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57 lines
1.8 KiB
57 lines
1.8 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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import numpy as np
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import mindspore.context as context
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.common import dtype as mstype
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from mindspore.ops import composite as C
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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class Net(nn.Cell):
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def __init__(self, shape, seed=0):
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super(Net, self).__init__()
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self.shape = shape
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self.seed = seed
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def construct(self, mean, stddev):
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return C.normal(self.shape, mean, stddev, self.seed)
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def test_net_1D():
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seed = 10
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shape = (3, 2, 4)
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mean = 1.0
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stddev = 1.0
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net = Net(shape, seed)
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tmean, tstddev = Tensor(mean, mstype.float32), Tensor(stddev, mstype.float32)
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output = net(tmean, tstddev)
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assert output.shape == (3, 2, 4)
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def test_net_ND():
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seed = 10
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shape = (3, 1, 2)
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mean = np.array([[[1], [2]], [[3], [4]], [[5], [6]]]).astype(np.float32)
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stddev = np.array([1.0]).astype(np.float32)
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net = Net(shape, seed)
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tmean, tstddev = Tensor(mean, mstype.float32), Tensor(stddev, mstype.float32)
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output = net(tmean, tstddev)
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assert output.shape == (3, 2, 2)
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