Wei Luning
5 years ago
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# 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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"""
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test assign sub
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"""
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import numpy as np
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import pytest
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import mindspore.context as context
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import mindspore.nn as nn
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import mindspore.ops.operations as P
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from mindspore import Tensor
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from mindspore.common.initializer import initializer
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from mindspore.common.parameter import Parameter
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import mindspore as ms
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class AssignW(nn.Cell):
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def __init__(self):
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super(AssignW, self).__init__()
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self.assign = P.Assign()
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def construct(self, x, w):
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self.assign(x, w)
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return x
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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.b = Parameter(initializer('ones', [5]), name='b')
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self.assign = AssignW()
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def construct(self, value):
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return self.assign(self.b, value)
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def test_assign_through_cell():
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context.set_context(mode=context.GRAPH_MODE, save_graphs=True)
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net = Net()
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net.to_float(ms.float16)
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net.add_flags_recursive(fp16=False)
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input_data = Tensor(np.ones([5]).astype(np.float32))
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net(input_data)
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with pytest.raises(TypeError):
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net(None)
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class NetScatterNdUpdate(nn.Cell):
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def __init__(self):
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super(NetScatterNdUpdate, self).__init__()
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self.b = Parameter(initializer('ones', [5, 5]), name='b')
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self.scatter = P.ScatterNdUpdate()
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def construct(self, idx, x):
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return self.scatter(self.b, idx, x)
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def test_scatter_nd_update():
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context.set_context(mode=context.GRAPH_MODE)
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net = NetScatterNdUpdate()
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x = Tensor(np.ones([5]).astype(np.float16))
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idx = Tensor(np.ones([1]).astype(np.int32))
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net(idx, x)
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