mask_as¶

paddle.sparse. mask_as ( x: Tensor, mask: Tensor, name: str | None = None ) → Tensor [source]

Filter the input dense tensor x using the indices of the sparse matrix mask, which in turn generates a sparse matrix of the corresponding format. The input x and mask must have the same shape, and the sparse tensor returned has the same indices as mask even zero values exist in the corresponding indices.

Parameters

• x (Tensor) – The input tensor. It should be a DenseTensor. The data type can be float32, float64, int32, int64, complex64, complex128, int8, int16, float16.

• mask (Tensor) – The input tensor. It can be SparseCooTensor or SparseCsrTensor. It should be 2D or 3D when the mask is SparseCsrTensor.

• name (str, optional) – Name for the operation (optional, default is None). For more information, please refer to Name.

Returns

A sparse tensor.

Return type

Tensor

Examples

>>> import paddle
>>> paddle.set_device('cpu')

>>> # csr sparse tensor
>>> crows = [0, 2, 3, 5]
>>> cols = [1, 3, 2, 0, 1]
>>> values = [1., 2., 3., 4., 5.]
>>> dense_shape = [3, 4]
>>> csr = paddle.sparse.sparse_csr_tensor(crows, cols, values, dense_shape)
>>> paddle.seed(2024)
>>> x = paddle.rand(dense_shape).astype(csr.dtype)
>>> out = paddle.sparse.mask_as(x, csr)
>>> print(out)
Tensor(shape=[3, 4], dtype=paddle.float32, place=Place(cpu), stop_gradient=True,
crows=[0, 2, 3, 5],
cols=[1, 3, 2, 0, 1],
values=[0.23659813, 0.08467803, 0.64152628, 0.66596609, 0.90394485])

>>> # coo sparse tensor
>>> indices = [[0, 1, 2], [1, 2, 0]]
>>> values = [1.0, 2.0, 3.0]
>>> dense_shape = [3, 3]
>>> coo = paddle.sparse.sparse_coo_tensor(indices, values, dense_shape)
>>> paddle.seed(2024)
>>> x = paddle.rand(dense_shape).astype(coo.dtype)
>>> out = paddle.sparse.mask_as(x, coo)
>>> print(out)
Tensor(shape=[3, 3], dtype=paddle.float32, place=Place(cpu), stop_gradient=True,
indices=[[0, 1, 2],
         [1, 2, 0]],
values=[0.23659813, 0.40340215, 0.64152628])