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.
54 lines
1.7 KiB
54 lines
1.7 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
|
|
from mindspore import Tensor
|
|
from mindspore.nn import Cell
|
|
import mindspore.ops.operations as P
|
|
|
|
|
|
class Net(Cell):
|
|
def __init__(self):
|
|
super(Net, self).__init__()
|
|
self.reduce_mean = P.ReduceMean(keep_dims=False)
|
|
|
|
def construct(self, x):
|
|
return self.reduce_mean(x)
|
|
|
|
|
|
def test_reduce_mean():
|
|
np.random.seed(0)
|
|
input_x = np.random.normal(0, 1, [2, 3, 4, 3]).astype(np.float32)
|
|
expect = np.mean(input_x, keepdims=False)
|
|
net = Net()
|
|
result = net(Tensor(input_x))
|
|
res = np.allclose(expect, result.asnumpy(), rtol=1.e-4, atol=1.e-7, equal_nan=True)
|
|
assert res
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_reduce_mean_gpu():
|
|
context.set_context(mode=context.GRAPH_MODE, enable_graph_kernel=True, device_target="GPU")
|
|
test_reduce_mean()
|
|
|
|
|
|
def test_reduce_mean_ascend():
|
|
context.set_context(mode=context.GRAPH_MODE, enable_graph_kernel=True, device_target="Ascend")
|
|
test_reduce_mean()
|