def unflatten(self, dim, namedshape):
r"""Unflattens the named dimension :attr:`dim`, viewing it in the shape
specified by :attr:`namedshape`.
Arguments:
namedshape: (iterable of ``(name, size)`` tuples).
Examples::
>>> flat_imgs = torch.rand(32, 3 * 128 * 128, names=('N', 'features'))
>>> imgs = flat_imgs.unflatten('features', (('C', 3), ('H', 128), ('W', 128)))
>>> imgs.names, imgs.shape
(('N', 'C', 'H', 'W'), torch.Size([32, 3, 128, 128]))
.. 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.unflatten, relevant_args, self,
dim, namedshape)
names, sizes = unzip_namedshape(namedshape)
return super(Tensor, self).unflatten(dim, sizes, names)
def unflatten(self, dim, sizes):
r"""Expands the dimension :attr:`dim` of the :attr:`self` tensor over multiple dimensions
of sizes given by :attr:`sizes`.
* :attr:`sizes` is the new shape of the unflattened dimension and it can be a `Tuple[int]` as well
as `torch.Size` if :attr:`self` is a `Tensor`, or `namedshape` (Tuple[(name: str, size: int)])
if :attr:`self` is a `NamedTensor`. The total number of elements in sizes must match the number
of elements in the original dim being unflattened.
Args:
dim (Union[int, str]): Dimension to unflatten
sizes (Union[Tuple[int] or torch.Size, Tuple[Tuple[str, int]]]): New shape of the unflattened dimension
Examples:
>>> torch.randn(3, 4, 1).unflatten(1, (2, 2)).shape
torch.Size([3, 2, 2, 1])
>>> torch.randn(3, 4, 1).unflatten(1, (-1, 2)).shape # the size -1 is inferred from the size of dimension 1
torch.Size([3, 2, 2, 1])
>>> torch.randn(2, 4, names=('A', 'B')).unflatten('B', (('B1', 2), ('B2', 2)))
tensor([[[-1.1772, 0.0180],
[ 0.2412, 0.1431]],
[[-1.1819, -0.8899],
[ 1.5813, 0.2274]]], names=('A', 'B1', 'B2'))
>>> torch.randn(2, names=('A',)).unflatten('A', (('B1', -1), ('B2', 1)))
tensor([[-0.8591],
[ 0.3100]], names=('B1', 'B2'))
.. warning::
The named tensor API is experimental and subject to change.
"""
if has_torch_function_unary(self):
return handle_torch_function(Tensor.unflatten, (self, ), self, dim,
sizes)
if not sizes:
raise RuntimeError("unflatten: sizes must be non-empty")
names = None
if isinstance(sizes,
OrderedDict) or (isinstance(sizes, (tuple, list))
and isinstance(sizes[0],
(tuple, list))):
names, sizes = unzip_namedshape(sizes)
return super(Tensor, self).unflatten(dim, sizes, names)
def unflatten(self, dim, namedshape):
r"""Unflattens the named dimension :attr:`dim`, viewing it in the shape
specified by :attr:`namedshape`.
Arguments:
namedshape: (iterable of ``(name, size)`` tuples).
Examples::
>>> flat_imgs = torch.rand(32, 3 * 128 * 128, names=('N', 'features'))
>>> imgs = flat_imgs.unflatten('features', (('C', 3), ('H', 128), ('W', 128)))
>>> imgs.names, images.shape
(('N', 'C', 'H', 'W'), torch.Size([32, 3, 128, 128]))
.. warning::
The named tensor API is experimental and subject to change.
"""
names, sizes = unzip_namedshape(namedshape)
return super(Tensor, self).unflatten(dim, sizes, names)
def unflatten(self, dim, namedshape):
names, sizes = unzip_namedshape(namedshape)
return super(Tensor, self).unflatten(dim, sizes, names)
