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- # Copyright 2021 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.
- # ============================================================================
- """st for scipy.utils"""
-
- import pytest
- import numpy as onp
- from mindspore import context, Tensor
- from mindspore.scipy.utils import _safe_normalize
-
-
- @pytest.mark.level0
- @pytest.mark.platform_x86_gpu_training
- @pytest.mark.platform_x86_cpu
- @pytest.mark.env_onecard
- @pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
- @pytest.mark.parametrize('shape', [(10,), (10, 1)])
- @pytest.mark.parametrize('dtype', [onp.float32, onp.float64])
- def test_safe_normalize(mode, shape, dtype):
- """
- Feature: ALL TO ALL
- Description: test cases for _safe_normalize
- Expectation: the result match scipy
- """
- context.set_context(mode=mode)
- x = onp.random.random(shape).astype(dtype)
- normalized_x, x_norm = _safe_normalize(Tensor(x))
-
- normalized_x = normalized_x.asnumpy()
- x_norm = x_norm.asnumpy()
- assert onp.allclose(onp.sum(normalized_x ** 2), 1)
- assert onp.allclose(x / x_norm, normalized_x)
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