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- # 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 numpy as np
- import pytest
- import mindspore.context as context
- import mindspore.nn as nn
- from mindspore import Tensor
- from mindspore.ops import operations as P
- from mindspore.common import dtype as mstype
-
- context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
-
- class NetCholesky(nn.Cell):
- def __init__(self):
- super(NetCholesky, self).__init__()
- self.cholesky = P.Cholesky()
-
- def construct(self, x):
- return self.cholesky(x)
-
-
- @pytest.mark.level0
- @pytest.mark.platform_x86_gpu_training
- @pytest.mark.env_onecard
- def test_cholesky_fp32():
- cholesky = NetCholesky()
- x = np.array([[4, 12, -16], [12, 37, -43], [-16, -43, 98]]).astype(np.float32)
- output = cholesky(Tensor(x, dtype=mstype.float32))
- expect = np.linalg.cholesky(x)
- tol = 1e-6
- assert (np.abs(output.asnumpy() - expect) < tol).all()
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