forked from mindspore-Ecosystem/mindspore
55 lines
1.7 KiB
Python
55 lines
1.7 KiB
Python
# Copyright 2022 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 operations as P
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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self.trunc = P.Trunc()
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def construct(self, x0):
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return self.trunc(x0)
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def test32_net():
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x = Tensor(np.array([1.2, -2.6, 5.0, 2.8, 0.2, -1.0, 2, -1.3]), mstype.float32)
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uniq = Net()
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output = uniq(x)
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print("x:\n", output)
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expect_x_result = [1., -2., 5., 2., 0., -1., 2, -1]
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print("expected_x:\n", expect_x_result)
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assert (output.asnumpy() == expect_x_result).all()
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def test16_net():
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x = Tensor(np.array([1.2, -2.6, 5.0, 2.8, 0.2, -1.0, 2, -1.3]), mstype.float16)
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uniq = Net()
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output = uniq(x)
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print("x:\n", output)
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expect_x_result = [1., -2., 5., 2., 0., -1., 2, -1]
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print("expected_x:\n", expect_x_result)
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assert (output.asnumpy() == expect_x_result).all()
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