1# Copyright 2022 Huawei Technologies Co., Ltd 2# 3# Licensed under the Apache License, Version 2.0 (the "License"); 4# you may not use this file except in compliance with the License. 5# You may obtain a copy of the License at 6# 7# http://www.apache.org/licenses/LICENSE-2.0 8# 9# Unless required by applicable law or agreed to in writing, software 10# distributed under the License is distributed on an "AS IS" BASIS, 11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12# See the License for the specific language governing permissions and 13# limitations under the License. 14# ============================================================================ 15 16import numpy as np 17import pytest 18 19import mindspore as ms 20import mindspore.nn as nn 21import mindspore.ops as ops 22 23 24class Net(nn.Cell): 25 def construct(self, x): 26 output = ops.logdet(x) 27 return output 28 29 30@pytest.mark.level2 31@pytest.mark.platform_x86_cpu 32@pytest.mark.platform_arm_cpu 33@pytest.mark.platform_x86_gpu_training 34@pytest.mark.env_onecard 35@pytest.mark.parametrize('mode', [ms.GRAPH_MODE, ms.PYNATIVE_MODE]) 36def test_logdet(mode): 37 """ 38 Feature: ops.logdet 39 Description: Verify the result of ops.logdet 40 Expectation: success 41 """ 42 ms.set_context(mode=mode) 43 x = ms.Tensor(np.array([[[1, 2], [-4, 5]], [[7, 8], [-10, 11]]]), ms.float32) 44 net = Net() 45 output = net(x) 46 expected = np.array([2.564947, 5.0562468]) 47 assert np.allclose(output.asnumpy(), expected) 48