forked from mindspore-Ecosystem/mindspore
51 lines
1.8 KiB
Python
51 lines
1.8 KiB
Python
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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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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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from mindspore import Tensor
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from mindspore.ops import operations as P
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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class Net(nn.Cell):
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def __init__(self, _shape):
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super(Net, self).__init__()
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self.shape = _shape
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self.scatternd = P.ScatterNd()
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def construct(self, indices, update):
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return self.scatternd(indices, update, self.shape)
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def scatternd_net(indices, update, _shape, expect):
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scatternd = Net(_shape)
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output = scatternd(Tensor(indices), Tensor(update))
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error = np.ones(shape=output.asnumpy().shape) * 1.0e-6
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diff = output.asnumpy() - expect
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assert np.all(diff < error)
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assert np.all(-diff < error)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_traning
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@pytest.mark.env_onecard
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def test_scatternd():
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arr_indices = np.array([[0, 1], [1, 1]]).astype(np.int32)
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arr_update = np.array([3.2, 1.1]).astype(np.float32)
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shape = (2, 2)
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expect = np.array([[0., 3.2],
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[0., 1.1]])
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scatternd_net(arr_indices, arr_update, shape, expect)
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