Function Differences with torch.Tensor.t

torch.Tensor.t

torch.Tensor.t(input)

For more information, see torch.Tensor.t.

mindspore.ops.Transpose

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

For more information, see mindspore.ops.Transpose.

Differences

PyTorch: Only applies to a 1D or 2D input.

MindSpore: No limit for dimension of the input, and how to transpose should be set by relevant parameters.

Code Example

import mindspore
from mindspore import Tensor
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 = Tensor(np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]), mindspore.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.