forked from mindspore-Ecosystem/mindspore
91 lines
2.9 KiB
Python
91 lines
2.9 KiB
Python
# Copyright 2020 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 numpy as np
|
|
import pytest
|
|
|
|
import mindspore.context as context
|
|
import mindspore.nn as nn
|
|
from mindspore import Tensor
|
|
from mindspore.ops import operations as P
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
|
|
|
|
|
|
class Net2Inputs(nn.Cell):
|
|
def __init__(self):
|
|
super(Net2Inputs, self).__init__()
|
|
self.addn = P.AddN()
|
|
|
|
def construct(self, x, y):
|
|
return self.addn((x, y))
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_two_tensors_add():
|
|
"""
|
|
Feature: ALL To ALL
|
|
Description: test cases for AddN of two tensors
|
|
Expectation: the result match to numpy
|
|
"""
|
|
x = np.arange(2 * 3 * 2).reshape((2, 3, 2))
|
|
y = np.arange(88, 2 * 3 * 2 + 88).reshape((2, 3, 2))
|
|
addn_net = Net2Inputs()
|
|
dtypes = (np.int32, np.float32, np.float64)
|
|
for dtype in dtypes:
|
|
output = addn_net(Tensor(x.astype(dtype)), Tensor(y.astype(dtype)))
|
|
expect_result = (x + y).astype(dtype)
|
|
assert output.asnumpy().dtype == expect_result.dtype
|
|
assert np.array_equal(output.asnumpy(), expect_result)
|
|
|
|
|
|
class Net4Inputs(nn.Cell):
|
|
def __init__(self):
|
|
super(Net4Inputs, self).__init__()
|
|
self.addn = P.AddN()
|
|
|
|
def construct(self, x, y, m, n):
|
|
return self.addn((x, y, m, n))
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_four_tensors_add():
|
|
"""
|
|
Feature: ALL To ALL
|
|
Description: test cases for AddN of four tensors
|
|
Expectation: the result match to numpy
|
|
"""
|
|
x = np.arange(2 * 3).reshape((2, 3))
|
|
y = np.arange(1, 2 * 3 + 1).reshape((2, 3))
|
|
m = np.arange(2, 2 * 3 + 2).reshape((2, 3))
|
|
n = np.arange(3, 2 * 3 + 3).reshape((2, 3))
|
|
addn_net = Net4Inputs()
|
|
dtypes = (np.int32, np.float32, np.float64)
|
|
for dtype in dtypes:
|
|
output = addn_net(Tensor(x.astype(dtype)), Tensor(y.astype(dtype)),
|
|
Tensor(m.astype(dtype)), Tensor(n.astype(dtype)))
|
|
expect_result = (x + y + m + n).astype(dtype)
|
|
assert output.asnumpy().dtype == expect_result.dtype
|
|
assert np.array_equal(output.asnumpy(), expect_result)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
test_two_tensors_add()
|
|
test_four_tensors_add()
|