forked from mindspore-Ecosystem/mindspore
74 lines
2.5 KiB
Python
74 lines
2.5 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 Tensor
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import mindspore.nn as nn
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import mindspore.ops.operations.nn_ops as nn_ops
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class NetNthElement(nn.Cell):
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def __init__(self, reverse):
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super().__init__()
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self.nth_element = nn_ops.NthElement(reverse=reverse)
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def construct(self, x, k):
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return self.nth_element(x, k)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_nth_element_1d():
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"""
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Feature: test nth_element to find the t-th item in 1D input
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Description: 1D x, 0D n
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Expectation: success
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"""
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for mode in [context.PYNATIVE_MODE, context.GRAPH_MODE]:
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context.set_context(mode=mode, device_target="GPU")
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x = Tensor(np.array([1, 20, 5]).astype(np.int32))
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input_x = x.asnumpy()
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n = Tensor(1).astype("int32")
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input_n = n.asnumpy()
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net = NetNthElement(reverse=True)
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y = net(x, n)
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expect = np.sort(input_x, axis=-1)[..., ::-1][..., input_n].astype(np.int32)
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assert (y.asnumpy() == expect).all()
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_nth_element_2d():
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"""
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Feature: test nth_element to find the t-th item in 2D input
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Description: 2D x, 0D n
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Expectation: success
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"""
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for mode in [context.PYNATIVE_MODE, context.GRAPH_MODE]:
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context.set_context(mode=mode, device_target="GPU")
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x = Tensor(np.array([[11., 20., 52.], [67., 18., 29.], [130., 24., 5.], [0.3, -0.4, -15.]]).astype(np.float32))
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input_x = x.asnumpy()
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n = Tensor(1).astype("int32")
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input_n = n.asnumpy()
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net = NetNthElement(reverse=True)
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y = net(x, n)
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expect = np.sort(input_x, axis=-1)[..., ::-1][..., input_n].astype(np.float32)
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assert (y.asnumpy() == expect).all()
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