mindspore/tests/st/tensor/test_log.py

76 lines
2.2 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
from mindspore import Tensor
class Log10Net(nn.Cell):
def construct(self, x):
return x.log10()
class Log2Net(nn.Cell):
def construct(self, x):
return x.log2()
@pytest.mark.level0
@pytest.mark.platform_x86_cpu
@pytest.mark.platform_arm_cpu
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.env_onecard
@pytest.mark.parametrize('mode', [ms.GRAPH_MODE, ms.PYNATIVE_MODE])
def test_log10(mode):
"""
Feature: test Tensor.log10.
Description: Verify the result of Tensor.log10..
Expectation: expect correct forward result.
"""
ms.set_context(mode=mode)
x = Tensor([10, 100, 1000], dtype=ms.float32)
log10 = Log10Net()
output = log10(x)
expect_output = np.array([1, 2, 3], dtype=np.float32)
assert np.allclose(output.asnumpy(), expect_output)
@pytest.mark.level0
@pytest.mark.platform_x86_cpu
@pytest.mark.platform_arm_cpu
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.env_onecard
@pytest.mark.parametrize('mode', [ms.GRAPH_MODE, ms.PYNATIVE_MODE])
def test_log2(mode):
"""
Feature: test Tensor.log2.
Description: Verify the result of Tensor.log2..
Expectation: expect correct forward result.
"""
ms.set_context(mode=mode)
x = Tensor([2, 4, 8], dtype=ms.float32)
log2 = Log2Net()
output = log2(x)
expect_output = np.array([1, 2, 3], dtype=np.float32)
assert np.allclose(output.asnumpy(), expect_output)