parametrize('ignore_query_point', [False, True])
@pytest.mark.parametrize('return_distances', [False, True])
@pytest.mark.parametrize('index_dtype', ['int32', 'int64'])
def test_fixed_radius_search_batches(dtype, ml, batch_size, radius,
hash_table_size_factor, metric,
ignore_query_point, return_distances,
index_dtype):
# skip dtype not supported on GPU
if mltest.is_gpu_device_name(ml.device) and not dtype in gpu_dtypes:
return
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)
# kd trees for computing the ground truth
p_norm = {'L1': 1, 'L2': 2, 'Linf': np.inf}[metric]
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]]
tree = cKDTree(points_i, copy_data=True)
gt_neighbors_index.extend([
list(
tree.query_ball_point(q, radius, p=p_norm) +
points_row_splits[i]) for q in queries_i
])
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.FixedRadiusSearch(metric=metric,
ignore_query_point=ignore_query_point,
return_distances=return_distances,
index_dtype=index_dtype_)
ans = mltest.run_op(
ml,
ml.device,
True,
layer,
points,
queries=queries,
radius=radius,
points_row_splits=points_row_splits,
queries_row_splits=queries_row_splits,
hash_table_size_factor=hash_table_size_factor,
)
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)
else:
gt_dist = np.linalg.norm(q - points[j], ord=p_norm)
np.testing.assert_allclose(dist, gt_dist, rtol=1e-7, atol=1e-8)
@dtypes
@mltest.parametrize.ml
@pytest.mark.parametrize('batch_size', [2, 3, 8])
@pytest.mark.parametrize('radius', [0.1, 0.3])
@pytest.mark.parametrize('hash_table_size_factor', [1 / 8, 1 / 64])
@pytest.mark.parametrize('metric', ['L1', 'L2', 'Linf'])
@pytest.mark.parametrize('ignore_query_point', [False, True])
@pytest.mark.parametrize('return_distances', [False, True])
@pytest.mark.parametrize('index_dtype', ['int32', 'int64'])
def test_fixed_radius_search_raggedtensor(dtype, ml, batch_size, radius,
hash_table_size_factor, metric,
ignore_query_point, return_distances,
index_dtype):
# the problem is specific to tensorflow
if ml.module.__name__ != 'tensorflow':
return
# skip dtype not supported on GPU
if mltest.is_gpu_device_name(ml.device) and not dtype in gpu_dtypes:
return
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)
# kd trees for computing the ground truth
p_norm = {'L1': 1, 'L2': 2, 'Linf': np.inf}[metric]
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]]
tree = cKDTree(points_i, copy_data=True)
gt_neighbors_index.extend([
list(
tree.query_ball_point(q, radius, p=p_norm) +
points_row_splits[i]) for q in queries_i
])
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')
points_ragged = tf.RaggedTensor.from_row_splits(
values=points, row_splits=points_row_splits)
queries_ragged = tf.RaggedTensor.from_row_splits(
values=queries, row_splits=queries_row_splits)
layer = ml.layers.FixedRadiusSearch(metric=metric,
ignore_query_point=ignore_query_point,
return_distances=return_distances,
index_dtype=index_dtype_)
ans = mltest.run_op(
ml,
ml.device,
True,
layer,
points_ragged,
queries=queries_ragged,
radius=radius,
hash_table_size_factor=hash_table_size_factor,
)
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)
else:
gt_dist = np.linalg.norm(q - points[j], ord=p_norm)
np.testing.assert_allclose(dist, gt_dist, rtol=1e-7, atol=1e-8)
|
