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pytorch/torch/_tensor_docs.py
Sam Gross c4d3f69053 Add Variable.item() (#5090) 
Variable.item() converts one-element tensors to standard Python numbers.
This operates like float(var) or int(var) depending on
the data type of the Variable.
2018-02-06 17:15:53 -05:00

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"""Adds docstrings to Tensor functions"""
import torch._C
from torch._C import _add_docstr as add_docstr
tensor_classes = [
'DoubleTensorBase',
'FloatTensorBase',
'LongTensorBase',
'IntTensorBase',
'ShortTensorBase',
'CharTensorBase',
'ByteTensorBase',
]
def add_docstr_all(method, docstr):
for cls_name in tensor_classes:
cls = getattr(torch._C, cls_name)
try:
add_docstr(getattr(cls, method), docstr)
except AttributeError:
pass
add_docstr_all('abs',
r"""
abs() -> Tensor
See :func:`torch.abs`
""")
add_docstr_all('abs_',
r"""
abs_() -> Tensor
In-place version of :meth:`~Tensor.abs`
""")
add_docstr_all('acos',
r"""
acos() -> Tensor
See :func:`torch.acos`
""")
add_docstr_all('acos_',
r"""
acos_() -> Tensor
In-place version of :meth:`~Tensor.acos`
""")
add_docstr_all('add',
r"""
add(value)
See :func:`torch.add`
""")
add_docstr_all('add_',
r"""
add_(value)
In-place version of :meth:`~Tensor.add`
""")
add_docstr_all('addbmm',
r"""
addbmm(beta=1, mat, alpha=1, batch1, batch2) -> Tensor
See :func:`torch.addbmm`
""")
add_docstr_all('addbmm_',
r"""
addbmm_(beta=1, mat, alpha=1, batch1, batch2) -> Tensor
In-place version of :meth:`~Tensor.addbmm`
""")
add_docstr_all('addcdiv',
r"""
addcdiv(value=1, tensor1, tensor2) -> Tensor
See :func:`torch.addcdiv`
""")
add_docstr_all('addcdiv_',
r"""
addcdiv_(value=1, tensor1, tensor2) -> Tensor
In-place version of :meth:`~Tensor.addcdiv`
""")
add_docstr_all('addcmul',
r"""
addcmul(value=1, tensor1, tensor2) -> Tensor
See :func:`torch.addcmul`
""")
add_docstr_all('addcmul_',
r"""
addcmul_(value=1, tensor1, tensor2) -> Tensor
In-place version of :meth:`~Tensor.addcmul`
""")
add_docstr_all('addmm',
r"""
addmm(beta=1, mat, alpha=1, mat1, mat2) -> Tensor
See :func:`torch.addmm`
""")
add_docstr_all('addmm_',
r"""
addmm_(beta=1, mat, alpha=1, mat1, mat2) -> Tensor
In-place version of :meth:`~Tensor.addmm`
""")
add_docstr_all('addmv',
r"""
addmv(beta=1, tensor, alpha=1, mat, vec) -> Tensor
See :func:`torch.addmv`
""")
add_docstr_all('addmv_',
r"""
addmv_(beta=1, tensor, alpha=1, mat, vec) -> Tensor
In-place version of :meth:`~Tensor.addmv`
""")
add_docstr_all('addr',
r"""
addr(beta=1, alpha=1, vec1, vec2) -> Tensor
See :func:`torch.addr`
""")
add_docstr_all('addr_',
r"""
addr_(beta=1, alpha=1, vec1, vec2) -> Tensor
In-place version of :meth:`~Tensor.addr`
""")
add_docstr_all('all',
r"""
all() -> bool
Returns True if all elements in the tensor are non-zero, False otherwise.
""")
add_docstr_all('any',
r"""
any() -> bool
Returns True if any elements in the tensor are non-zero, False otherwise.
""")
add_docstr_all('apply_',
r"""
apply_(callable) -> Tensor
Applies the function :attr:`callable` to each element in the tensor, replacing
each element with the value returned by :attr:`callable`.
.. note::
This function only works with CPU tensors and should not be used in code
sections that require high performance.
""")
add_docstr_all('asin',
r"""
asin() -> Tensor
See :func:`torch.asin`
""")
add_docstr_all('asin_',
r"""
asin_() -> Tensor
In-place version of :meth:`~Tensor.asin`
""")
add_docstr_all('atan',
r"""
atan() -> Tensor
See :func:`torch.atan`
""")
add_docstr_all('atan2',
r"""
atan2(other) -> Tensor
See :func:`torch.atan2`
""")
add_docstr_all('atan2_',
r"""
atan2_(other) -> Tensor
In-place version of :meth:`~Tensor.atan2`
""")
add_docstr_all('atan_',
r"""
atan_() -> Tensor
In-place version of :meth:`~Tensor.atan`
""")
add_docstr_all('baddbmm',
r"""
baddbmm(beta=1, alpha=1, batch1, batch2) -> Tensor
See :func:`torch.baddbmm`
""")
add_docstr_all('baddbmm_',
r"""
baddbmm_(beta=1, alpha=1, batch1, batch2) -> Tensor
In-place version of :meth:`~Tensor.baddbmm`
""")
add_docstr_all('bernoulli',
r"""
bernoulli() -> Tensor
See :func:`torch.bernoulli`
""")
add_docstr_all('bernoulli_',
r"""
bernoulli_() -> Tensor
In-place version of :meth:`~Tensor.bernoulli`
""")
add_docstr_all('bmm',
r"""
bmm(batch2) -> Tensor
See :func:`torch.bmm`
""")
add_docstr_all('btrifact',
r"""
btrifact(info=None, pivot=True) -> (Tensor, Tensor)
See :func:`torch.btrifact`
""")
add_docstr_all('btrifact_with_info',
r"""
btrifact_with_info(pivot=True) -> (Tensor, Tensor, Tensor)
See :func:`torch.btrifact_with_info`
""")
add_docstr_all('cauchy_',
r"""
cauchy_(median=0, sigma=1, *, generator=None) -> Tensor
Fills the tensor with numbers drawn from the Cauchy distribution:
.. math::
P(x) = \dfrac{1}{\pi} \dfrac{\sigma}{(x - median)^2 + \sigma^2}
""")
add_docstr_all('ceil',
r"""
ceil() -> Tensor
See :func:`torch.ceil`
""")
add_docstr_all('ceil_',
r"""
ceil_() -> Tensor
In-place version of :meth:`~Tensor.ceil`
""")
add_docstr_all('clamp',
r"""
clamp(min, max) -> Tensor
See :func:`torch.clamp`
""")
add_docstr_all('clamp_',
r"""
clamp_(min, max) -> Tensor
In-place version of :meth:`~Tensor.clamp`
""")
add_docstr_all('clone',
r"""
clone() -> Tensor
Returns a copy of the :attr:`self` tensor. The copy has the same size and data
type as :attr:`self`.
""")
add_docstr_all('contiguous',
r"""
contiguous() -> Tensor
Returns a contiguous tensor containing the same data as :attr:`self` tensor. If
:attr:`self` tensor is contiguous, this function returns the :attr:`self`
tensor.
""")
add_docstr_all('copy_',
r"""
copy_(src, non_blocking=False, broadcast=True) -> Tensor
Copies the elements from :attr:`src` into :attr:`self` tensor and returns
:attr:`self`.
If :attr:`broadcast` is True, the :attr:`src` tensor must be
:ref:`broadcastable <broadcasting-semantics>` with :attr:`self` tensor.
Otherwise, :attr:`src` tensor should have the same number of elements as
:attr:`self` tensor. It may be of a different data type or reside on a
different device.
Args:
src (Tensor): the source tensor to copy from
non_blocking (bool): if ``True`` and this copy is between CPU and GPU,
the copy may occur asynchronously with respect to the host. For other
cases, this argument has no effect.
broadcast (bool): if ``True``, :attr:`src` will be broadcast to the shape of
the underlying tensor.
""")
add_docstr_all('cos',
r"""
cos() -> Tensor
See :func:`torch.cos`
""")
add_docstr_all('cos_',
r"""
cos_() -> Tensor
In-place version of :meth:`~Tensor.cos`
""")
add_docstr_all('cosh',
r"""
cosh() -> Tensor
See :func:`torch.cosh`
""")
add_docstr_all('cosh_',
r"""
cosh_() -> Tensor
In-place version of :meth:`~Tensor.cosh`
""")
add_docstr_all('cross',
r"""
cross(other, dim=-1) -> Tensor
See :func:`torch.cross`
""")
add_docstr_all('cumprod',
r"""
cumprod(dim) -> Tensor
See :func:`torch.cumprod`
""")
add_docstr_all('cumsum',
r"""
cumsum(dim) -> Tensor
See :func:`torch.cumsum`
""")
add_docstr_all('data_ptr',
r"""
data_ptr() -> int
Returns the address of the first element of :attr:`self` tensor.
""")
add_docstr_all('diag',
r"""
diag(diagonal=0) -> Tensor
See :func:`torch.diag`
""")
add_docstr_all('dim',
r"""
dim() -> int
Returns the number of dimensions of :attr:`self` tensor.
""")
add_docstr_all('dist',
r"""
dist(other, p=2) -> float
See :func:`torch.dist`
""")
add_docstr_all('div',
r"""
div(value)
See :func:`torch.div`
""")
add_docstr_all('div_',
r"""
div_(value)
In-place version of :meth:`~Tensor.div`
""")
add_docstr_all('dot',
r"""
dot(tensor2) -> float
See :func:`torch.dot`
""")
add_docstr_all('eig',
r"""
eig(eigenvectors=False) -> (Tensor, Tensor)
See :func:`torch.eig`
""")
add_docstr_all('element_size',
r"""
element_size() -> int
Returns the size in bytes of an individual element.
Example:
>>> torch.FloatTensor().element_size()
4
>>> torch.ByteTensor().element_size()
1
""")
add_docstr_all('eq',
r"""
eq(other) -> Tensor
See :func:`torch.eq`
""")
add_docstr_all('eq_',
r"""
eq_(other) -> Tensor
In-place version of :meth:`~Tensor.eq`
""")
add_docstr_all('equal',
r"""
equal(other) -> bool
See :func:`torch.equal`
""")
add_docstr_all('erf',
r"""
erf() -> Tensor
See :func:`torch.erf`
""")
add_docstr_all('erfinv',
r"""
erfinv() -> Tensor
See :func:`torch.erfinv`
""")
add_docstr_all('exp',
r"""
exp() -> Tensor
See :func:`torch.exp`
""")
add_docstr_all('exp_',
r"""
exp_() -> Tensor
In-place version of :meth:`~Tensor.exp`
""")
add_docstr_all('expm1',
r"""
expm1() -> Tensor
See :func:`torch.expm1`
""")
add_docstr_all('expm1_',
r"""
expm1_() -> Tensor
In-place version of :meth:`~Tensor.expm1`
""")
add_docstr_all('exponential_',
r"""
exponential_(lambd=1, *, generator=None) -> Tensor
Fills :attr:`self` tensor with elements drawn from the exponential distribution:
.. math::
P(x) = \lambda e^{-\lambda x}
""")
add_docstr_all('fill_',
r"""
fill_(value) -> Tensor
Fills :attr:`self` tensor with the specified value.
""")
add_docstr_all('floor',
r"""
floor() -> Tensor
See :func:`torch.floor`
""")
add_docstr_all('floor_',
r"""
floor_() -> Tensor
In-place version of :meth:`~Tensor.floor`
""")
add_docstr_all('fmod',
r"""
fmod(divisor) -> Tensor
See :func:`torch.fmod`
""")
add_docstr_all('fmod_',
r"""
fmod_(divisor) -> Tensor
In-place version of :meth:`~Tensor.fmod`
""")
add_docstr_all('frac',
r"""
frac() -> Tensor
See :func:`torch.frac`
""")
add_docstr_all('frac_',
r"""
frac_() -> Tensor
In-place version of :meth:`~Tensor.frac`
""")
add_docstr_all('gather',
r"""
gather(dim, index) -> Tensor
See :func:`torch.gather`
""")
add_docstr_all('ge',
r"""
ge(other) -> Tensor
See :func:`torch.ge`
""")
add_docstr_all('ge_',
r"""
ge_(other) -> Tensor
In-place version of :meth:`~Tensor.ge`
""")
add_docstr_all('gels',
r"""
gels(A) -> Tensor
See :func:`torch.gels`
""")
add_docstr_all('geometric_',
r"""
geometric_(p, *, generator=None) -> Tensor
Fills :attr:`self` tensor with elements drawn from the geometric distribution:
.. math::
P(X=k) = (1 - p)^{k - 1} p
""")
add_docstr_all('geqrf',
r"""
geqrf() -> (Tensor, Tensor)
See :func:`torch.geqrf`
""")
add_docstr_all('ger',
r"""
ger(vec2) -> Tensor
See :func:`torch.ger`
""")
add_docstr_all('gesv',
r"""
gesv(A) -> Tensor, Tensor
See :func:`torch.gesv`
""")
add_docstr_all('gt',
r"""
gt(other) -> Tensor
See :func:`torch.gt`
""")
add_docstr_all('gt_',
r"""
gt_(other) -> Tensor
In-place version of :meth:`~Tensor.gt`
""")
add_docstr_all('histc',
r"""
histc(bins=100, min=0, max=0) -> Tensor
See :func:`torch.histc`
""")
add_docstr_all('index',
r"""
index(m) -> Tensor
Selects elements from :attr:`self` tensor using a binary mask or along a given
dimension. The expression ``tensor.index(m)`` is equivalent to ``tensor[m]``.
Args:
m (int or ByteTensor or slice): the dimension or mask used to select elements
""")
add_docstr_all('index_add_',
r"""
index_add_(dim, index, tensor) -> Tensor
Accumulate the elements of :attr:`tensor` into the :attr:`self` tensor by adding
to the indices in the order given in :attr:`index`. For example, if ``dim == 0``
and ``index[i] == j``, then the ``i``\ th row of :attr:`tensor` is added to the
``j``\ th row of :attr:`self`.
The :attr:`dim`\ th dimension of :attr:`tensor` must have the same size as the
length of :attr:`index` (which must be a vector), and all other dimensions must
match :attr:`self`, or an error will be raised.
Args:
dim (int): dimension along which to index
index (LongTensor): indices of :attr:`tensor` to select from
tensor (Tensor): the tensor containing values to add
Example:
>>> x = torch.Tensor(5, 3).fill_(1)
>>> t = torch.Tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
>>> index = torch.LongTensor([0, 4, 2])
>>> x.index_add_(0, index, t)
>>> x
2 3 4
1 1 1
8 9 10
1 1 1
5 6 7
[torch.FloatTensor of size 5x3]
""")
add_docstr_all('index_copy_',
r"""
index_copy_(dim, index, tensor) -> Tensor
Copies the elements of :attr:`tensor` into the :attr:`self` tensor by selecting
the indices in the order given in :attr:`index`. For example, if ``dim == 0``
and ``index[i] == j``, then the ``i``\ th row of :attr:`tensor` is copied to the
``j``\ th row of :attr:`self`.
The :attr:`dim`\ th dimension of :attr:`tensor` must have the same size as the
length of :attr:`index` (which must be a vector), and all other dimensions must
match :attr:`self`, or an error will be raised.
Args:
dim (int): dimension along which to index
index (LongTensor): indices of :attr:`tensor` to select from
tensor (Tensor): the tensor containing values to copy
Example:
>>> x = torch.zeros(5, 3)
>>> t = torch.Tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
>>> index = torch.LongTensor([0, 4, 2])
>>> x.index_copy_(0, index, t)
>>> x
1 2 3
0 0 0
7 8 9
0 0 0
4 5 6
[torch.FloatTensor of size 5x3]
""")
add_docstr_all('index_fill_',
r"""
index_fill_(dim, index, val) -> Tensor
Fills the elements of the :attr:`self` tensor with value :attr:`val` by
selecting the indices in the order given in :attr:`index`.
Args:
dim (int): dimension along which to index
index (LongTensor): indices of :attr:`self` tensor to fill in
val (float): the value to fill with
Example:
>>> x = torch.Tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
>>> index = torch.LongTensor([0, 2])
>>> x.index_fill_(1, index, -1)
>>> x
-1 2 -1
-1 5 -1
-1 8 -1
[torch.FloatTensor of size 3x3]
""")
add_docstr_all('index_select',
r"""
index_select(dim, index) -> Tensor
See :func:`torch.index_select`
""")
add_docstr_all('inverse',
r"""
inverse() -> Tensor
See :func:`torch.inverse`
""")
add_docstr_all('is_contiguous',
r"""
is_contiguous() -> bool
Returns True if :attr:`self` tensor is contiguous in memory in C order.
""")
add_docstr_all('is_set_to',
r"""
is_set_to(tensor) -> bool
Returns True if this object refers to the same ``THTensor`` object from the
Torch C API as the given tensor.
""")
# TODO (sgross): move to Tensor once Variable and Tensor are merged
add_docstr(torch.autograd.Variable.item, r"""
item() -> number
Returns the value of this tensor as a standard Python number. This only works
for tensors with one element.
This operation is not differentiable.
Example:
>>> x = torch.Tensor([1.0])
>>> x.item()
1.0
""")
add_docstr_all('kthvalue',
r"""
kthvalue(k, dim=None, keepdim=False) -> (Tensor, LongTensor)
See :func:`torch.kthvalue`
""")
add_docstr_all('le',
r"""
le(other) -> Tensor
See :func:`torch.le`
""")
add_docstr_all('le_',
r"""
le_(other) -> Tensor
In-place version of :meth:`~Tensor.le`
""")
add_docstr_all('lerp',
r"""
lerp(start, end, weight)
See :func:`torch.lerp`
""")
add_docstr_all('lerp_',
r"""
lerp_(start, end, weight)
In-place version of :meth:`~Tensor.lerp`
""")
add_docstr_all('log',
r"""
log() -> Tensor
See :func:`torch.log`
""")
add_docstr_all('log1p',
r"""
log1p() -> Tensor
See :func:`torch.log1p`
""")
add_docstr_all('log1p_',
r"""
log1p_() -> Tensor
In-place version of :meth:`~Tensor.log1p`
""")
add_docstr_all('log_', r"""
log_() -> Tensor
In-place version of :meth:`~Tensor.log`
""")
add_docstr_all('log_normal_', u"""
log_normal_(mean=1, std=2, *, generator=None)
Fills :attr:`self` tensor with numbers samples from the log-normal distribution
parameterized by the given mean (\u00B5) and standard deviation (\u03C3).
Note that :attr:`mean` and :attr:`stdv` are the mean and standard deviation of
the underlying normal distribution, and not of the returned distribution:
.. math::
P(x) = \\dfrac{1}{x \\sigma \\sqrt{2\\pi}}\ e^{-\\dfrac{(\\ln x - \\mu)^2}{2\\sigma^2}}
""")
add_docstr_all('lt',
r"""
lt(other) -> Tensor
See :func:`torch.lt`
""")
add_docstr_all('lt_',
r"""
lt_(other) -> Tensor
In-place version of :meth:`~Tensor.lt`
""")
add_docstr_all('map_',
r"""
map_(tensor, callable)
Applies :attr:`callable` for each element in :attr:`self` tensor and the given
:attr:`tensor` and stores the results in :attr:`self` tensor. :attr:`self` tensor and
the given :attr:`tensor` must be :ref:`broadcastable <broadcasting-semantics>`.
The :attr:`callable` should have the signature::
def callable(a, b) -> number
""")
add_docstr_all('masked_scatter_',
r"""
masked_scatter_(mask, source)
Copies elements from :attr:`source` into :attr:`self` tensor at positions where
the :attr:`mask` is one.
The shape of :attr:`mask` must be :ref:`broadcastable <broadcasting-semantics>`
with the shape of the underlying tensor. The :attr:`source` should have at least
as many elements as the number of ones in :attr:`mask`
Args:
mask (ByteTensor): the binary mask
source (Tensor): the tensor to copy from
.. note::
The :attr:`mask` operates on the :attr:`self` tensor, not on the given
:attr:`source` tensor.
""")
add_docstr_all('masked_fill_',
r"""
masked_fill_(mask, value)
Fills elements of :attr:`self` tensor with :attr:`value` where :attr:`mask` is
one. The shape of :attr:`mask` must be
:ref:`broadcastable <broadcasting-semantics>` with the shape of the underlying
tensor.
Args:
mask (ByteTensor): the binary mask
value (float): the value to fill in with
""")
add_docstr_all('masked_select',
r"""
masked_select(mask) -> Tensor
See :func:`torch.masked_select`
""")
add_docstr_all('max',
r"""
max(dim=None, keepdim=False) -> float or (Tensor, Tensor)
See :func:`torch.max`
""")
add_docstr_all('mean',
r"""
mean(dim=None, keepdim=False) -> float or (Tensor, Tensor)
See :func:`torch.mean`
""")
add_docstr_all('median',
r"""
median(dim=None, keepdim=False) -> (Tensor, LongTensor)
See :func:`torch.median`
""")
add_docstr_all('min',
r"""
min(dim=None, keepdim=False) -> float or (Tensor, Tensor)
See :func:`torch.min`
""")
add_docstr_all('mm',
r"""
mm(mat2) -> Tensor
See :func:`torch.mm`
""")
add_docstr_all('mode',
r"""
mode(dim=None, keepdim=False) -> (Tensor, LongTensor)
See :func:`torch.mode`
""")
add_docstr_all('mul',
r"""
mul(value) -> Tensor
See :func:`torch.mul`
""")
add_docstr_all('mul_',
r"""
mul_(value)
In-place version of :meth:`~Tensor.mul`
""")
add_docstr_all('multinomial',
r"""
multinomial(num_samples, replacement=False, *, generator=None)
See :func:`torch.multinomial`
""")
add_docstr_all('mv',
r"""
mv(vec) -> Tensor
See :func:`torch.mv`
""")
add_docstr_all('narrow',
r"""
narrow(dimension, start, length) -> Tensor
Returns a new tensor that is a narrowed version of :attr:`self` tensor. The
dimension :attr:`dim` is narrowed from :attr:`start` to :attr:`start + length`. The
returned tensor and :attr:`self` tensor share the same underlying storage.
Args:
dimension (int): the dimension along which to narrow
start (int): the starting dimension
length (int): the distance to the ending dimension
Example:
>>> x = torch.Tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
>>> x.narrow(0, 0, 2)
1 2 3
4 5 6
[torch.FloatTensor of size 2x3]
>>> x.narrow(1, 1, 2)
2 3
5 6
8 9
[torch.FloatTensor of size 3x2]
""")
add_docstr_all('ndimension',
r"""
ndimension() -> int
Alias for :meth:`~Tensor.dim()`
""")
add_docstr_all('ne',
r"""
ne(other) -> Tensor
See :func:`torch.ne`
""")
add_docstr_all('ne_',
r"""
ne_(other) -> Tensor
In-place version of :meth:`~Tensor.ne`
""")
add_docstr_all('neg',
r"""
neg() -> Tensor
See :func:`torch.neg`
""")
add_docstr_all('neg_',
r"""
neg_() -> Tensor
In-place version of :meth:`~Tensor.neg`
""")
add_docstr_all('nelement',
r"""
nelement() -> int
Alias for :meth:`~Tensor.numel`
""")
add_docstr_all('nonzero',
r"""
nonzero() -> LongTensor
See :func:`torch.nonzero`
""")
add_docstr_all('norm',
r"""
norm(p=2, dim=None, keepdim=False) -> float
See :func:`torch.norm`
""")
add_docstr_all('normal_',
r"""
normal_(mean=0, std=1, *, generator=None)
Fills :attr:`self` tensor with elements samples from the normal distribution
parameterized by :attr:`mean` and :attr:`std`.
""")
add_docstr_all('numel',
r"""
numel() -> int
See :func:`torch.numel`
""")
add_docstr_all('numpy',
r"""
numpy() -> ndarray
Returns :attr:`self` tensor as a NumPy :class:`ndarray`. This tensor and the
returned :class:`ndarray` share the same underlying storage. Changes to
:attr:`self` tensor will be reflected in the :class:`ndarray` and vice versa.
""")
add_docstr_all('orgqr',
r"""
orgqr(input2) -> Tensor
See :func:`torch.orgqr`
""")
add_docstr_all('ormqr',
r"""
ormqr(input2, input3, left=True, transpose=False) -> Tensor
See :func:`torch.ormqr`
""")
add_docstr_all('potrf',
r"""
potrf(upper=True) -> Tensor
See :func:`torch.potrf`
""")
add_docstr_all('potri',
r"""
potri(upper=True) -> Tensor
See :func:`torch.potri`
""")
add_docstr_all('potrs',
r"""
potrs(input2, upper=True) -> Tensor
See :func:`torch.potrs`
""")
add_docstr_all('pow',
r"""
pow(exponent)
See :func:`torch.pow`
""")
add_docstr_all('pow_',
r"""
pow_(exponent)
In-place version of :meth:`~Tensor.pow`
""")
add_docstr_all('prod',
r"""
prod(dim=None, keepdim=False) -> float
See :func:`torch.prod`
""")
add_docstr_all('pstrf',
r"""
pstrf(upper=True, tol=-1) -> (Tensor, IntTensor)
See :func:`torch.pstrf`
""")
add_docstr_all('put_',
r"""
put_(indices, tensor, accumulate=False) -> Tensor
Copies the elements from :attr:`tensor` into the positions specified by
indices. For the puropose of indexing, the :attr:`self` tensor is treated as if
it were a 1-D tensor.
If :attr:`accumulate` is ``True``, the elements in :attr:`tensor` are added to
:attr:`self`. If accumulate is ``False``, the behavior is undefined if indices
contains duplicate elements.
Args:
indices (LongTensor): the indices into self
tensor (Tensor): the tensor containing values to copy from
accumulate (bool): whether to accumulate into self
Example::
>>> src = torch.Tensor([[4, 3, 5],
... [6, 7, 8]])
>>> src.put_(torch.LongTensor([1, 3]), torch.Tensor([9, 10]))
4 9 5
10 7 8
[torch.FloatTensor of size 2x3]
""")
add_docstr_all('qr',
r"""
qr() -> (Tensor, Tensor)
See :func:`torch.qr`
""")
add_docstr_all('random_',
r"""
random_(from=0, to=None, *, generator=None)
Fills :attr:`self` tensor with numbers sampled from the discrete uniform
distribution over ``[from, to - 1]``. If not specified, the values are usually
only bounded by :attr:`self` tensor's data type. However, for floating point
types, if unspecified, range will be ``[0, 2^mantissa]`` to ensure that every
value is representable. For example, `torch.DoubleTensor(1).random_()` will be
uniform in ``[0, 2^53]``.
""")
add_docstr_all('reciprocal',
r"""
reciprocal() -> Tensor
See :func:`torch.reciprocal`
""")
add_docstr_all('reciprocal_',
r"""
reciprocal_() -> Tensor
In-place version of :meth:`~Tensor.reciprocal`
""")
add_docstr_all('remainder',
r"""
remainder(divisor) -> Tensor
See :func:`torch.remainder`
""")
add_docstr_all('remainder_',
r"""
remainder_(divisor) -> Tensor
In-place version of :meth:`~Tensor.remainder`
""")
add_docstr_all('renorm',
r"""
renorm(p, dim, maxnorm) -> Tensor
See :func:`torch.renorm`
""")
add_docstr_all('renorm_',
r"""
renorm_(p, dim, maxnorm) -> Tensor
In-place version of :meth:`~Tensor.renorm`
""")
add_docstr_all('resize_',
r"""
resize_(*sizes)
Resizes :attr:`self` tensor to the specified size. If the number of elements is
larger than the current storage size, then the underlying storage is resized
to fit the new number of elements. If the number of elements is smaller, the
underlying storage is not changed. Existing elements are preserved but any new
memory is uninitialized.
Args:
sizes (torch.Size or int...): the desired size
Example:
>>> x = torch.Tensor([[1, 2], [3, 4], [5, 6]])
>>> x.resize_(2, 2)
>>> x
1 2
3 4
[torch.FloatTensor of size 2x2]
""")
add_docstr_all('resize_as_',
r"""
resize_as_(tensor)
Resizes the :attr:`self` tensor to be the same size as the specified
:attr:`tensor`. This is equivalent to::
self.resize_(tensor.size())
""")
add_docstr_all('round',
r"""
round() -> Tensor
See :func:`torch.round`
""")
add_docstr_all('round_',
r"""
round_() -> Tensor
In-place version of :meth:`~Tensor.round`
""")
add_docstr_all('rsqrt',
r"""
rsqrt() -> Tensor
See :func:`torch.rsqrt`
""")
add_docstr_all('rsqrt_',
r"""
rsqrt_() -> Tensor
In-place version of :meth:`~Tensor.rsqrt`
""")
add_docstr_all('scatter_',
r"""
scatter_(dim, index, src) -> Tensor
Writes all values from the tensor :attr:`src` into :attr:`self` at the indices
specified in the :attr:`index` tensor. For each value in :attr:`src`, its output
index is specified by its index in :attr:`src` for dimension != :attr:`dim` and
by the corresponding value in :attr:`index` for dimension = :attr:`dim`.
For a 3-D tensor, :attr:`self` is updated as::
self[index[i][j][k]][j][k] = src[i][j][k] # if dim == 0
self[i][index[i][j][k]][k] = src[i][j][k] # if dim == 1
self[i][j][index[i][j][k]] = src[i][j][k] # if dim == 2
This is the reverse operation of the manner described in :meth:`~Tensor.gather`.
:attr:`self`, :attr:`index` and :attr:`src` should have same number of
dimensions. It is also required that `index->size[d] <= src->size[d]` for all
dimension `d`, and that `index->size[d] <= real->size[d]` for all dimensions
`d != dim`.
Moreover, as for :meth:`~Tensor.gather`, the values of :attr:`index` must be
between `0` and `(self.size(dim) -1)` inclusive, and all values in a row along
the specified dimension :attr:`dim` must be unique.
Args:
input (Tensor): the source tensor
dim (int): the axis along which to index
index (LongTensor): the indices of elements to scatter
src (Tensor or float): the source element(s) to scatter
Example::
>>> x = torch.rand(2, 5)
>>> x
0.4319 0.6500 0.4080 0.8760 0.2355
0.2609 0.4711 0.8486 0.8573 0.1029
[torch.FloatTensor of size 2x5]
>>> torch.zeros(3, 5).scatter_(0, torch.LongTensor([[0, 1, 2, 0, 0], [2, 0, 0, 1, 2]]), x)
0.4319 0.4711 0.8486 0.8760 0.2355
0.0000 0.6500 0.0000 0.8573 0.0000
0.2609 0.0000 0.4080 0.0000 0.1029
[torch.FloatTensor of size 3x5]
>>> z = torch.zeros(2, 4).scatter_(1, torch.LongTensor([[2], [3]]), 1.23)
>>> z
0.0000 0.0000 1.2300 0.0000
0.0000 0.0000 0.0000 1.2300
[torch.FloatTensor of size 2x4]
""")
add_docstr_all('select',
r"""
select(dim, index) -> Tensor or number
Slices the :attr:`self` tensor along the selected dimension at the given index.
If :attr:`self` is one dimensional, this function returns a number. Otherwise,
it returns a tensor with the given dimension removed.
Args:
dim (int): the dimension to slice
index (int): the index to select with
.. note::
:meth:`select` is equivalent to slicing. For example,
``tensor.select(0, index)`` is equivalent to ``tensor[index]`` and
``tensor.select(2, index)`` is equivalent to ``tensor[:,:,index]``.
""")
add_docstr_all('set_',
r"""
set_(source=None, storage_offset=0, size=None, stride=None)
Sets the underlying storage, size, and strides. If :attr:`source` is a tensor,
:attr:`self` tensor will share the same storage and have the same size and
strides as :attr:`source`. Changes to elements in one tensor will be reflected
in the other.
If :attr:`source` is a :class:`~torch.Storage`, the method sets the underlying
storage, offset, size, and stride.
Args:
source (Tensor or Storage): the tensor or storage to use
storage_offset (int): the offset in the storage
size (torch.Size): the desired size. Defaults to the size of the source.
stride (tuple): the desired stride. Defaults to C-contiguous strides.
""")
add_docstr_all('sigmoid',
r"""
sigmoid() -> Tensor
See :func:`torch.sigmoid`
""")
add_docstr_all('sigmoid_',
r"""
sigmoid_() -> Tensor
In-place version of :meth:`~Tensor.sigmoid`
""")
add_docstr_all('sign',
r"""
sign() -> Tensor
See :func:`torch.sign`
""")
add_docstr_all('sign_',
r"""
sign_() -> Tensor
In-place version of :meth:`~Tensor.sign`
""")
add_docstr_all('sin',
r"""
sin() -> Tensor
See :func:`torch.sin`
""")
add_docstr_all('sin_',
r"""
sin_() -> Tensor
In-place version of :meth:`~Tensor.sin`
""")
add_docstr_all('sinh',
r"""
sinh() -> Tensor
See :func:`torch.sinh`
""")
add_docstr_all('sinh_',
r"""
sinh_() -> Tensor
In-place version of :meth:`~Tensor.sinh`
""")
add_docstr_all('size',
r"""
size() -> torch.Size
Returns the size of the :attr:`self` tensor. The returned value is a subclass of
:class:`tuple`.
Example:
>>> torch.Tensor(3, 4, 5).size()
torch.Size([3, 4, 5])
""")
add_docstr_all('sort',
r"""
sort(dim=None, descending=False) -> (Tensor, LongTensor)
See :func:`torch.sort`
""")
add_docstr_all('sqrt',
r"""
sqrt() -> Tensor
See :func:`torch.sqrt`
""")
add_docstr_all('sqrt_',
r"""
sqrt_() -> Tensor
In-place version of :meth:`~Tensor.sqrt`
""")
add_docstr_all('squeeze',
r"""
squeeze(dim=None)
See :func:`torch.squeeze`
""")
add_docstr_all('squeeze_',
r"""
squeeze_(dim=None)
In-place version of :meth:`~Tensor.squeeze`
""")
add_docstr_all('std',
r"""
std(dim=None, unbiased=True, keepdim=False) -> float
See :func:`torch.std`
""")
add_docstr_all('storage',
r"""
storage() -> torch.Storage
Returns the underlying storage
""")
add_docstr_all('storage_offset',
r"""
storage_offset() -> int
Returns :attr:`self` tensor's offset in the underlying storage in terms of
number of storage elements (not bytes).
Example:
>>> x = torch.Tensor([1, 2, 3, 4, 5])
>>> x.storage_offset()
0
>>> x[3:].storage_offset()
3
""")
add_docstr_all('stride',
r"""
stride(dim) -> tuple or int
Returns the stride of :attr:`self` tensor.
Stride is the jump necessary to go from one element to the next one in the
specified dimension :attr:`dim`. A tuple of all strides is returned when no
argument is passed in. Otherwise, an integer value is returned as the stride in
the particular dimension :attr:`dim`.
Args:
dim (int, optional): the desired dimension in which stride is required
Example:
>>> x = torch.Tensor([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]])
>>> x.stride()
(5, 1)
>>>x.stride(0)
5
>>> x.stride(-1)
1
""")
add_docstr_all('sub',
r"""
sub(value, other) -> Tensor
Subtracts a scalar or tensor from :attr:`self` tensor. If both :attr:`value` and
:attr:`other` are specified, each element of :attr:`other` is scaled by
:attr:`value` before being used.
When :attr:`other` is a tensor, the shape of :attr:`other` must be
:ref:`broadcastable <broadcasting-semantics>` with the shape of the underlying
tensor.
""")
add_docstr_all('sub_',
r"""
sub_(x) -> Tensor
In-place version of :meth:`~Tensor.sub`
""")
add_docstr_all('sum',
r"""
sum(dim=None, keepdim=False) -> float
See :func:`torch.sum`
""")
add_docstr_all('svd',
r"""
svd(some=True) -> (Tensor, Tensor, Tensor)
See :func:`torch.svd`
""")
add_docstr_all('symeig',
r"""
symeig(eigenvectors=False, upper=True) -> (Tensor, Tensor)
See :func:`torch.symeig`
""")
add_docstr_all('t',
r"""
t() -> Tensor
See :func:`torch.t`
""")
add_docstr_all('t_',
r"""
t_() -> Tensor
In-place version of :meth:`~Tensor.t`
""")
add_docstr_all('take',
r"""
take(indices) -> Tensor
See :func:`torch.take`
""")
add_docstr_all('tan_',
r"""
tan_() -> Tensor
In-place version of :meth:`~Tensor.tan`
""")
add_docstr_all('tanh',
r"""
tanh() -> Tensor
See :func:`torch.tanh`
""")
add_docstr_all('tanh_',
r"""
tanh_() -> Tensor
In-place version of :meth:`~Tensor.tanh`
""")
add_docstr_all('topk',
r"""
topk(k, dim=None, largest=True, sorted=True) -> (Tensor, LongTensor)
See :func:`torch.topk`
""")
add_docstr_all('trace',
r"""
trace() -> float
See :func:`torch.trace`
""")
add_docstr_all('transpose',
r"""
transpose(dim0, dim1) -> Tensor
See :func:`torch.transpose`
""")
add_docstr_all('transpose_',
r"""
transpose_(dim0, dim1) -> Tensor
In-place version of :meth:`~Tensor.transpose`
""")
add_docstr_all('tril',
r"""
tril(k=0) -> Tensor
See :func:`torch.tril`
""")
add_docstr_all('tril_',
r"""
tril_(k=0) -> Tensor
In-place version of :meth:`~Tensor.tril`
""")
add_docstr_all('triu',
r"""
triu(k=0) -> Tensor
See :func:`torch.triu`
""")
add_docstr_all('triu_',
r"""
triu_(k=0) -> Tensor
In-place version of :meth:`~Tensor.triu`
""")
add_docstr_all('trtrs',
r"""
trtrs(A, upper=True, transpose=False, unitriangular=False) -> (Tensor, Tensor)
See :func:`torch.trtrs`
""")
add_docstr_all('trunc',
r"""
trunc() -> Tensor
See :func:`torch.trunc`
""")
add_docstr_all('trunc_',
r"""
trunc_() -> Tensor
In-place version of :meth:`~Tensor.trunc`
""")
add_docstr_all('unfold',
r"""
unfold(dim, size, step) -> Tensor
Returns a tensor which contains all slices of size :attr:`size` from
:attr:`self` tensor in the dimension :attr:`dim`.
Step between two slices is given by :attr:`step`.
If `sizedim` is the size of dimension dim for :attr:`self`, the size of
dimension :attr:`dim` in the returned tensor will be
`(sizedim - size) / step + 1`.
An additional dimension of size size is appended in the returned tensor.
Args:
dim (int): dimension in which unfolding happens
size (int): the size of each slice that is unfolded
step (int): the step between each slice
Example::
>>> x = torch.arange(1, 8)
>>> x
1
2
3
4
5
6
7
[torch.FloatTensor of size 7]
>>> x.unfold(0, 2, 1)
1 2
2 3
3 4
4 5
5 6
6 7
[torch.FloatTensor of size 6x2]
>>> x.unfold(0, 2, 2)
1 2
3 4
5 6
[torch.FloatTensor of size 3x2]
""")
add_docstr_all('uniform_',
r"""
uniform_(from=0, to=1) -> Tensor
Fills :attr:`self` tensor with numbers sampled from the continuous uniform
distribution:
.. math:
P(x) = \dfrac{1}{to - from}
""")
add_docstr_all('unsqueeze',
r"""
unsqueeze(dim)
See :func:`torch.unsqueeze`
""")
add_docstr_all('unsqueeze_',
r"""
unsqueeze_(dim)
In-place version of :meth:`~Tensor.unsqueeze`
""")
add_docstr_all('var',
r"""
var(dim=None, unbiased=True, keepdim=False) -> float
See :func:`torch.var`
""")
add_docstr_all('view',
r"""
view(*args) -> Tensor
Returns a new tensor with the same data as the :attr:`self` tensor but of a
different size.
The returned tensor shares the same data and must have the same number
of elements, but may have a different size. For a tensor to be viewed, the new
view size must be compatible with its original size and stride, i.e., each new
view dimension must either be a subspace of an original dimension, or only span
across original dimensions :math:`d, d+1, \dots, d+k` that satisfy the following
contiguity-like condition that :math:`\forall i = 0, \dots, k-1`,
.. math::
stride[i] = stride[i+1] \times size[i+1]
Otherwise, :func:`contiguous` needs to be called before the tensor can be
viewed.
Args:
args (torch.Size or int...): the desired size
Example:
>>> x = torch.randn(4, 4)
>>> x.size()
torch.Size([4, 4])
>>> y = x.view(16)
>>> y.size()
torch.Size([16])
>>> z = x.view(-1, 8) # the size -1 is inferred from other dimensions
>>> z.size()
torch.Size([2, 8])
""")
add_docstr_all('expand',
r"""
expand(*sizes) -> Tensor
Returns a new view of the :attr:`self` tensor with singleton dimensions expanded
to a larger size.
Passing -1 as the size for a dimension means not changing the size of
that dimension.
Tensor can be also expanded to a larger number of dimensions, and the
new ones will be appended at the front. For the new dimensions, the
size cannot be set to -1.
Expanding a tensor does not allocate new memory, but only creates a
new view on the existing tensor where a dimension of size one is
expanded to a larger size by setting the ``stride`` to 0. Any dimension
of size 1 can be expanded to an arbitrary value without allocating new
memory.
Args:
*sizes (torch.Size or int...): the desired expanded size
Example:
>>> x = torch.Tensor([[1], [2], [3]])
>>> x.size()
torch.Size([3, 1])
>>> x.expand(3, 4)
1 1 1 1
2 2 2 2
3 3 3 3
[torch.FloatTensor of size 3x4]
>>> x.expand(-1, 4) # -1 means not changing the size of that dimension
1 1 1 1
2 2 2 2
3 3 3 3
[torch.FloatTensor of size 3x4]
""")
add_docstr_all('zero_',
r"""
zero_() -> Tensor
Fills :attr:`self` tensor with zeros.
""")
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