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- # Copyright 2020 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
- from mindspore import context, nn, Tensor, Parameter
- from mindspore.common import dtype as mstype
- from mindspore.ops import operations as P
-
-
- context.set_context(mode=context.GRAPH_MODE, save_graphs=False)
-
- class Net(nn.Cell):
- def __init__(self, data):
- super(Net, self).__init__()
- self.start = Tensor(0, dtype=mstype.int32)
- self.end = Tensor(2, dtype=mstype.int32)
- self.max_output = Parameter(data, "output_x")
- self.upd = P.ScatterNdUpdate()
- self.zero = Tensor(np.ones([1], dtype=np.int32))
-
- def construct(self, inputs):
- idx = self.start
- end = self.end
- while idx < end:
- xi = inputs[idx, :, :]
- self.upd(self.max_output, idx + self.zero, xi)
- idx = idx + 1
- return self.max_output + 0
-
-
- def test_x():
- x = Tensor(np.arange(10 * 2 * 3).reshape(10, 2, 3).astype(np.float32))
- net = Net(x)
- net(x)
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