forked from mindspore-Ecosystem/mindspore
95 lines
2.8 KiB
Python
95 lines
2.8 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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from mindspore import ops
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import mindspore.nn as nn
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from mindspore import Tensor
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import mindspore.common.dtype as mstype
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from mindspore.ops import operations as P
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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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.unique = P.Unique()
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def construct(self, x):
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return self.unique(x)
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class NetFunc(nn.Cell):
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def __init__(self):
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super(NetFunc, self).__init__()
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self.unique = ops.unique
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def construct(self, x):
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return self.unique(x)
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@pytest.mark.level1
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_unqiue():
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x = Tensor(np.array([1, 1, 2, 2, 3, 3]), mstype.int32)
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unique = Net()
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output = unique(x)
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expect1 = np.array([1, 2, 3])
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expect2 = np.array([0, 0, 1, 1, 2, 2])
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assert (output[0].asnumpy() == expect1).all()
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assert (output[1].asnumpy() == expect2).all()
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_unqiue_func_1d():
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"""
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Feature: Test unique function
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Description: Input 1D Tensor
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Expectation: Successful execution.
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"""
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x = Tensor(np.array([1, 1, 2, 2, 3, 3]), mstype.int32)
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unique = NetFunc()
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output = unique(x)
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expect1 = np.array([1, 2, 3])
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expect2 = np.array([0, 0, 1, 1, 2, 2])
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assert (output[0].asnumpy() == expect1).all()
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assert (output[1].asnumpy() == expect2).all()
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_unqiue_func_2d():
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"""
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Feature: Test unique function
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Description: Input 2D Tensor
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Expectation: Successful execution.
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"""
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x = Tensor(np.array([[1, 1, 2], [2, 3, 3]]), mstype.int32)
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unique = NetFunc()
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output = unique(x)
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expect1 = np.array([1, 2, 3])
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expect2 = np.array([[0, 0, 1], [1, 2, 2]])
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assert (output[0].asnumpy() == expect1).all()
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assert (output[1].asnumpy() == expect2).all()
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