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.
97 lines
3.8 KiB
97 lines
3.8 KiB
# Copyright 2019-2021 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 numpy as np
|
|
import pytest
|
|
|
|
import mindspore.context as context
|
|
import mindspore.nn as nn
|
|
from mindspore import Tensor
|
|
from mindspore.common.api import ms_function
|
|
from mindspore.ops import operations as P
|
|
|
|
context.set_context(device_target='GPU')
|
|
|
|
|
|
class Net(nn.Cell):
|
|
def __init__(self):
|
|
super(Net, self).__init__()
|
|
self.add = P.AddN()
|
|
|
|
@ms_function
|
|
def construct(self, x, y, z):
|
|
return self.add((x, y, z))
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_net():
|
|
x = np.arange(1 * 3 * 3 * 4).reshape(1, 3, 3, 4).astype(np.float32)
|
|
y = np.arange(1 * 3 * 3 * 4).reshape(1, 3, 3, 4).astype(np.float32)
|
|
z = np.arange(1 * 3 * 3 * 4).reshape(1, 3, 3, 4).astype(np.float32)
|
|
add = Net()
|
|
output = add(Tensor(x), Tensor(y), Tensor(z))
|
|
expect_result = [[[[0., 3., 6., 9.],
|
|
[12., 15., 18., 21.],
|
|
[24., 27., 30., 33.]],
|
|
[[36., 39., 42., 45.],
|
|
[48., 51., 54., 57.],
|
|
[60., 63., 66., 69.]],
|
|
[[72., 75., 78., 81.],
|
|
[84., 87., 90., 93.],
|
|
[96., 99., 102., 105.]]]]
|
|
|
|
assert (output.asnumpy() == expect_result).all()
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_net_float64():
|
|
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
|
|
x = np.arange(1 * 3 * 3 * 4).reshape(1, 3, 3, 4).astype(np.float64)
|
|
y = np.arange(1 * 3 * 3 * 4).reshape(1, 3, 3, 4).astype(np.float64)
|
|
z = np.arange(1 * 3 * 3 * 4).reshape(1, 3, 3, 4).astype(np.float64)
|
|
add = Net()
|
|
output = add(Tensor(x), Tensor(y), Tensor(z))
|
|
expect_result = np.array([[[[0., 3., 6., 9.],
|
|
[12., 15., 18., 21.],
|
|
[24., 27., 30., 33.]],
|
|
[[36., 39., 42., 45.],
|
|
[48., 51., 54., 57.],
|
|
[60., 63., 66., 69.]],
|
|
[[72., 75., 78., 81.],
|
|
[84., 87., 90., 93.],
|
|
[96., 99., 102., 105.]]]]).astype(np.float64)
|
|
assert (output.asnumpy() == expect_result).all()
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
|
|
x = np.arange(1 * 3 * 3 * 4).reshape(1, 3, 3, 4).astype(np.float64)
|
|
y = np.arange(1 * 3 * 3 * 4).reshape(1, 3, 3, 4).astype(np.float64)
|
|
z = np.arange(1 * 3 * 3 * 4).reshape(1, 3, 3, 4).astype(np.float64)
|
|
add = Net()
|
|
output = add(Tensor(x), Tensor(y), Tensor(z))
|
|
expect_result = np.array([[[[0., 3., 6., 9.],
|
|
[12., 15., 18., 21.],
|
|
[24., 27., 30., 33.]],
|
|
[[36., 39., 42., 45.],
|
|
[48., 51., 54., 57.],
|
|
[60., 63., 66., 69.]],
|
|
[[72., 75., 78., 81.],
|
|
[84., 87., 90., 93.],
|
|
[96., 99., 102., 105.]]]]).astype(np.float64)
|
|
assert (output.asnumpy() == expect_result).all()
|