forked from mindspore-Ecosystem/mindspore
57 lines
1.9 KiB
Python
57 lines
1.9 KiB
Python
# Copyright 2020-2022 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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import numpy as np
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import pytest
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import mindspore.context as context
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.ops import operations as P
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class SquareNet(nn.Cell):
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def __init__(self):
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super(SquareNet, self).__init__()
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self.ops = P.Square()
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def construct(self, x):
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return self.ops(x)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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@pytest.mark.parametrize('dtype', [np.float32, np.float64])
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def test_square_normal(dtype):
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"""
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Feature: ALL To ALL
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Description: test cases for Square
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Expectation: the result match to numpy
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"""
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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x_np = np.random.rand(2, 3, 4, 4).astype(np.float32)
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output_ms = P.Square()(Tensor(x_np))
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output_np = np.square(x_np)
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assert np.allclose(output_ms.asnumpy(), output_np)
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x_np = np.random.rand(2, 3, 1, 5, 4, 4).astype(np.float32)
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output_ms = P.Square()(Tensor(x_np))
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output_np = np.square(x_np)
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assert np.allclose(output_ms.asnumpy(), output_np)
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x_np = np.random.rand(2).astype(np.float32)
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output_ms = P.Square()(Tensor(x_np))
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output_np = np.square(x_np)
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assert np.allclose(output_ms.asnumpy(), output_np)
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