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.
82 lines
2.5 KiB
82 lines
2.5 KiB
# 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 SquareNet(nn.Cell):
|
|
def __init__(self):
|
|
super(SquareNet, self).__init__()
|
|
self.square = P.Square()
|
|
|
|
def construct(self, x):
|
|
return self.square(x)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_square():
|
|
x = np.array([1, 2, 3]).astype(np.int16)
|
|
net = SquareNet()
|
|
output = net(Tensor(x))
|
|
expect_output = np.array([1, 4, 9]).astype(np.int16)
|
|
print(output)
|
|
assert np.all(output.asnumpy() == expect_output)
|
|
|
|
x = np.array([1, 2, 3]).astype(np.int32)
|
|
net = SquareNet()
|
|
output = net(Tensor(x))
|
|
expect_output = np.array([1, 4, 9]).astype(np.int32)
|
|
print(output)
|
|
assert np.all(output.asnumpy() == expect_output)
|
|
|
|
x = np.array([1, 2, 3]).astype(np.int64)
|
|
net = SquareNet()
|
|
output = net(Tensor(x))
|
|
expect_output = np.array([1, 4, 9]).astype(np.int64)
|
|
print(output)
|
|
assert np.all(output.asnumpy() == expect_output)
|
|
|
|
x = np.array([1, 2, 3]).astype(np.float16)
|
|
net = SquareNet()
|
|
output = net(Tensor(x))
|
|
expect_output = np.array([1, 4, 9]).astype(np.float16)
|
|
print(output)
|
|
assert np.all(output.asnumpy() == expect_output)
|
|
|
|
x = np.array([1, 2, 3]).astype(np.float32)
|
|
net = SquareNet()
|
|
output = net(Tensor(x))
|
|
expect_output = np.array([1, 4, 9]).astype(np.float32)
|
|
print(output)
|
|
assert np.all(output.asnumpy() == expect_output)
|
|
|
|
x = np.array([1, 2, 3]).astype(np.float64)
|
|
net = SquareNet()
|
|
output = net(Tensor(x))
|
|
expect_output = np.array([1, 4, 9]).astype(np.float64)
|
|
print(output)
|
|
assert np.all(output.asnumpy() == expect_output)
|
|
|
|
test_square()
|