def rename_(self, *names, **rename_map):
# Note [rename_ / rename API]
# The Python API for these is different from the C++ API. In Python:
# 1) tensor.rename(*names) takes a vararglist of names
# 2) tensor.rename(**rename_map) takes a map of names to rename.
# C++ is static, making it difficult to implement similar behavior.
return update_names(self, names, rename_map, inplace=True)
def rename_(self, *names, **rename_map):
"""In-place version of :meth:`~Tensor.rename`."""
if has_torch_function_unary(self):
return handle_torch_function(Tensor.rename_, (self,), self, *names, **rename_map)
# Note [rename_ / rename API]
# The Python API for these is different from the C++ API. In Python:
# 1) tensor.rename(*names) takes a vararglist of names
# 2) tensor.rename(**rename_map) takes a map of names to rename.
# C++ is static, making it difficult to implement similar behavior.
return update_names(self, names, rename_map, inplace=True)
def rename_(self, *names, **rename_map):
"""In-place version of :meth:`~Tensor.rename`."""
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.rename_, relevant_args, self, *names, **rename_map)
# Note [rename_ / rename API]
# The Python API for these is different from the C++ API. In Python:
# 1) tensor.rename(*names) takes a vararglist of names
# 2) tensor.rename(**rename_map) takes a map of names to rename.
# C++ is static, making it difficult to implement similar behavior.
return update_names(self, names, rename_map, inplace=True)
def rename(self, *names, **rename_map):
"""Renames dimension names of :attr:`self`.
There are two main usages:
``self.rename(**rename_map)`` returns a view on tensor that has dims
renamed as specified in the mapping :attr:`rename_map`.
``self.rename(*names)`` returns a view on tensor, renaming all
dimensions positionally using :attr:`names`.
Use ``self.rename(None)`` to drop names on a tensor.
One cannot specify both positional args :attr:`names` and keyword args
:attr:`rename_map`.
Examples::
>>> imgs = torch.rand(2, 3, 5, 7, names=('N', 'C', 'H', 'W'))
>>> renamed_imgs = imgs.rename(N='batch', C='channels')
>>> renamed_imgs.names
('batch', 'channels', 'H', 'W')
>>> renamed_imgs = imgs.rename(None)
>>> renamed_imgs.names
(None,)
>>> renamed_imgs = imgs.rename('batch', 'channel', 'height', 'width')
>>> renamed_imgs.names
('batch', 'channel', 'height', 'width')
.. 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.rename, relevant_args, self,
*names, **rename_map)
# See Note [rename_ / rename API]
return update_names(self, names, rename_map, inplace=False)
def rename(self, *names, **rename_map):
# See Note [rename_ / rename API]
return update_names(self, names, rename_map, inplace=False)
