# 比较与torch.sparse_coo_tensor的功能差异

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## torch.sparse_coo_tensor

```python
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](https://pytorch.org/docs/1.5.0/torch.html#torch.sparse_coo_tensor)。

## mindspore.SparseTensor

```python
class mindspore.SparseTensor(
  indices,
  values,
  dense_shape
)
```

更多内容详见[mindspore.SparseTensor](https://mindspore.cn/docs/zh-CN/r1.8/api_python/mindspore/mindspore.SparseTensor.html#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

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

indices = ms.Tensor([[0, 1], [1, 2]])
values = ms.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)
```