比较与torch.sparse_coo_tensor的功能差异

torch.sparse_coo_tensor

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

更多内容详见torch.sparse_coo_tensor。

mindspore.SparseTensor

class mindspore.SparseTensor(
  indices,
  values,
  dense_shape
)

更多内容详见mindspore.SparseTensor。

使用方式

PyTorch：以COO(rdinate)格式构造一个稀疏张量。

MindSpore：构造稀疏张量，只能在Cell的构造方法中使用，PyNative模式暂不支持稀疏张量。

代码示例

# In MindSpore：
import mindspore as ms
import mindspore.nn as nn
from mindspore import Tensor
from mindspore import SparseTensor
class Net(nn.Cell):
    def __init__(self, dense_shape):
        super(Net, self).__init__()
        self.dense_shape = dense_shape
    def construct(self, indices, values):
        x = SparseTensor(indices, values, self.dense_shape)
        return x.indices, x.values, x.dense_shape

indices = Tensor([[0, 1], [1, 2]])
values = Tensor([1, 2], dtype=ms.float32)
out = Net((3, 4))(indices, values)
print(out[0])
print(out[1])
print(out[2])
# Linux Out:
# [[0 1]
#  [1 2]]
# [1. 2.]
# (3, 4)
# Windows Out:
# [[0 1]
#  [1 2]]
# [1. 2.]
# (Tensor(shape=[], dtype=Int64, value= 3), Tensor(shape=[], dtype=Int64, value= 4))

# In torch:
import torch
i = torch.tensor([[0, 1],
                  [1, 2]])
v = torch.tensor([1, 2], dtype=torch.float32)
out = torch.sparse_coo_tensor(i, v, [3, 4])
print(out)
# Out:
# tensor(indices=tensor([[0, 1],
#                       [1, 2]]),
#        values=tensor([1., 2.]),
#        size=(3, 4), nnz=2, layout=torch.sparse_coo)