Differences with torch.unique

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torch.unique

torch.unique(
    *args,
    **kwargs
)

For more information, see torch.unique.

mindspore.ops.unique

mindspore.ops.unique(input)

For more information, see mindspore.ops.unique.

Differences

PyTorch: Deduplicate the elements in the Tensor. The parameter sorted can be set to determine whether the output is sorted in ascending order. Set the parameter return_inverse to determine whether to output the index of each element of the input Tensor in the output Tensor. Set the parameter return_counts to determine whether to output the number of each unique value in the input Tensor; set the parameter dim to specify the dimension of the unique. MindSpore does not support these functions.

MindSpore: Deduplicate the elements in the Tensor, as well as return the position index of each element of the input Tensor in the output Tensor.

Categories

Subcategories

PyTorch

MindSpore

Difference

Parameters

Parameter1

input

input

Consistent

Parameter2

sorted

-

When sorted is True, the output Tensor is sorted in ascending order; when sorted is False, it is sorted in the original order

Parameter3

return_inverse

-

When return_inverse is True, the index position of each element of the input Tensor in the output Tensor is returned

Parameter4

return_counts

-

When return_counts is True, the number of each element of the output Tensor in the input Tensor is returned

Parameter5

dim

-

Specify the dimension of unique

Code Example

# In MindSpore
import mindspore

x = mindspore.Tensor([1, 3, 2, 3], mindspore.float32)
output, idx = mindspore.ops.unique(x)
print(output)
# [1. 3. 2.]
print(idx)
# [0 1 2 1]

# In PyTorch
import torch

output, inverse_indices, counts = torch.unique(torch.tensor([1, 3, 2, 3], dtype=torch.long), sorted=True, return_inverse=True, return_counts=True)
print(output)
# tensor([1, 2, 3])
print(inverse_indices)
# tensor([0, 2, 1, 2])
print(counts)
# tensor([1, 1, 2])

# Example of using unique with dim
output, inverse_indices = torch.unique(torch.tensor([[3, 1], [1, 2]], dtype=torch.long), sorted=True, return_inverse=True, dim=0)
print(output)
# tensor([[1, 2],
#         [3, 1]])
print(inverse_indices)
# tensor([1, 0])