mindspore/tests/st/tensor/test_sgn.py

49 lines
1.6 KiB
Python

# Copyright 2022 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 numpy as np
import pytest
import mindspore as ms
import mindspore.nn as nn
class Net(nn.Cell):
def construct(self, x):
return x.sgn()
@pytest.mark.level0
@pytest.mark.platform_x86_cpu
@pytest.mark.platform_arm_cpu
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
@pytest.mark.parametrize('mode', [ms.GRAPH_MODE, ms.PYNATIVE_MODE])
def test_sgn_normal(mode):
"""
Feature: sgn
Description: Verify the result of sgn
Expectation: success
"""
ms.set_context(mode=mode)
net = Net()
x = ms.Tensor([[3 + 4j, 7 - 24j, 0, 6 + 8j, 8], [15 + 20j, 7 - 24j, 0, 3 + 4j, 20]], dtype=ms.complex64)
output = net(x)
expect_output = np.array([[0.6 + 0.8j, 0.28 - 0.96j, 0. + 0.j, 0.6 + 0.8j, 1. + 0.j],
[0.6 + 0.8j, 0.28 - 0.96j, 0. + 0.j, 0.6 + 0.8j, 1. + 0.j]])
print(output)
print(expect_output)
assert np.allclose(output.asnumpy(), expect_output)