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94 lines
3.7 KiB
94 lines
3.7 KiB
5 years ago
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# 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 pytest
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import numpy as np
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from mindspore import Tensor
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from mindspore.ops import operations as P
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import mindspore.nn as nn
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import mindspore.context as context
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from mindspore.common.api import ms_function
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context.set_context(device_target="CPU")
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class NetReduce(nn.Cell):
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def __init__(self):
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super(NetReduce, self).__init__()
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self.axis0 = 0
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self.axis1 = 1
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self.axis2 = -1
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self.axis3 = (0, 1)
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self.axis4 = (0, 1, 2)
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self.reduce_mean = P.ReduceMean(False)
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self.reduce_sum = P.ReduceSum(False)
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self.reduce_max = P.ReduceMax(False)
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@ms_function
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def construct(self, indice):
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return (self.reduce_mean(indice, self.axis0),
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self.reduce_mean(indice, self.axis1),
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self.reduce_mean(indice, self.axis2),
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self.reduce_mean(indice, self.axis3),
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self.reduce_mean(indice, self.axis4),
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self.reduce_sum(indice, self.axis0),
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self.reduce_sum(indice, self.axis2),
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self.reduce_max(indice, self.axis0),
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self.reduce_max(indice, self.axis2))
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_reduce():
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reduce = NetReduce()
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indice = Tensor(np.array([
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[[0., 2., 1., 4., 0., 2.], [3., 1., 2., 2., 4., 0.]],
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[[2., 0., 1., 5., 0., 1.], [1., 0., 0., 4., 4., 3.]],
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[[4., 1., 4., 0., 0., 0.], [2., 5., 1., 0., 1., 3.]]
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]).astype(np.float32))
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output = reduce(indice)
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print(output[0])
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print(output[1])
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print(output[2])
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print(output[3])
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print(output[4])
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print(output[5])
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print(output[6])
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print(output[7])
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print(output[8])
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expect_0 = np.array([[2., 1., 2., 3., 0., 1], [2., 2., 1., 2., 3., 2.]]).astype(np.float32)
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expect_1 = np.array([[1.5, 1.5, 1.5, 3., 2., 1.], [1.5, 0., 0.5, 4.5, 2., 2.], [3., 3., 2.5, 0., 0.5, 1.5]]).astype(
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np.float32)
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expect_2 = np.array([[1.5, 2.], [1.5, 2.], [1.5, 2.]]).astype(np.float32)
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expect_3 = np.array([2, 1.5, 1.5, 2.5, 1.5, 1.5]).astype(np.float32)
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expect_4 = np.array([1.75]).astype(np.float32)
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expect_5 = np.array([[6., 3., 6., 9., 0., 3.], [6., 6., 3., 6., 9., 6.]]).astype(np.float32)
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expect_6 = np.array([[9., 12.], [9., 12.], [9., 12.]]).astype(np.float32)
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expect_7 = np.array([[4., 2., 4., 5., 0., 2.], [3., 5., 2., 4., 4., 3.]]).astype(np.float32)
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expect_8 = np.array([[4., 4.], [5., 4.], [4., 5.]]).astype(np.float32)
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assert (output[0].asnumpy() == expect_0).all()
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assert (output[1].asnumpy() == expect_1).all()
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assert (output[2].asnumpy() == expect_2).all()
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assert (output[3].asnumpy() == expect_3).all()
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assert (output[4].asnumpy() == expect_4).all()
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assert (output[5].asnumpy() == expect_5).all()
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assert (output[6].asnumpy() == expect_6).all()
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assert (output[7].asnumpy() == expect_7).all()
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assert (output[8].asnumpy() == expect_8).all()
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test_reduce()
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