You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
184 lines
7.6 KiB
184 lines
7.6 KiB
# Copyright 2020-2021 Huawei Technologies Co., Ltd
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
# ============================================================================
|
|
|
|
import pytest
|
|
import numpy as np
|
|
from mindspore import Tensor
|
|
from mindspore.ops import operations as P
|
|
import mindspore.nn as nn
|
|
import mindspore.context as context
|
|
from mindspore.common.api import ms_function
|
|
|
|
context.set_context(device_target="CPU")
|
|
|
|
|
|
class NetReduce(nn.Cell):
|
|
def __init__(self):
|
|
super(NetReduce, self).__init__()
|
|
self.axis0 = 0
|
|
self.axis1 = 1
|
|
self.axis2 = -1
|
|
self.axis3 = (0, 1)
|
|
self.axis4 = (0, 1, 2)
|
|
self.axis5 = (-1,)
|
|
self.axis6 = ()
|
|
self.reduce_mean = P.ReduceMean(False)
|
|
self.reduce_sum = P.ReduceSum(False)
|
|
self.reduce_max = P.ReduceMax(False)
|
|
self.reduce_min = P.ReduceMin(False)
|
|
|
|
@ms_function
|
|
def construct(self, indice):
|
|
return (self.reduce_mean(indice, self.axis0),
|
|
self.reduce_mean(indice, self.axis1),
|
|
self.reduce_mean(indice, self.axis2),
|
|
self.reduce_mean(indice, self.axis3),
|
|
self.reduce_mean(indice, self.axis4),
|
|
self.reduce_sum(indice, self.axis0),
|
|
self.reduce_sum(indice, self.axis2),
|
|
self.reduce_max(indice, self.axis0),
|
|
self.reduce_max(indice, self.axis2),
|
|
self.reduce_max(indice, self.axis5),
|
|
self.reduce_max(indice, self.axis6),
|
|
self.reduce_min(indice, self.axis0),
|
|
self.reduce_min(indice, self.axis1),
|
|
self.reduce_min(indice, self.axis2),
|
|
self.reduce_min(indice, self.axis3),
|
|
self.reduce_min(indice, self.axis4),
|
|
self.reduce_min(indice, self.axis5),
|
|
self.reduce_min(indice, self.axis6))
|
|
|
|
|
|
class NetReduceLogic(nn.Cell):
|
|
def __init__(self):
|
|
super(NetReduceLogic, self).__init__()
|
|
self.axis0 = 0
|
|
self.axis1 = -1
|
|
self.axis2 = (0, 1, 2)
|
|
self.axis3 = ()
|
|
self.reduce_all = P.ReduceAll(False)
|
|
self.reduce_any = P.ReduceAny(False)
|
|
|
|
@ms_function
|
|
def construct(self, indice):
|
|
return (self.reduce_all(indice, self.axis0),
|
|
self.reduce_all(indice, self.axis1),
|
|
self.reduce_all(indice, self.axis2),
|
|
self.reduce_all(indice, self.axis3),
|
|
self.reduce_any(indice, self.axis0),
|
|
self.reduce_any(indice, self.axis1),
|
|
self.reduce_any(indice, self.axis2),
|
|
self.reduce_any(indice, self.axis3),)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_reduce():
|
|
reduce = NetReduce()
|
|
indice = Tensor(np.array([
|
|
[[0., 2., 1., 4., 0., 2.], [3., 1., 2., 2., 4., 0.]],
|
|
[[2., 0., 1., 5., 0., 1.], [1., 0., 0., 4., 4., 3.]],
|
|
[[4., 1., 4., 0., 0., 0.], [2., 5., 1., 0., 1., 3.]]
|
|
]).astype(np.float32))
|
|
output = reduce(indice)
|
|
print(output[0])
|
|
print(output[1])
|
|
print(output[2])
|
|
print(output[3])
|
|
print(output[4])
|
|
print(output[5])
|
|
print(output[6])
|
|
print(output[7])
|
|
print(output[8])
|
|
print(output[9])
|
|
print(output[10])
|
|
print(output[11])
|
|
print(output[12])
|
|
print(output[13])
|
|
print(output[14])
|
|
print(output[15])
|
|
print(output[16])
|
|
print(output[17])
|
|
expect_0 = np.array([[2., 1., 2., 3., 0., 1], [2., 2., 1., 2., 3., 2.]]).astype(np.float32)
|
|
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(
|
|
np.float32)
|
|
expect_2 = np.array([[1.5, 2.], [1.5, 2.], [1.5, 2.]]).astype(np.float32)
|
|
expect_3 = np.array([2, 1.5, 1.5, 2.5, 1.5, 1.5]).astype(np.float32)
|
|
expect_4 = np.array([1.75]).astype(np.float32)
|
|
expect_5 = np.array([[6., 3., 6., 9., 0., 3.], [6., 6., 3., 6., 9., 6.]]).astype(np.float32)
|
|
expect_6 = np.array([[9., 12.], [9., 12.], [9., 12.]]).astype(np.float32)
|
|
expect_7 = np.array([[4., 2., 4., 5., 0., 2.], [3., 5., 2., 4., 4., 3.]]).astype(np.float32)
|
|
expect_8 = np.array([[4., 4.], [5., 4.], [4., 5.]]).astype(np.float32)
|
|
expect_9 = np.array([[0., 0., 1., 0., 0., 0.], [1., 0., 0., 0., 1., 0.]]).astype(np.float32)
|
|
expect_10 = np.array([[0., 1., 1., 2., 0., 0.], [1., 0., 0., 4., 0., 1.], [2., 1., 1., 0., 0., 0.]]).astype(
|
|
np.float32)
|
|
expect_11 = np.array([[0., 0.], [0., 0.], [0., 0.]]).astype(np.float32)
|
|
expect_12 = np.array([0., 0., 0., 0., 0., 0.]).astype(np.float32)
|
|
assert (output[0].asnumpy() == expect_0).all()
|
|
assert (output[1].asnumpy() == expect_1).all()
|
|
assert (output[2].asnumpy() == expect_2).all()
|
|
assert (output[3].asnumpy() == expect_3).all()
|
|
assert (output[4].asnumpy() == expect_4).all()
|
|
assert (output[5].asnumpy() == expect_5).all()
|
|
assert (output[6].asnumpy() == expect_6).all()
|
|
assert (output[7].asnumpy() == expect_7).all()
|
|
assert (output[8].asnumpy() == expect_8).all()
|
|
assert (output[9].asnumpy() == expect_8).all()
|
|
assert (output[10].asnumpy() == 5.0).all()
|
|
assert (output[11].asnumpy() == expect_9).all()
|
|
assert (output[12].asnumpy() == expect_10).all()
|
|
assert (output[13].asnumpy() == expect_11).all()
|
|
assert (output[14].asnumpy() == expect_12).all()
|
|
assert (output[15].asnumpy() == 0.0).all()
|
|
assert (output[16].asnumpy() == expect_11).all()
|
|
assert (output[17].asnumpy() == 0.0).all()
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_reduce_logic():
|
|
reduce_logic = NetReduceLogic()
|
|
indice_bool = Tensor([[[False, True, True, True, False, True],
|
|
[True, True, True, True, True, False]],
|
|
[[True, False, True, True, False, True],
|
|
[True, False, False, True, True, True]],
|
|
[[True, True, True, False, False, False],
|
|
[True, True, True, False, True, True]]])
|
|
output = reduce_logic(indice_bool)
|
|
expect_all_1 = np.array([[False, False, True, False, False, False],
|
|
[True, False, False, False, True, False]])
|
|
expect_all_2 = np.array([[False, False], [False, False], [False, False]])
|
|
expect_all_3 = False
|
|
expect_all_4 = False
|
|
expect_any_1 = np.array([[True, True, True, True, False, True], [True, True, True, True, True, True]])
|
|
expect_any_2 = np.array([[True, True], [True, True], [True, True]])
|
|
expect_any_3 = True
|
|
expect_any_4 = True
|
|
|
|
assert (output[0].asnumpy() == expect_all_1).all()
|
|
assert (output[1].asnumpy() == expect_all_2).all()
|
|
assert (output[2].asnumpy() == expect_all_3).all()
|
|
assert (output[3].asnumpy() == expect_all_4).all()
|
|
assert (output[4].asnumpy() == expect_any_1).all()
|
|
assert (output[5].asnumpy() == expect_any_2).all()
|
|
assert (output[6].asnumpy() == expect_any_3).all()
|
|
assert (output[7].asnumpy() == expect_any_4).all()
|
|
|
|
|
|
test_reduce()
|
|
test_reduce_logic()
|