You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
93 lines
3.0 KiB
93 lines
3.0 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
# ============================================================================
|
|
|
|
import pytest
|
|
from mindspore.ops import operations as P
|
|
from mindspore.nn import Cell
|
|
from mindspore.common.tensor import Tensor
|
|
import mindspore.context as context
|
|
import numpy as np
|
|
|
|
|
|
class NetAnd(Cell):
|
|
def __init__(self):
|
|
super(NetAnd, self).__init__()
|
|
self.logicaland = P.LogicalAnd()
|
|
|
|
def construct(self, x, y):
|
|
return self.logicaland(x, y)
|
|
|
|
class NetOr(Cell):
|
|
def __init__(self):
|
|
super(NetOr, self).__init__()
|
|
self.logicalor = P.LogicalOr()
|
|
|
|
def construct(self, x, y):
|
|
return self.logicalor(x, y)
|
|
|
|
class NetNot(Cell):
|
|
def __init__(self):
|
|
super(NetNot, self).__init__()
|
|
self.logicalnot = P.LogicalNot()
|
|
|
|
def construct(self, x):
|
|
return self.logicalnot(x)
|
|
|
|
x = np.array([True, False, False]).astype(np.bool)
|
|
y = np.array([False]).astype(np.bool)
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_logicaland():
|
|
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
|
|
logicaland = NetAnd()
|
|
output = logicaland(Tensor(x), Tensor(y))
|
|
assert np.all(output.asnumpy() == np.logical_and(x, y))
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
|
|
logicaland = NetAnd()
|
|
output = logicaland(Tensor(x), Tensor(y))
|
|
assert np.all(output.asnumpy() == np.logical_and(x, y))
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_logicalor():
|
|
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
|
|
logicalor = NetOr()
|
|
output = logicalor(Tensor(x), Tensor(y))
|
|
assert np.all(output.asnumpy() == np.logical_or(x, y))
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
|
|
logicalor = NetOr()
|
|
output = logicalor(Tensor(x), Tensor(y))
|
|
assert np.all(output.asnumpy() == np.logical_or(x, y))
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_logicalnot():
|
|
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
|
|
logicalnot = NetNot()
|
|
output = logicalnot(Tensor(x))
|
|
assert np.all(output.asnumpy() == np.logical_not(x))
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
|
|
logicalnot = NetNot()
|
|
output = logicalnot(Tensor(x))
|
|
assert np.all(output.asnumpy() == np.logical_not(x))
|
|
|