- torch.unique_consecutive(*args, **kwargs)¶
Eliminates all but the first element from every consecutive group of equivalent elements.
This function is different from
torch.unique()in the sense that this function only eliminates consecutive duplicate values. This semantics is similar to std::unique in C++.
input (Tensor) – the input tensor
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.
dim (int) – the dimension to apply unique. If
None, the unique of the flattened input is returned. default:
A tensor or a tuple of tensors containing
output (Tensor): the output list of unique scalar elements.
inverse_indices (Tensor): (optional) if
return_inverseis 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_countsis 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:
>>> x = torch.tensor([1, 1, 2, 2, 3, 1, 1, 2]) >>> output = torch.unique_consecutive(x) >>> output tensor([1, 2, 3, 1, 2]) >>> output, inverse_indices = torch.unique_consecutive(x, return_inverse=True) >>> output tensor([1, 2, 3, 1, 2]) >>> inverse_indices tensor([0, 0, 1, 1, 2, 3, 3, 4]) >>> output, counts = torch.unique_consecutive(x, return_counts=True) >>> output tensor([1, 2, 3, 1, 2]) >>> counts tensor([2, 2, 1, 2, 1])