forked from mindspore-Ecosystem/mindspore
161 lines
4.7 KiB
Python
161 lines
4.7 KiB
Python
# Copyright 2020 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 Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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self.status = P.FloatStatus()
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def construct(self, x):
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return self.status(x)
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class Netnan(nn.Cell):
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def __init__(self):
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super(Netnan, self).__init__()
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self.isnan = P.IsNan()
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def construct(self, x):
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return self.isnan(x)
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class Netinf(nn.Cell):
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def __init__(self):
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super(Netinf, self).__init__()
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self.isinf = P.IsInf()
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def construct(self, x):
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return self.isinf(x)
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class Netfinite(nn.Cell):
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def __init__(self):
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super(Netfinite, self).__init__()
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self.isfinite = P.IsFinite()
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def construct(self, x):
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return self.isfinite(x)
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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x1 = np.array([[1.2, 2, np.nan, 88]]).astype(np.float32)
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x2 = np.array([[np.inf, 1, 88.0, 0]]).astype(np.float32)
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x3 = np.array([[1, 2], [3, 4], [5.0, 88.0]]).astype(np.float32)
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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.float16, np.float32, np.float64])
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def test_status(dtype):
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"""
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Feature: ALL To ALL
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Description: test cases for FloatStatus
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Expectation: the result match to expectation
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"""
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ms_status = Net()
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output1 = ms_status(Tensor(x1.astype(dtype)))
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expect1 = 1
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assert output1.asnumpy()[0] == expect1
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output2 = ms_status(Tensor(x2.astype(dtype)))
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expect2 = 1
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assert output2.asnumpy()[0] == expect2
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output3 = ms_status(Tensor(x3.astype(dtype)))
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expect3 = 0
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assert output3.asnumpy()[0] == expect3
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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.float16, np.float32, np.float64])
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def test_nan(dtype):
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"""
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Feature: ALL To ALL
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Description: test cases for IsNan
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Expectation: the result match to expectation
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"""
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ms_isnan = Netnan()
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output1 = ms_isnan(Tensor(x1.astype(dtype)))
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expect1 = [[False, False, True, False]]
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assert (output1.asnumpy() == expect1).all()
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output2 = ms_isnan(Tensor(x2.astype(dtype)))
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expect2 = [[False, False, False, False]]
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assert (output2.asnumpy() == expect2).all()
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output3 = ms_isnan(Tensor(x3.astype(dtype)))
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expect3 = [[False, False], [False, False], [False, False]]
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assert (output3.asnumpy() == expect3).all()
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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.float16, np.float32, np.float64])
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def test_inf(dtype):
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"""
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Feature: ALL To ALL
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Description: test cases for IsInf
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Expectation: the result match to expectation
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"""
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ms_isinf = Netinf()
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output1 = ms_isinf(Tensor(x1.astype(dtype)))
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expect1 = [[False, False, False, False]]
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assert (output1.asnumpy() == expect1).all()
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output2 = ms_isinf(Tensor(x2.astype(dtype)))
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expect2 = [[True, False, False, False]]
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assert (output2.asnumpy() == expect2).all()
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output3 = ms_isinf(Tensor(x3.astype(dtype)))
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expect3 = [[False, False], [False, False], [False, False]]
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assert (output3.asnumpy() == expect3).all()
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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.float16, np.float32, np.float64])
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def test_finite(dtype):
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"""
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Feature: ALL To ALL
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Description: test cases for Netfinite
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Expectation: the result match to expectation
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"""
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ms_isfinite = Netfinite()
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output1 = ms_isfinite(Tensor(x1.astype(dtype)))
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expect1 = [[True, True, False, True]]
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assert (output1.asnumpy() == expect1).all()
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output2 = ms_isfinite(Tensor(x2.astype(dtype)))
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expect2 = [[False, True, True, True]]
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assert (output2.asnumpy() == expect2).all()
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output3 = ms_isfinite(Tensor(x3.astype(dtype)))
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expect3 = [[True, True], [True, True], [True, True]]
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assert (output3.asnumpy() == expect3).all()
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