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Revert D14605905: [pytorch][PR] Add return_counts to torch.unique

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Differential Revision:
D14605905

Original commit changeset: 555f5a12a8e2

fbshipit-source-id: c7874f5987893e956c022180a37763d88bba38db
This commit is contained in:
Soumith Chintala
2019-03-26 17:14:26 -07:00
committed by Facebook Github Bot
parent bdd098c694
commit 66628f78b7
9 changed files with 68 additions and 214 deletions
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32
torch/functional.py
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@ -374,8 +374,8 @@ def stft(input, n_fft, hop_length=None, win_length=None, window=None,
return torch._C._VariableFunctions.stft(input, n_fft, hop_length, win_length, window, normalized, onesided)
def unique(input, sorted=True, return_inverse=False, return_counts=False, dim=None):
r"""Returns the unique elements of the input tensor.
def unique(input, sorted=True, return_inverse=False, dim=None):
r"""Returns the unique scalar elements of the input tensor as a 1-D tensor.
Arguments:
input (Tensor): the input tensor
@ -383,26 +383,18 @@ def unique(input, sorted=True, return_inverse=False, return_counts=False, dim=No
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.
dim (int): the dimension to apply unique. If ``None``, the unique of the
flattened input is returned. default: ``None``
Returns:
(Tensor, Tensor (optional) Tensor (optional)):
A tensor or a tuple of tensors containing
(Tensor, Tensor (optional)): A tensor or a tuple of tensors containing
- **output** (*Tensor*): the output list of unique scalar elements.
- **inverse_indices** (*Tensor*): (optional) if
:attr:`return_inverse` is True, there will be an additional
returned tensor (same shape as input) representing the indices
:attr:`return_inverse` is True, there will be a
2nd 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
:attr:`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 occurences
for each unique value or tensor.
Example::
@ -427,26 +419,20 @@ def unique(input, sorted=True, return_inverse=False, return_counts=False, dim=No
"""
if dim is not None:
output, inverse_indices, counts = torch._unique_dim(
output, inverse_indices = torch._unique_dim(
input,
dim,
sorted=sorted,
return_inverse=return_inverse,
return_counts=return_counts
return_inverse=return_inverse
)
else:
output, inverse_indices, counts = torch._unique(
output, inverse_indices = torch._unique(
input,
sorted=sorted,
return_inverse=return_inverse,
return_counts=return_counts
)
if return_inverse and return_counts:
return output, inverse_indices, counts
elif return_inverse:
if return_inverse:
return output, inverse_indices
elif return_counts:
return output, counts
else:
return output