def refine_names(self, *names):
r"""Refines the dimension names of :attr:`self` according to :attr:`names`.
Refining is a special case of renaming that "lifts" unnamed dimensions.
A ``None`` dim can be refined to have any name; a named dim can only be
refined to have the same name.
Because named tensors can coexist with unnamed tensors, refining names
gives a nice way to write named-tensor-aware code that works with both
named and unnamed tensors.
:attr:`names` may contain up to one Ellipsis (``...``).
The Ellipsis is expanded greedily; it is expanded in-place to fill
:attr:`names` to the same length as ``self.dim()`` using names from the
corresponding indices of ``self.names``.
Python 2 does not support Ellipsis but one may use a string literal
instead (``'...'``).
Arguments:
names (iterable of str): The desired names of the output tensor. May
contain up to one Ellipsis.
Examples::
>>> imgs = torch.randn(32, 3, 128, 128)
>>> named_imgs = imgs.refine_names('N', 'C', 'H', 'W')
>>> named_imgs.names
('N', 'C', 'H', 'W')
>>> tensor = torch.randn(2, 3, 5, 7, 11)
>>> tensor = tensor.refine_names('A', ..., 'B', 'C')
>>> tensor.names
('A', None, None, 'B', 'C')
.. warning::
The named tensor API is experimental and subject to change.
"""
relevant_args = (self, )
from torch.overrides import has_torch_function, handle_torch_function
if type(self) is not Tensor and has_torch_function(relevant_args):
return handle_torch_function(Tensor.refine_names, relevant_args,
self, *names)
names = resolve_ellipsis(names, self.names, 'refine_names')
return super(Tensor, self).refine_names(names)
def align_to(self, *names):
r"""Permutes the dimensions of the :attr:`self` tensor to match the order
specified in :attr:`names`, adding size-one dims for any new names.
All of the dims of :attr:`self` must be named in order to use this method.
The resulting tensor is a view on the original tensor.
All dimension names of :attr:`self` must be present in :attr:`names`.
:attr:`names` may contain additional names that are not in ``self.names``;
the output tensor has a size-one dimension for each of those new names.
:attr:`names` may contain up to one Ellipsis (``...``).
The Ellipsis is expanded to be equal to all dimension names of :attr:`self`
that are not mentioned in :attr:`names`, in the order that they appear
in :attr:`self`.
Python 2 does not support Ellipsis but one may use a string literal
instead (``'...'``).
Arguments:
names (iterable of str): The desired dimension ordering of the
output tensor. May contain up to one Ellipsis that is expanded
to all unmentioned dim names of :attr:`self`.
Examples::
>>> tensor = torch.randn(2, 2, 2, 2, 2, 2)
>>> named_tensor = tensor.refine_names('A', 'B', 'C', 'D', 'E', 'F')
# Move the F and E dims to the front while keeping the rest in order
>>> named_tensor.align_to('F', 'E', ...)
.. warning::
The named tensor API is experimental and subject to change.
"""
return super(Tensor, self).align_to(
resolve_ellipsis(names,
self.names,
'align_to',
is_positional=False))
def refine_names(self, *names):
names = resolve_ellipsis(names, self.names, 'refine_names')
return super(Tensor, self).refine_names(names)
def align_to(self, *names):
return super(Tensor, self).align_to(resolve_ellipsis(names, self.names, 'align_to'))
def align_to(self, *names):
return super(Tensor, self).align_to(
resolve_ellipsis(names,
self.names,
'align_to',
is_positional=False))
