pytorch tensor 排序 pytorch sparse tensor

Pytorch 构造稀疏 Tensor

torch.sparse_coo_tensor(indices, values,
					    size=None, *,
					    dtype=None,
					    device=None,
					    requires_grad=False)

Constructs a sparse tensor in COO(rdinate) format with specified values at the given indices.
This function returns an uncoalesced tensor.

1. COO Style Matrix

COO 格式的矩阵可以通过一个三元组表示，如上图右侧所示，从上到下依次为：

• ① 矩阵非零元素所在的行；
• ② 矩阵非零元素所在的列（与行位置匹配）；
• ③ 矩阵非零元素对应的值。

有了该基础接下来再看 sparse_coo_tensor()

2. sparse_coo_tensor()示例

接下来看一下参数说明，知道了COO格式，下面可以先略过。

Parameters

• indices (array_like) – Initial data for the tensor. Can be a list, tuple, NumPy ndarray, scalar, and other types. Will be cast to a torch.LongTensor internally. The indices are the coordinates of the non-zero values in the matrix, and thus should be two-dimensional where the first dimension is the number of tensor dimensions and the second dimension is the number of non-zero values.
• values (array_like) – Initial values for the tensor. Can be a list, tuple, NumPy ndarray, scalar, and other types.
• size (list, tuple, or torch.Size, optional) – Size of the sparse tensor. If not provided the size will be inferred as the minimum size big enough to hold all non-zero elements.

Keyword Arguments

• dtype (torch.dtype, optional) – the desired data type of returned tensor. Default: if None, infers data type from values.
• device (torch.device, optional) – the desired device of returned tensor. Default: if None, uses the current device for the default tensor type (see torch.set_default_tensor_type()). device will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types.
• requires_grad (bool, optional) – If autograd should record operations on the returned tensor. Default: False.

# setting valid pixel location
indice = torch.tensor([[0, 1, 1],
                       [2, 0, 2]])
# setting match values
values = torch.tensor([3, 4, 5], dtype=torch.float32)
print('COO tensor: \n{}'.format(torch.sparse_coo_tensor(indice, values, [2, 4])))
print('COO tensor: \n{}'.format(torch.sparse_coo_tensor(indice, values)))   # inferred as the minimum size
print('COO tensor: \n{}'.format(torch.sparse_coo_tensor(indice, values, [2, 4], dtype=torch.float64)))
# Create a empty sparse tensor.
print('COO tensor: \n{}'.format(torch.sparse_coo_tensor(torch.empty([1, 0]), [], [1])))
print('COO tensor: \n{}'.format(torch.sparse_coo_tensor(torch.empty([1, 0]), torch.empty([0, 2]), [1, 2])))

测试结果：

COO tensor: 
tensor(indices=tensor([[0, 1, 1],
                       [2, 0, 2]]),
       values=tensor([3., 4., 5.]),
       size=(2, 4), nnz=3, dtype=torch.float32, layout=torch.sparse_coo)
COO tensor: 
tensor(indices=tensor([[0, 1, 1],
                       [2, 0, 2]]),
       values=tensor([3., 4., 5.]),
       size=(2, 3), nnz=3, dtype=torch.float32, layout=torch.sparse_coo)
COO tensor: 
tensor(indices=tensor([[0, 1, 1],
                       [2, 0, 2]]),
       values=tensor([3., 4., 5.]),
       size=(2, 4), nnz=3, layout=torch.sparse_coo)
COO tensor: 
tensor(indices=tensor([], size=(1, 0)),
       values=tensor([], size=(0,)),
       size=(1,), nnz=0, layout=torch.sparse_coo)
COO tensor: 
tensor(indices=tensor([], size=(1, 0)),
       values=tensor([], size=(0, 2)),
       size=(1, 2), nnz=0, layout=torch.sparse_coo)