forked from mindspore-Ecosystem/mindspore
62 lines
2.2 KiB
Python
62 lines
2.2 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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"""
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@File : test_sparse_tensor.py
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@Author:
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@Date : 2020-07-16
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@Desc : test mindspore sparse_tensor's operation
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"""
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import mindspore as ms
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import mindspore.nn as nn
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from mindspore.ops import composite as C
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from mindspore import Tensor, SparseTensor, context
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context.set_context(mode=context.GRAPH_MODE, enable_sparse=True)
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def test_sparse_tensor_make_sparse_tensor():
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class MakeSparseTensor(nn.Cell):
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def __init__(self):
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super(MakeSparseTensor, self).__init__()
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self.dense_shape = (3, 4)
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def construct(self, indices, values):
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ret = (SparseTensor(indices, values, self.dense_shape),)
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return ret[0]
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indices = Tensor([[0, 1], [1, 2]])
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values = Tensor([1, 2], dtype=ms.float32)
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MakeSparseTensor()(indices, values)
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def test_sparse_tensor_attr():
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grad_op = C.GradOperation('get_all', get_all=True)
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class GradWrap(nn.Cell):
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def __init__(self, network):
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super(GradWrap, self).__init__()
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self.network = network
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def construct(self, input1, input2):
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gout = grad_op(self.network)(input1, input2)
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return gout
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class SparseTensorGetAttr(nn.Cell):
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def __init__(self):
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super(SparseTensorGetAttr, self).__init__()
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self.dense_shape = (3, 4)
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def construct(self, indices, values):
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x = SparseTensor(indices, values, self.dense_shape)
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return x.values(), x.indices(), x.dense_shape()
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indices = Tensor([[0, 1], [1, 2]])
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values = Tensor([1, 2], dtype=ms.float32)
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SparseTensorGetAttr()(indices, values)
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