forked from mindspore-Ecosystem/mindspore
60 lines
2.0 KiB
Python
60 lines
2.0 KiB
Python
# Copyright 2022 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
|
|
import mindspore.context as context
|
|
import mindspore.nn as nn
|
|
import mindspore.common.dtype as mstype
|
|
from mindspore import Tensor
|
|
from mindspore.scipy.ops import LU
|
|
|
|
|
|
class NetLU(nn.Cell):
|
|
def __init__(self):
|
|
super(NetLU, self).__init__()
|
|
self.ops = LU()
|
|
|
|
def construct(self, a):
|
|
return self.ops(a)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_lu():
|
|
"""
|
|
Feature: lu op use custom compile test on graph mode.
|
|
Description: test lu op on graph mode
|
|
Expectation: the result equal to expect.
|
|
"""
|
|
num = 32
|
|
one_tensor = np.zeros((num, num))
|
|
for i in range(num):
|
|
for j in range(num):
|
|
one_tensor[i, j] = min(min(i, j), 8) + 1
|
|
upper_matrix = np.triu(one_tensor).astype(np.float32)
|
|
lower_matrix = np.tril(np.ones((num, num))).astype(np.float32)
|
|
input1 = np.dot(lower_matrix, upper_matrix)
|
|
expect = upper_matrix + lower_matrix - np.eye(num)
|
|
real_input = Tensor(input1, mstype.float32)
|
|
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
|
|
net = NetLU()
|
|
result = net(real_input)
|
|
rtol = 0.001
|
|
atol = 0.001
|
|
assert np.allclose(result[0].asnumpy(), expect, rtol, atol, equal_nan=True)
|