Source code for mindspore.nn.sparse.sparse

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"""Sparse related tools."""
from __future__ import absolute_import

from mindspore.ops import operations as P
from ..cell import Cell


[文档]class SparseToDense(Cell): """ Converts a sparse tensor(COOTensor) into dense. In Python, for the ease of use, three tensors are collected into a SparseTensor class. MindSpore uses three independent dense tensors: indices, value and dense shape to represent the sparse tensor. Separate indexes, values and dense shape tensors can be wrapped in a Sparse Tensor object before being passed to the OPS below. Inputs: - **coo_tensor** (:class:`mindspore.COOTensor`) - the sparse COOTensor to convert. Outputs: Tensor, converted from sparse tensor. Raises: TypeError: If `sparse_tensor.indices` is not a Tensor. TypeError: If `sparse_tensor.values` is not a Tensor. TypeError: If `sparse_tensor.shape` is not a tuple. Supported Platforms: ``CPU`` Examples: >>> import mindspore as ms >>> from mindspore import Tensor, COOTensor >>> import mindspore.nn as nn >>> ms.set_context(mode=ms.PYNATIVE_MODE) >>> indices = Tensor([[0, 1], [1, 2]]) >>> values = Tensor([1, 2], dtype=ms.int32) >>> dense_shape = (3, 4) >>> class Net(nn.Cell): ... def __init__(self, dense_shape): ... super(Net, self).__init__() ... self.dense_shape = dense_shape ... self.op = nn.SparseToDense() ... ... def construct(self, indices, values): ... x = COOTensor(indices, values, self.dense_shape) ... return self.op(x) ... >>> print(Net(dense_shape)(indices, values)) [[0 1 0 0] [0 0 2 0] [0 0 0 0]] """ def __init__(self): """Initialize SparseToDense.""" super(SparseToDense, self).__init__() self.sparse_to_dense = P.SparseToDense() def construct(self, sparse_tensor): return self.sparse_to_dense(sparse_tensor.indices, sparse_tensor.values, sparse_tensor.shape)
[文档]class SparseTensorDenseMatmul(Cell): """ Multiplies sparse matrix `a` and dense matrix `b`. The rank of sparse matrix and dense matrix must be equal to `2`. Args: adjoint_st (bool): If true, sparse tensor is transposed before multiplication. Default: False. adjoint_dt (bool): If true, dense tensor is transposed before multiplication. Default: False. Inputs: - **indices** (Tensor) - A 2-D Tensor, represents the position of the element in the sparse tensor. Support int32, int64, each element value should be non-negative. The shape is :math:`(n, 2)`. - **values** (Tensor) - A 1-D Tensor, represents the value corresponding to the position in the `indices`. Support float16, float32, float64, int32, int64. The shape should be :math:`(n,)`. - **sparse_shape** (tuple) - A positive int tuple which specifies the shape of sparse tensor, should have 2 elements, represent sparse tensor shape is :math:`(N, C)`. - **dense** (Tensor) - A 2-D Tensor, the dtype is same as `values`. If `adjoint_st` is False and `adjoint_dt` is False, the shape must be :math:`(C, M)`. If `adjoint_st` is False and `adjoint_dt` is True, the shape must be :math:`(M, C)`. If `adjoint_st` is True and `adjoint_dt` is False, the shape must be :math:`(N, M)`. If `adjoint_st` is True and `adjoint_dt` is True, the shape must be :math:`(M, N)`. Outputs: Tensor, the dtype is the same as `values`. If `adjoint_st` is False, the shape is :math:`(N, M)`. If `adjoint_st` is True, the shape is :math:`(C, M)`. Raises: TypeError: If the type of `adjoint_st` or `adjoint_dt` is not bool, or the dtype of `indices`, dtype of `values` and dtype of `dense` don't meet the parameter description. ValueError: If `sparse_shape`, shape of `indices`, shape of `values`, and shape of `dense` don't meet the parameter description. Supported Platforms: ``CPU`` Examples: >>> import mindspore as ms >>> from mindspore import Tensor >>> from mindspore import nn >>> indices = Tensor([[0, 1], [1, 2]], dtype=ms.int32) >>> values = Tensor([1, 2], dtype=ms.float32) >>> sparse_shape = (3, 4) >>> dense = Tensor([[1, 1], [2, 2], [3, 3], [4, 4]], dtype=ms.float32) >>> sparse_dense_matmul = nn.SparseTensorDenseMatmul() >>> out = sparse_dense_matmul(indices, values, sparse_shape, dense) >>> print(out) [[2. 2.] [6. 6.] [0. 0.]] """ def __init__(self, adjoint_st=False, adjoint_dt=False): """Initialize SparseTensorDenseMatmul""" super(SparseTensorDenseMatmul, self).__init__() self.adj_st = adjoint_st self.adj_dt = adjoint_dt self.sparse_dense_matmul = P.SparseTensorDenseMatmul(adjoint_st=self.adj_st, adjoint_dt=self.adj_dt) def construct(self, indices, values, sparse_shape, dense): return self.sparse_dense_matmul(indices, values, sparse_shape, dense)