forked from mindspore-Ecosystem/mindspore
46 lines
1.6 KiB
Python
46 lines
1.6 KiB
Python
# 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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import numpy as np
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from mindspore import context, nn, Tensor, Parameter
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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, save_graphs=False)
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class Net(nn.Cell):
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def __init__(self, data):
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super(Net, self).__init__()
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self.start = Tensor(0, dtype=mstype.int32)
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self.end = Tensor(2, dtype=mstype.int32)
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self.max_output = Parameter(data, "output_x")
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self.upd = P.ScatterNdUpdate()
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self.zero = Tensor(np.ones([1], dtype=np.int32))
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def construct(self, inputs):
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idx = self.start
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end = self.end
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while idx < end:
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xi = inputs[idx, :, :]
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self.upd(self.max_output, idx + self.zero, xi)
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idx = idx + 1
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return self.max_output + 0
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def test_x():
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x = Tensor(np.arange(10 * 2 * 3).reshape(10, 2, 3).astype(np.float32))
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net = Net(x)
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net(x)
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