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- # Copyright 2019 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.common.dtype as mstype
- import mindspore.context as context
- from mindspore.common.tensor import Tensor
- from mindspore.nn import Cell
- from mindspore.ops import operations as P
-
-
- class Net(Cell):
- def __init__(self):
- super(Net, self).__init__()
- self.Cast = P.Cast()
-
- def construct(self, x0, type0, x1, type1):
- output = (self.Cast(x0, type0),
- self.Cast(x1, type1))
- return output
-
-
- @pytest.mark.level0
- @pytest.mark.platform_x86_gpu_training
- @pytest.mark.env_onecard
- def test_cast():
- x0 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.float32))
- t0 = mstype.float16
- x1 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.float16))
- t1 = mstype.float32
-
- context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
- net = Net()
- output = net(x0, t0, x1, t1)
- type0 = output[0].asnumpy().dtype
- assert type0 == 'float16'
- type1 = output[1].asnumpy().dtype
- assert type1 == 'float32'
-
-
- @pytest.mark.level0
- @pytest.mark.platform_x86_gpu_training
- @pytest.mark.env_onecard
- def test_cast1():
- x0 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.int32))
- t0 = mstype.float32
- x1 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.bool))
- t1 = mstype.float32
-
- context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
- net = Net()
- output = net(x0, t0, x1, t1)
- type0 = output[0].asnumpy().dtype
- assert type0 == 'float32'
- type1 = output[1].asnumpy().dtype
- assert type1 == 'float32'
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