Function Differences with torch.sparse_coo_tensor
torch.sparse_coo_tensor
torch.sparse_coo_tensor(
indices,
values,
size=None,
dtype=None,
device=None,
requires_grad=False
)
For more information, see torch.sparse_coo_tensor.
mindspore.SparseTensor
class mindspore.SparseTensor(
indices,
values,
dense_shape
)
For more information, see mindspore.SparseTensor.
Differences
PyTorch: Constructs a sparse tensors in COO(rdinate)
format.
MindSpore:Constructs a sparse tensors. It can only be used in the Cell
construct method. PyNative mode does not support sparse tensor.
Code Example
# 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)