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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 open3d.core as o3c
import numpy as np
import pytest
import operator
import sys
import os
sys.path.append(os.path.dirname(os.path.realpath(__file__)) + "/..")
from open3d_benchmark import list_tensor_sizes, list_non_bool_dtypes, list_float_dtypes, to_numpy_dtype
class UnaryEWOps:
@staticmethod
def sqrt(a):
return a.sqrt()
@staticmethod
def sin(a):
return a.sin()
@staticmethod
def cos(a):
return a.cos()
@staticmethod
def neg(a):
return a.neg()
@staticmethod
def exp(a):
return a.exp()
@staticmethod
def abs(a):
return a.abs()
@staticmethod
def isnan(a):
return a.isnan()
@staticmethod
def isinf(a):
return a.isinf()
@staticmethod
def isfinite(a):
return a.isfinite()
@staticmethod
def floor(a):
return a.floor()
@staticmethod
def ceil(a):
return a.ceil()
@staticmethod
def round(a):
return a.round()
@staticmethod
def trunc(a):
return a.trunc()
@staticmethod
def logical_not(a):
return a.logical_not()
def list_unary_ops():
return [
UnaryEWOps.neg,
UnaryEWOps.abs,
UnaryEWOps.isnan,
UnaryEWOps.isinf,
UnaryEWOps.isfinite,
UnaryEWOps.floor,
UnaryEWOps.ceil,
UnaryEWOps.round,
UnaryEWOps.trunc,
UnaryEWOps.logical_not,
]
def list_float_unary_ops():
return [
UnaryEWOps.sqrt,
UnaryEWOps.sin,
UnaryEWOps.cos,
UnaryEWOps.exp,
]
def to_numpy_unary_op(op):
conversions = {
UnaryEWOps.sqrt: np.sqrt,
UnaryEWOps.sin: np.sin,
UnaryEWOps.cos: np.cos,
UnaryEWOps.neg: operator.neg,
UnaryEWOps.exp: np.exp,
UnaryEWOps.abs: np.abs,
UnaryEWOps.isnan: np.isnan,
UnaryEWOps.isinf: np.isinf,
UnaryEWOps.isfinite: np.isfinite,
UnaryEWOps.floor: np.floor,
UnaryEWOps.ceil: np.ceil,
UnaryEWOps.round: np.round,
UnaryEWOps.trunc: np.trunc,
UnaryEWOps.logical_not: np.logical_not,
}
return conversions[op]
@pytest.mark.parametrize("size", list_tensor_sizes())
@pytest.mark.parametrize("dtype", list_non_bool_dtypes())
@pytest.mark.parametrize("op", list_unary_ops())
def test_unary_ew_ops(benchmark, size, dtype, op):
# Set upper bound to 88 to avoid overflow for exp() op.
np_a = np.array(np.random.uniform(1, 88, size), dtype=to_numpy_dtype(dtype))
a = o3c.Tensor(np_a, dtype=dtype, device=o3c.Device("CPU:0"))
benchmark(op, a)
@pytest.mark.parametrize("size", list_tensor_sizes())
@pytest.mark.parametrize("dtype", list_float_dtypes())
@pytest.mark.parametrize("op", list_float_unary_ops())
def test_float_unary_ew_ops(benchmark, size, dtype, op):
# Set upper bound to 88 to avoid overflow for exp() op.
np_a = np.array(np.random.uniform(1, 88, size), dtype=to_numpy_dtype(dtype))
a = o3c.Tensor(np_a, dtype=dtype, device=o3c.Device("CPU:0"))
benchmark(op, a)
@pytest.mark.parametrize("size", list_tensor_sizes())
@pytest.mark.parametrize("dtype", list_non_bool_dtypes())
@pytest.mark.parametrize("op", list_unary_ops())
def test_unary_ew_ops_numpy(benchmark, size, dtype, op):
# Set upper bound to 88 to avoid overflow for exp() op.
np_a = np.array(np.random.uniform(1, 88, size), dtype=to_numpy_dtype(dtype))
benchmark(to_numpy_unary_op(op), np_a)
@pytest.mark.parametrize("size", list_tensor_sizes())
@pytest.mark.parametrize("dtype", list_float_dtypes())
@pytest.mark.parametrize("op", list_float_unary_ops())
def test_float_unary_ew_ops_numpy(benchmark, size, dtype, op):
# Set upper bound to 88 to avoid overflow for exp() op.
np_a = np.array(np.random.uniform(1, 88, size), dtype=to_numpy_dtype(dtype))
benchmark(to_numpy_unary_op(op), np_a)
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