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101 lines
3.0 KiB
101 lines
3.0 KiB
# 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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context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
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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.ops = P.IsFinite()
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def construct(self, x):
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return self.ops(x)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu_training
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@pytest.mark.env_onecard
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def test_net():
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x0 = Tensor(np.array([np.log(-1), 0.4, np.log(0)]).astype(np.float16))
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x1 = Tensor(np.array([np.log(-1), 0.4, np.log(0)]).astype(np.float32))
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x2 = Tensor(np.array([np.log(-1), 0.4, np.log(0)]).astype(np.float64))
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x3 = Tensor(np.array([4, 1, -5]).astype(np.int8))
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x4 = Tensor(np.array([4, 1, -5]).astype(np.int16))
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x5 = Tensor(np.array([4, 1, -5]).astype(np.int32))
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x6 = Tensor(np.array([4, 1, -5]).astype(np.int64))
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x7 = Tensor(np.array([4, 1, -5]).astype(np.uint8))
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x8 = Tensor(np.array([4, 1, -5]).astype(np.uint16))
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x9 = Tensor(np.array([4, 1, -5]).astype(np.uint32))
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x10 = Tensor(np.array([4, 1, -5]).astype(np.uint64))
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x11 = Tensor(np.array([False, True, False]).astype(np.bool_))
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net = Net()
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out = net(x0).asnumpy()
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expect = [False, True, False]
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assert np.all(out == expect)
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out = net(x1).asnumpy()
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expect = [False, True, False]
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assert np.all(out == expect)
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out = net(x2).asnumpy()
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expect = [False, True, False]
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assert np.all(out == expect)
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out = net(x3).asnumpy()
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expect = [True, True, True]
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assert np.all(out == expect)
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out = net(x4).asnumpy()
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expect = [True, True, True]
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assert np.all(out == expect)
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out = net(x5).asnumpy()
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expect = [True, True, True]
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assert np.all(out == expect)
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out = net(x6).asnumpy()
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expect = [True, True, True]
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assert np.all(out == expect)
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out = net(x7).asnumpy()
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expect = [True, True, True]
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assert np.all(out == expect)
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out = net(x8).asnumpy()
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expect = [True, True, True]
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assert np.all(out == expect)
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out = net(x9).asnumpy()
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expect = [True, True, True]
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assert np.all(out == expect)
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out = net(x10).asnumpy()
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expect = [True, True, True]
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assert np.all(out == expect)
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out = net(x11).asnumpy()
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expect = [True, True, True]
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assert np.all(out == expect)
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