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52 lines
1.7 KiB
52 lines
1.7 KiB
# Copyright 2019 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 pytest
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from mindspore.ops import operations as P
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from mindspore.nn import Cell
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from mindspore.common.tensor import Tensor
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import mindspore.common.dtype as mstype
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import mindspore.context as context
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import numpy as np
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class Net(Cell):
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def __init__(self):
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super(Net, self).__init__()
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self.Cast = P.Cast()
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def construct(self, x0, type0, x1, type1):
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output = (self.Cast(x0, type0),
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self.Cast(x1, type1))
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return output
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_cast():
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x0 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.float32))
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t0 = mstype.float16
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x1 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.float16))
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t1 = mstype.float32
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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net = Net()
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output = net(x0, t0, x1, t1)
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type0 = output[0].asnumpy().dtype
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assert (type0 == 'float16')
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type1 = output[1].asnumpy().dtype
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assert (type1 == 'float32')
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