# 比较与torch.Tensor.t的功能差异

<a href="https://gitee.com/mindspore/docs/blob/r2.0.0-alpha/docs/mindspore/source_zh_cn/note/api_mapping/pytorch_diff/Transpose.md" target="_blank"><img src="https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/r2.0.0-alpha/resource/_static/logo_source.png"></a>

## torch.Tensor.t

```python
torch.Tensor.t(input)
```

更多内容详见[torch.Tensor.t](https://pytorch.org/docs/1.5.0/tensors.html#torch.Tensor.t)。

## mindspore.ops.Transpose

```python
class mindspore.ops.Transpose(*args, **kwargs)(
    input_x,
    input_perm
)
```

更多内容详见[mindspore.ops.Transpose](https://mindspore.cn/docs/zh-CN/r2.0.0-alpha/api_python/ops/mindspore.ops.Transpose.html#mindspore.ops.Transpose)。

## 使用方式

PyTorch：仅适用于一维和二维的输入。

MindSpore：输入的维度不限，且需要通过参数设置转置方式。

## 代码示例

```python
import mindspore as ms
import mindspore.ops as ops
import torch
import numpy as np

# In MindSpore, the input tensor will be transposed based on the dimension you set.
input_tensor = ms.Tensor(np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]), ms.float32)
perm = (0, 2, 1)
transpose = ops.Transpose()
output = transpose(input_tensor, perm)
print(output.shape)
# Out：
# (2, 3, 2)

# In torch, only input of 2D dimension or lower will be accepted.
input1 = torch.randn(())
input2 = torch.randn((2, 3))
input3 = torch.randn((2, 3, 4))
for n, x in enumerate([input1, input2, input3]):
    try:
        output = torch.t(x)
        print(output.shape)
    except Exception as e:
        print('ERROR when inputting {}D: '.format(n + 1) + str(e))
# Out：
# torch.Size([])
# torch.Size([3, 2])
# ERROR when inputting 3D: t() expects a tensor with <=2 dimensions, but self is 3D.
```