forked from mindspore-Ecosystem/mindspore
247 lines
10 KiB
Python
247 lines
10 KiB
Python
# Copyright 2020-2021 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(mode=context.PYNATIVE_MODE, 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, -2)
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self.axis4 = (0, 1, 2)
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self.axis5 = (-1,)
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self.axis6 = ()
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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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self.reduce_min = P.ReduceMin(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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self.reduce_max(indice, self.axis5),
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self.reduce_max(indice, self.axis6),
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self.reduce_min(indice, self.axis0),
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self.reduce_min(indice, self.axis1),
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self.reduce_min(indice, self.axis2),
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self.reduce_min(indice, self.axis3),
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self.reduce_min(indice, self.axis4),
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self.reduce_min(indice, self.axis5),
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self.reduce_min(indice, self.axis6))
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class NetReduceLogic(nn.Cell):
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def __init__(self):
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super(NetReduceLogic, self).__init__()
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self.axis0 = 0
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self.axis1 = -1
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self.axis2 = (0, 1, 2)
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self.axis3 = ()
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self.reduce_all = P.ReduceAll(False)
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self.reduce_any = P.ReduceAny(False)
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@ms_function
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def construct(self, indice):
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return (self.reduce_all(indice, self.axis0),
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self.reduce_all(indice, self.axis1),
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self.reduce_all(indice, self.axis2),
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self.reduce_all(indice, self.axis3),
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self.reduce_any(indice, self.axis0),
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self.reduce_any(indice, self.axis1),
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self.reduce_any(indice, self.axis2),
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self.reduce_any(indice, self.axis3),)
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class NetReduceProd(nn.Cell):
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def __init__(self):
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super(NetReduceProd, 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 = ()
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self.reduce_prod = P.ReduceProd(False)
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self.reduce_prod_keep = P.ReduceProd(True)
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@ms_function
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def construct(self, indices):
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return (self.reduce_prod(indices, self.axis0),
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self.reduce_prod(indices, self.axis1),
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self.reduce_prod(indices, self.axis2),
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self.reduce_prod(indices, self.axis3),
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self.reduce_prod_keep(indices, self.axis4))
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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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"""
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/// Feature: Reduce
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/// Description: reduce tensor elements, include reduce_mean, reduce_max, etc.
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/// Expectation: Euqal to numpy results
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"""
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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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print(output[9])
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print(output[10])
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print(output[11])
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print(output[12])
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print(output[13])
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print(output[14])
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print(output[15])
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print(output[16])
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print(output[17])
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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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expect_9 = np.array([[0., 0., 1., 0., 0., 0.], [1., 0., 0., 0., 1., 0.]]).astype(np.float32)
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expect_10 = np.array([[0., 1., 1., 2., 0., 0.], [1., 0., 0., 4., 0., 1.], [2., 1., 1., 0., 0., 0.]]).astype(
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np.float32)
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expect_11 = np.array([[0., 0.], [0., 0.], [0., 0.]]).astype(np.float32)
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expect_12 = np.array([0., 0., 0., 0., 0., 0.]).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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assert (output[9].asnumpy() == expect_8).all()
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assert (output[10].asnumpy() == 5.0).all()
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assert (output[11].asnumpy() == expect_9).all()
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assert (output[12].asnumpy() == expect_10).all()
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assert (output[13].asnumpy() == expect_11).all()
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assert (output[14].asnumpy() == expect_12).all()
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assert (output[15].asnumpy() == 0.0).all()
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assert (output[16].asnumpy() == expect_11).all()
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assert (output[17].asnumpy() == 0.0).all()
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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_logic():
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"""
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/// Feature: Reduce logic
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/// Description: Include reduce_all, reduce_any
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/// Expectation: Euqal to numpy results
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"""
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reduce_logic = NetReduceLogic()
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indice_bool = Tensor([[[False, True, True, True, False, True],
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[True, True, True, True, True, False]],
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[[True, False, True, True, False, True],
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[True, False, False, True, True, True]],
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[[True, True, True, False, False, False],
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[True, True, True, False, True, True]]])
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output = reduce_logic(indice_bool)
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expect_all_1 = np.array([[False, False, True, False, False, False],
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[True, False, False, False, True, False]])
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expect_all_2 = np.array([[False, False], [False, False], [False, False]])
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expect_all_3 = False
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expect_all_4 = False
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expect_any_1 = np.array([[True, True, True, True, False, True], [True, True, True, True, True, True]])
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expect_any_2 = np.array([[True, True], [True, True], [True, True]])
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expect_any_3 = True
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expect_any_4 = True
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assert (output[0].asnumpy() == expect_all_1).all()
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assert (output[1].asnumpy() == expect_all_2).all()
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assert (output[2].asnumpy() == expect_all_3).all()
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assert (output[3].asnumpy() == expect_all_4).all()
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assert (output[4].asnumpy() == expect_any_1).all()
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assert (output[5].asnumpy() == expect_any_2).all()
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assert (output[6].asnumpy() == expect_any_3).all()
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assert (output[7].asnumpy() == expect_any_4).all()
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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_prod():
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"""
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/// Feature: Reduce prod
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/// Description: Product of tensor elements
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/// Expectation: Euqal to numpy results
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"""
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reduce_prod = NetReduceProd()
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indices = Tensor(np.array([[[1, 1, 1, 1, 1, 1], [2, 2, 2, 2, 2, 2], [3, 3, 3, 3, 3, 3]],
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[[4, 4, 4, 4, 4, 4], [5, 5, 5, 5, 5, 5], [6, 6, 6, 6, 6, 6]],
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[[7, 7, 7, 7, 7, 7], [8, 8, 8, 8, 8, 8], [9, 9, 9, 9, 9, 9]]]).astype(np.float32))
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output = reduce_prod(indices)
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expect_prod_0 = np.array([[28, 28, 28, 28, 28, 28],
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[80, 80, 80, 80, 80, 80],
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[162, 162, 162, 162, 162, 162]]).astype(np.float32)
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expect_prod_1 = np.array([[6, 6, 6, 6, 6, 6],
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[120, 120, 120, 120, 120, 120],
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[504, 504, 504, 504, 504, 504]]).astype(np.float32)
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expect_prod_2 = np.array([[1.00000e+00, 6.40000e+01, 7.29000e+02],
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[4.09600e+03, 1.56250e+04, 4.66560e+04],
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[1.17649e+05, 2.62144e+05, 5.31441e+05]]).astype(np.float32)
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expect_prod_3 = np.array([362880, 362880, 362880, 362880, 362880, 362880]).astype(np.float32)
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expect_prod_4 = np.array([[[2.2833798e+33]]]).astype(np.float32)
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assert (output[0].asnumpy() == expect_prod_0).all()
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assert (output[1].asnumpy() == expect_prod_1).all()
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assert (output[2].asnumpy() == expect_prod_2).all()
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assert (output[3].asnumpy() == expect_prod_3).all()
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assert (output[4].asnumpy() == expect_prod_4).all()
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test_reduce()
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test_reduce_logic()
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