forked from mindspore-Ecosystem/mindspore
49 lines
1.5 KiB
Python
49 lines
1.5 KiB
Python
|
# Copyright 2023 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.common.dtype as mstype
|
||
|
|
import mindspore.nn as nn
|
||
|
|
import mindspore.ops as ops
|
||
|
|
from mindspore import Tensor
|
||
|
|
from mindspore import context
|
||
|
|
|
||
|
|
|
||
|
|
class Net(nn.Cell):
|
||
|
|
def construct(self, x):
|
||
|
|
return ops.det(x)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.level0
|
||
|
|
@pytest.mark.platform_x86_cpu
|
||
|
|
@pytest.mark.platform_arm_cpu
|
||
|
|
@pytest.mark.platform_x86_gpu_training
|
||
|
|
@pytest.mark.platform_x86_ascend_training
|
||
|
|
@pytest.mark.platform_arm_ascend_training
|
||
|
|
@pytest.mark.env_onecard
|
||
|
|
@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
|
||
|
|
def test_det(mode):
|
||
|
|
"""
|
||
|
|
Feature: ops.det(x)
|
||
|
|
Description: Verify the result of ops.det(x)
|
||
|
|
Expectation: success
|
||
|
|
"""
|
||
|
|
context.set_context(mode=mode)
|
||
|
|
net = Net()
|
||
|
|
x = Tensor([[1.5, 2.0], [3, 4.6]], dtype=mstype.float32)
|
||
|
|
output = net(x)
|
||
|
|
expected = np.array(0.9)
|
||
|
|
assert np.allclose(output.asnumpy(), expected)
|