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# ----------------------------------------------------------------------------
# - 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 open3d as o3d
import numpy as np
import pytest
import mltest
# skip all tests if the ml ops were not built
pytestmark = mltest.default_marks
# the supported input dtypes
value_dtypes = pytest.mark.parametrize(
'dtype', [np.int32, np.int64, np.float32, np.float64])
@pytest.mark.parametrize('seed', range(3))
@value_dtypes
@mltest.parametrize.ml
def test_reduce_subarrays_sum_random(seed, dtype, ml):
rng = np.random.RandomState(seed)
values_shape = [rng.randint(100, 200)]
values = rng.uniform(0, 10, size=values_shape).astype(dtype)
row_splits = [0]
for _ in range(rng.randint(1, 10)):
row_splits.append(
rng.randint(0, values_shape[0] - row_splits[-1]) + row_splits[-1])
row_splits.extend(values_shape)
expected_result = []
for start, stop in zip(row_splits, row_splits[1:]):
# np.sum correctly handles zero length arrays and returns 0
expected_result.append(np.sum(values[start:stop]))
np.array(expected_result, dtype=dtype)
row_splits = np.array(row_splits, dtype=np.int64)
ans = mltest.run_op(ml,
ml.device,
True,
ml.ops.reduce_subarrays_sum,
values=values,
row_splits=row_splits)
if np.issubdtype(dtype, np.integer):
np.testing.assert_equal(ans, expected_result)
else: # floating point types
np.testing.assert_allclose(ans, expected_result, rtol=1e-5, atol=1e-8)
@mltest.parametrize.ml
def test_reduce_subarrays_sum_zero_length_values(ml):
rng = np.random.RandomState(1)
shape = [rng.randint(100, 200)]
values = np.array([], dtype=np.float32)
row_splits = [0]
for _ in range(rng.randint(1, 10)):
row_splits.append(
rng.randint(0, shape[0] - row_splits[-1]) + row_splits[-1])
row_splits.extend(shape)
row_splits = np.array(row_splits, dtype=np.int64)
ans = mltest.run_op(ml,
ml.device,
True,
ml.ops.reduce_subarrays_sum,
values=values,
row_splits=row_splits)
assert ans.shape == values.shape
assert ans.dtype == values.dtype
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