oneflow.unique

oneflow.unique(input, sorted=True, return_inverse=False, return_counts=False, dtype=oneflow.int32)

Returns the unique elements of the input tensor.

The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.unique.html.

Parameters

  • input (Tensor) – The input tensor.

  • sorted (bool) – Whether to sort the unique elements in ascending order before returning as output.

  • return_inverse (bool) – Whether to also return the indices for where elements in the original input ended up in the returned unique list.

  • return_counts (bool) – Whether to also return the counts for each unique element.

  • dtype (flow.dtype) – Dtype of the returned indices and counts.

Returns

  • output (Tensor): the output list of unique scalar elements.

  • inverse_indices (Tensor): (optional) if return_inverse is True, there will be an additional returned tensor (same shape as input) representing the indices for where elements in the original input map to in the output; otherwise, this function will only return a single tensor.

  • counts (Tensor): (optional) if return_counts is True, there will be an additional returned tensor (same shape as output or output.size(dim), if dim was specified) representing the number of occurrences for each unique value or tensor.

Return type

oneflow.Tensor or List of oneflow.Tensor

For example:

>>> import oneflow as flow
>>> x = flow.tensor([3, 1, 2, 0 ,2])
>>> flow.unique(x)
tensor([0, 1, 2, 3], dtype=oneflow.int64)
>>> flow.unique(x, sorted=False)
tensor([3, 1, 2, 0], dtype=oneflow.int64)
>>> results, indices = flow.unique(x, return_inverse=True)
>>> indices
tensor([3, 1, 2, 0, 2], dtype=oneflow.int32)
>>> results, counts = flow.unique(x, return_counts=True)
>>> counts
tensor([1, 1, 2, 1], dtype=oneflow.int32)
>>> results, indices = flow.unique(x, return_inverse=True, dtype=flow.long)
>>> indices
tensor([3, 1, 2, 0, 2], dtype=oneflow.int64)