622 lines
19 KiB
Python
622 lines
19 KiB
Python
# 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 numpy as np
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import pytest
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import mindspore.common.dtype as mstype
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import mindspore.context as context
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from mindspore.common.tensor import Tensor
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from mindspore.nn import Cell
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from mindspore.ops import operations as P
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from mindspore.ops.operations import _inner_ops as inner
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class Net(Cell):
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def __init__(self, type0, type1):
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super(Net, self).__init__()
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self.Cast = P.Cast()
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self.type0 = type0
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self.type1 = type1
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def construct(self, x0, x1):
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output = (self.Cast(x0, self.type0),
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self.Cast(x1, self.type1))
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return output
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class NetDynamic(Cell):
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def __init__(self, type0, type1):
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super(NetDynamic, self).__init__()
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self.conv = inner.GpuConvertToDynamicShape()
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self.Cast = P.Cast()
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self.type0 = type0
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self.type1 = type1
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def construct(self, x0, x1):
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x0_conv = self.conv(x0)
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x1_conv = self.conv(x1)
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output = (self.Cast(x0_conv, self.type0),
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self.Cast(x1_conv, self.type1))
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return output
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@pytest.mark.level1
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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(t0, t1)
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output = net(x0, x1)
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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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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_cast1():
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x0 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.int32))
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t0 = mstype.float32
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x1 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.bool))
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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(t0, t1)
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output = net(x0, x1)
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type0 = output[0].asnumpy().dtype
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assert type0 == 'float32'
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type1 = output[1].asnumpy().dtype
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assert type1 == 'float32'
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_cast2():
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x0 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.float16))
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t0 = mstype.int32
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x1 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.float16))
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t1 = mstype.float64
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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net = Net(t0, t1)
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output = net(x0, x1)
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type0 = output[0].asnumpy().dtype
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assert type0 == 'int32'
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type1 = output[1].asnumpy().dtype
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assert type1 == 'float64'
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_cast3():
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x0 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.int64))
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t0 = mstype.int32
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x1 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.float32))
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t1 = mstype.int32
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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net = Net(t0, t1)
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output = net(x0, x1)
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type0 = output[0].asnumpy().dtype
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assert type0 == 'int32'
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type1 = output[1].asnumpy().dtype
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assert type1 == 'int32'
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_cast4():
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x0 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.int32))
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t0 = mstype.float16
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x1 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.int32))
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t1 = mstype.int8
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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net = Net(t0, t1)
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output = net(x0, x1)
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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 == 'int8'
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_cast5():
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x0 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.int32))
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t0 = mstype.uint8
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x1 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.int32))
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t1 = mstype.bool_
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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net = Net(t0, t1)
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output = net(x0, x1)
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type0 = output[0].asnumpy().dtype
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assert type0 == 'uint8'
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type1 = output[1].asnumpy().dtype
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assert type1 == 'bool'
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_cast6():
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x0 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.int8))
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t0 = mstype.float64
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x1 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.int8))
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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(t0, t1)
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output = net(x0, x1)
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type0 = output[0].asnumpy().dtype
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assert type0 == 'float64'
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type1 = output[1].asnumpy().dtype
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assert type1 == 'float32'
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_cast7():
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x0 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.int8))
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t0 = mstype.float32
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x1 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.int8))
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t1 = mstype.float16
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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net = Net(t0, t1)
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output = net(x0, x1)
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type0 = output[0].asnumpy().dtype
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assert type0 == 'float32'
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type1 = output[1].asnumpy().dtype
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assert type1 == 'float16'
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_cast8():
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x0 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.int8))
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t0 = mstype.int32
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x1 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.int8))
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t1 = mstype.int16
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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net = Net(t0, t1)
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output = net(x0, x1)
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type0 = output[0].asnumpy().dtype
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assert type0 == 'int32'
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type1 = output[1].asnumpy().dtype
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assert type1 == 'int16'
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_cast9():
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x0 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.int8))
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t0 = mstype.int64
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x1 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.bool))
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t1 = mstype.float16
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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net = Net(t0, t1)
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output = net(x0, x1)
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type0 = output[0].asnumpy().dtype
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assert type0 == 'int64'
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type1 = output[1].asnumpy().dtype
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assert type1 == 'float16'
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_cast10():
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x0 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.bool))
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t0 = mstype.int8
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x1 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.bool))
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t1 = mstype.float64
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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net = Net(t0, t1)
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output = net(x0, x1)
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type0 = output[0].asnumpy().dtype
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assert type0 == 'int8'
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type1 = output[1].asnumpy().dtype
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assert type1 == 'float64'
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_cast11():
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x0 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.bool))
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t0 = mstype.int16
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x1 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.bool))
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t1 = mstype.int32
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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net = Net(t0, t1)
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output = net(x0, x1)
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type0 = output[0].asnumpy().dtype
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assert type0 == 'int16'
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type1 = output[1].asnumpy().dtype
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assert type1 == 'int32'
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_cast12():
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x0 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.bool))
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t0 = mstype.int64
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x1 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.uint8))
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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(t0, t1)
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output = net(x0, x1)
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type0 = output[0].asnumpy().dtype
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assert type0 == 'int64'
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type1 = output[1].asnumpy().dtype
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assert type1 == 'float32'
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_cast13():
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x0 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.uint8))
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t0 = mstype.int32
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x1 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.uint8))
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t1 = mstype.float16
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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net = Net(t0, t1)
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output = net(x0, x1)
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type0 = output[0].asnumpy().dtype
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assert type0 == 'int32'
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type1 = output[1].asnumpy().dtype
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assert type1 == 'float16'
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_cast14():
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x0 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.int16))
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t0 = mstype.float64
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x1 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.int16))
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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(t0, t1)
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output = net(x0, x1)
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type0 = output[0].asnumpy().dtype
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assert type0 == 'float64'
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type1 = output[1].asnumpy().dtype
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assert type1 == 'float32'
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_cast15():
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x0 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.int16))
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t0 = mstype.float16
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x1 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.int16))
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t1 = mstype.int32
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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net = Net(t0, t1)
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output = net(x0, x1)
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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 == 'int32'
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_cast16():
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x0 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.int16))
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t0 = mstype.float16
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x1 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.int64))
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t1 = mstype.float64
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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net = Net(t0, t1)
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output = net(x0, x1)
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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 == 'float64'
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_cast17():
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x0 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.int16))
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t0 = mstype.float32
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x1 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.int16))
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t1 = mstype.float16
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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net = Net(t0, t1)
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output = net(x0, x1)
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type0 = output[0].asnumpy().dtype
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assert type0 == 'float32'
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type1 = output[1].asnumpy().dtype
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assert type1 == 'float16'
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_cast18():
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x0 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.int64))
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t0 = mstype.float32
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x1 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.int64))
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t1 = mstype.float16
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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net = Net(t0, t1)
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output = net(x0, x1)
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type0 = output[0].asnumpy().dtype
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assert type0 == 'float32'
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type1 = output[1].asnumpy().dtype
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assert type1 == 'float16'
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_cast19():
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x0 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.int8))
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t0 = mstype.bool_
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x1 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.int16))
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t1 = mstype.bool_
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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net = Net(t0, t1)
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output = net(x0, x1)
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type0 = output[0].asnumpy().dtype
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assert type0 == 'bool'
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type1 = output[1].asnumpy().dtype
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assert type1 == 'bool'
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_cast20():
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x0 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.int64))
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t0 = mstype.bool_
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x1 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.float16))
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t1 = mstype.bool_
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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net = Net(t0, t1)
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output = net(x0, x1)
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type0 = output[0].asnumpy().dtype
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assert type0 == 'bool'
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type1 = output[1].asnumpy().dtype
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assert type1 == 'bool'
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_cast21():
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x0 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.float32))
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t0 = mstype.bool_
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x1 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.float64))
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t1 = mstype.bool_
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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net = Net(t0, t1)
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output = net(x0, x1)
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type0 = output[0].asnumpy().dtype
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assert type0 == 'bool'
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type1 = output[1].asnumpy().dtype
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assert type1 == 'bool'
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_cast22():
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x0 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.uint8))
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t0 = mstype.bool_
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x1 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.int32))
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t1 = mstype.bool_
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|
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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net = Net(t0, t1)
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output = net(x0, x1)
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type0 = output[0].asnumpy().dtype
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assert type0 == 'bool'
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type1 = output[1].asnumpy().dtype
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assert type1 == 'bool'
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|
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@pytest.mark.level1
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|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_cast23():
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x0 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.float64))
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t0 = mstype.float32
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x1 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.float64))
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t1 = mstype.float16
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|
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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net = Net(t0, t1)
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output = net(x0, x1)
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type0 = output[0].asnumpy().dtype
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assert type0 == 'float32'
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|
type1 = output[1].asnumpy().dtype
|
|
assert type1 == 'float16'
|
|
|
|
@pytest.mark.level1
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|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_cast24():
|
|
x0 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.float64))
|
|
t0 = mstype.int64
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|
x1 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.float64))
|
|
t1 = mstype.int32
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
|
|
net = Net(t0, t1)
|
|
output = net(x0, x1)
|
|
type0 = output[0].asnumpy().dtype
|
|
assert type0 == 'int64'
|
|
type1 = output[1].asnumpy().dtype
|
|
assert type1 == 'int32'
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_cast25():
|
|
x0 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.float64))
|
|
t0 = mstype.int16
|
|
x1 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.float64))
|
|
t1 = mstype.int8
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
|
|
net = Net(t0, t1)
|
|
output = net(x0, x1)
|
|
type0 = output[0].asnumpy().dtype
|
|
assert type0 == 'int16'
|
|
type1 = output[1].asnumpy().dtype
|
|
assert type1 == 'int8'
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_cast26():
|
|
x0 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.int32))
|
|
t0 = mstype.int64
|
|
x1 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.int32))
|
|
t1 = mstype.float64
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
|
|
net = Net(t0, t1)
|
|
output = net(x0, x1)
|
|
type0 = output[0].asnumpy().dtype
|
|
assert type0 == 'int64'
|
|
type1 = output[1].asnumpy().dtype
|
|
assert type1 == 'float64'
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_cast27():
|
|
x0 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.float32))
|
|
t0 = mstype.float64
|
|
x1 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.float32))
|
|
t1 = mstype.uint64
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
|
|
net = Net(t0, t1)
|
|
output = net(x0, x1)
|
|
type0 = output[0].asnumpy().dtype
|
|
assert type0 == 'float64'
|
|
type1 = output[1].asnumpy().dtype
|
|
assert type1 == 'uint64'
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_cast28():
|
|
x0 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.float32))
|
|
t0 = mstype.int8
|
|
x1 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.float32))
|
|
t1 = mstype.int16
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
|
|
net = Net(t0, t1)
|
|
output = net(x0, x1)
|
|
type0 = output[0].asnumpy().dtype
|
|
assert type0 == 'int8'
|
|
type1 = output[1].asnumpy().dtype
|
|
assert type1 == 'int16'
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_cast29():
|
|
x0 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.float32))
|
|
t0 = mstype.int64
|
|
x1 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.float32))
|
|
t1 = mstype.uint8
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
|
|
net = Net(t0, t1)
|
|
output = net(x0, x1)
|
|
type0 = output[0].asnumpy().dtype
|
|
assert type0 == 'int64'
|
|
type1 = output[1].asnumpy().dtype
|
|
assert type1 == 'uint8'
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_cast30():
|
|
x0 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.float32))
|
|
t0 = mstype.uint16
|
|
x1 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.float32))
|
|
t1 = mstype.uint32
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
|
|
net = Net(t0, t1)
|
|
output = net(x0, x1)
|
|
type0 = output[0].asnumpy().dtype
|
|
assert type0 == 'uint16'
|
|
type1 = output[1].asnumpy().dtype
|
|
assert type1 == 'uint32'
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_cast31():
|
|
x0 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.float32))
|
|
t0 = mstype.uint16
|
|
x1 = Tensor(np.arange(24).reshape((4, 3, 2)).astype(np.float32))
|
|
t1 = mstype.uint32
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
|
|
net = NetDynamic(t0, t1)
|
|
output = net(x0, x1)
|
|
type0 = output[0].asnumpy().dtype
|
|
assert type0 == 'uint16'
|
|
type1 = output[1].asnumpy().dtype
|
|
assert type1 == 'uint32'
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_cast32():
|
|
np.random.seed(10)
|
|
x = np.random.uniform(-5, 5, (3, 2)).astype(np.float16)
|
|
x0 = Tensor(x)
|
|
t0 = mstype.int32
|
|
x1 = Tensor(x)
|
|
t1 = mstype.float64
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
|
|
net = Net(t0, t1)
|
|
output = net(x0, x1)
|
|
type0 = output[0].asnumpy().dtype
|
|
assert type0 == 'int32'
|
|
expected = x.astype(np.int32)
|
|
assert (output[0].asnumpy() == expected).all()
|
|
type1 = output[1].asnumpy().dtype
|
|
assert type1 == 'float64'
|