torch.Tensor¶

A torch.Tensor is a multi-dimensional matrix containing elements of a single data type.

Torch defines seven CPU tensor types and eight GPU tensor types:

Data type	CPU tensor	GPU tensor
32-bit floating point	`torch.FloatTensor`	`torch.cuda.FloatTensor`
64-bit floating point	`torch.DoubleTensor`	`torch.cuda.DoubleTensor`
16-bit floating point	`torch.HalfTensor`	`torch.cuda.HalfTensor`
8-bit integer (unsigned)	`torch.ByteTensor`	`torch.cuda.ByteTensor`
8-bit integer (signed)	`torch.CharTensor`	`torch.cuda.CharTensor`
16-bit integer (signed)	`torch.ShortTensor`	`torch.cuda.ShortTensor`
32-bit integer (signed)	`torch.IntTensor`	`torch.cuda.IntTensor`
64-bit integer (signed)	`torch.LongTensor`	`torch.cuda.LongTensor`

The torch.Tensor constructor is an alias for the default tensor type (torch.FloatTensor).

A tensor can be constructed from a Python list or sequence:

>>> torch.FloatTensor([[1, 2, 3], [4, 5, 6]])
1  2  3
4  5  6
[torch.FloatTensor of size 2x3]

An empty tensor can be constructed by specifying its size:

>>> torch.IntTensor(2, 4).zero_()
0  0  0  0
0  0  0  0
[torch.IntTensor of size 2x4]

The contents of a tensor can be accessed and modified using Python’s indexing and slicing notation:

>>> x = torch.FloatTensor([[1, 2, 3], [4, 5, 6]])
>>> print(x[1][2])
6.0
>>> x[0][1] = 8
>>> print(x)
 1  8  3
 4  5  6
[torch.FloatTensor of size 2x3]

Each tensor has an associated torch.Storage, which holds its data. The tensor class provides multi-dimensional, strided view of a storage and defines numeric operations on it.

Note

Methods which mutate a tensor are marked with an underscore suffix. For example, torch.FloatTensor.abs_() computes the absolute value in-place and returns the modified tensor, while torch.FloatTensor.abs() computes the result in a new tensor.

class torch.Tensor¶

class torch.Tensor(*sizes)

class torch.Tensor(size)

class torch.Tensor(sequence)

class torch.Tensor(ndarray)

class torch.Tensor(tensor)

class torch.Tensor(storage)

Creates a new tensor from an optional size or data.

If no arguments are given, an empty zero-dimensional tensor is returned. If a numpy.ndarray, torch.Tensor, or torch.Storage is given, a new tensor that shares the same data is returned. If a Python sequence is given, a new tensor is created from a copy of the sequence.

abs() → Tensor¶: See torch.abs()

abs_() → Tensor¶: In-place version of abs()

acos() → Tensor¶: See torch.acos()

acos_() → Tensor¶: In-place version of acos()

add(value) → Tensor¶: See torch.add()

add_(value) → Tensor¶: In-place version of add()

addbmm(beta=1, mat, alpha=1, batch1, batch2) → Tensor¶: See torch.addbmm()

addbmm_(beta=1, mat, alpha=1, batch1, batch2) → Tensor¶: In-place version of addbmm()

addcdiv(value=1, tensor1, tensor2) → Tensor¶: See torch.addcdiv()

addcdiv_(value=1, tensor1, tensor2) → Tensor¶: In-place version of addcdiv()

addcmul(value=1, tensor1, tensor2) → Tensor¶: See torch.addcmul()

addcmul_(value=1, tensor1, tensor2) → Tensor¶: In-place version of addcmul()

addmm(beta=1, mat, alpha=1, mat1, mat2) → Tensor¶: See torch.addmm()

addmm_(beta=1, mat, alpha=1, mat1, mat2) → Tensor¶: In-place version of addmm()

addmv(beta=1, tensor, alpha=1, mat, vec) → Tensor¶: See torch.addmv()

addmv_(beta=1, tensor, alpha=1, mat, vec) → Tensor¶: In-place version of addmv()

addr(beta=1, alpha=1, vec1, vec2) → Tensor¶: See torch.addr()

addr_(beta=1, alpha=1, vec1, vec2) → Tensor¶: In-place version of addr()

apply_(callable) → Tensor¶: Applies the function callable to each element in the tensor, replacing each element with the value returned by callable.

Note

This function only works with CPU tensors and should not be used in code sections that require high performance.

asin() → Tensor¶: See torch.asin()

asin_() → Tensor¶: In-place version of asin()

atan() → Tensor¶: See torch.atan()

atan2(other) → Tensor¶: See torch.atan2()

atan2_(other) → Tensor¶: In-place version of atan2()

atan_() → Tensor¶: In-place version of atan()

baddbmm(beta=1, alpha=1, batch1, batch2) → Tensor¶: See torch.baddbmm()

baddbmm_(beta=1, alpha=1, batch1, batch2) → Tensor¶: In-place version of baddbmm()

bernoulli() → Tensor¶: See torch.bernoulli()

bernoulli_() → Tensor¶: In-place version of bernoulli()

bmm(batch2) → Tensor¶: See torch.bmm()

byte()¶

btrifact(info=None, pivot=True)¶: See torch.btrifact()

btrifact_with_info(pivot=True) -> (Tensor, Tensor, Tensor)¶: See torch.btrifact_with_info()

btrisolve()¶

cauchy_(median=0, sigma=1, *, generator=None) → Tensor¶: Fills the tensor with numbers drawn from the Cauchy distribution:

\[f(x) = \dfrac{1}{\pi} \dfrac{\sigma}{(x - median)^2 + \sigma^2}\]

ceil() → Tensor¶: See torch.ceil()

ceil_() → Tensor¶: In-place version of ceil()

char()¶

chunk(chunks, dim=0) → List of Tensors¶: See torch.chunk()

clamp(min, max) → Tensor¶: See torch.clamp()

clamp_(min, max) → Tensor¶: In-place version of clamp()

clone() → Tensor¶: Returns a copy of the self tensor. The copy has the same size and data type as self.

contiguous() → Tensor¶: Returns a contiguous tensor containing the same data as self tensor. If self tensor is contiguous, this function returns the self tensor.

copy_(src, non_blocking=False) → Tensor¶

Copies the elements from src into self tensor and returns self.

The src tensor must be broadcastable with the self tensor. It may be of a different data type or reside on a different device.

Parameters:	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.

cos() → Tensor¶: See torch.cos()

cos_() → Tensor¶: In-place version of cos()

cosh() → Tensor¶: See torch.cosh()

cosh_() → Tensor¶: In-place version of cosh()

cpu()¶

cross(other, dim=-1) → Tensor¶: See torch.cross()

cuda()¶

cumprod(dim) → Tensor¶: See torch.cumprod()

cumsum(dim) → Tensor¶: See torch.cumsum()

data_ptr() → int¶: Returns the address of the first element of self tensor.

det() → Tensor¶: See torch.det()

diag(diagonal=0) → Tensor¶: See torch.diag()

dim() → int¶: Returns the number of dimensions of self tensor.

dist(other, p=2) → Tensor¶: See torch.dist()

div(value) → Tensor¶: See torch.div()

div_(value) → Tensor¶: In-place version of div()

dot(tensor2) → Tensor¶: See torch.dot()

double()¶

eig(eigenvectors=False) -> (Tensor, Tensor)¶: See torch.eig()

element_size() → int¶

Returns the size in bytes of an individual element.

Example:

>>> torch.FloatTensor().element_size()
4
>>> torch.ByteTensor().element_size()
1

eq(other) → Tensor¶: See torch.eq()

eq_(other) → Tensor¶: In-place version of eq()

equal(other) → bool¶: See torch.equal()

erf() → Tensor¶: See torch.erf()

erf_()¶

erfinv() → Tensor¶: See torch.erfinv()

erfinv_()¶

exp() → Tensor¶: See torch.exp()

exp_() → Tensor¶: In-place version of exp()

expm1() → Tensor¶: See torch.expm1()

expm1_() → Tensor¶: In-place version of expm1()

expand(*sizes) → Tensor¶

Returns a new view of the 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.

Parameters:	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 (3,4)]

>>> 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 (3,4)]

expand_as(tensor)¶

exponential_(lambd=1, *, generator=None) → Tensor¶: Fills self tensor with elements drawn from the exponential distribution:

\[f(x) = \lambda e^{-\lambda x}\]

fill_(value) → Tensor¶: Fills self tensor with the specified value.

float()¶

floor() → Tensor¶: See torch.floor()

floor_() → Tensor¶: In-place version of floor()

fmod(divisor) → Tensor¶: See torch.fmod()

fmod_(divisor) → Tensor¶: In-place version of fmod()

frac() → Tensor¶: See torch.frac()

frac_() → Tensor¶: In-place version of frac()

gather(dim, index) → Tensor¶: See torch.gather()

ge(other) → Tensor¶: See torch.ge()

ge_(other) → Tensor¶: In-place version of ge()

gels(A) → Tensor¶: See torch.gels()

geometric_(p, *, generator=None) → Tensor¶: Fills self tensor with elements drawn from the geometric distribution:

\[f(X=k) = (1 - p)^{k - 1} p\]

geqrf() -> (Tensor, Tensor)¶: See torch.geqrf()

ger(vec2) → Tensor¶: See torch.ger()

gesv(A) → Tensor, Tensor¶: See torch.gesv()

gt(other) → Tensor¶: See torch.gt()

gt_(other) → Tensor¶: In-place version of gt()

half()¶

histc(bins=100, min=0, max=0) → Tensor¶: See torch.histc()

index(m) → Tensor¶

Selects elements from self tensor using a binary mask or along a given dimension. The expression tensor.index(m) is equivalent to tensor[m].

Parameters:	m (int or ByteTensor or slice) – the dimension or mask used to select elements

index_add_(dim, index, tensor) → Tensor¶

Accumulate the elements of tensor into the self tensor by adding to the indices in the order given in index. For example, if dim == 0 and index[i] == j, then the ith row of tensor is added to the jth row of self.

The dimth dimension of tensor must have the same size as the length of index (which must be a vector), and all other dimensions must match self, or an error will be raised.

Parameters:	dim (int) – dimension along which to index index (LongTensor) – indices of `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 (5,3)]

index_copy_(dim, index, tensor) → Tensor¶

Copies the elements of tensor into the self tensor by selecting the indices in the order given in index. For example, if dim == 0 and index[i] == j, then the ith row of tensor is copied to the jth row of self.

The dimth dimension of tensor must have the same size as the length of index (which must be a vector), and all other dimensions must match self, or an error will be raised.

Parameters:	dim (int) – dimension along which to index index (LongTensor) – indices of `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 (5,3)]

index_fill_(dim, index, val) → Tensor¶

Fills the elements of the self tensor with value val by selecting the indices in the order given in index.

Parameters:	dim (int) – dimension along which to index index (LongTensor) – indices of `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 (3,3)]

index_select(dim, index) → Tensor¶: See torch.index_select()

int()¶

inverse() → Tensor¶: See torch.inverse()

is_contiguous() → bool¶: Returns True if self tensor is contiguous in memory in C order.

is_cuda¶

is_pinned()¶: Returns true if this tensor resides in pinned memory

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.

is_signed()¶

kthvalue(k, dim=None, keepdim=False) -> (Tensor, LongTensor)¶: See torch.kthvalue()

le(other) → Tensor¶: See torch.le()

le_(other) → Tensor¶: In-place version of le()

lerp(start, end, weight) → Tensor¶: See torch.lerp()

lerp_(start, end, weight) → Tensor¶: In-place version of lerp()

log() → Tensor¶: See torch.log()

logdet() → Tensor¶: See torch.logdet()

log1p() → Tensor¶: See torch.log1p()

log1p_() → Tensor¶: In-place version of log1p()

log_() → Tensor¶: In-place version of log()

log_normal_(mean=1, std=2, *, generator=None)¶: Fills self tensor with numbers samples from the log-normal distribution parameterized by the given mean (µ) and standard deviation (σ). Note that mean and stdv are the mean and standard deviation of the underlying normal distribution, and not of the returned distribution:

\[f(x) = \dfrac{1}{x \sigma \sqrt{2\pi}}\ e^{-\dfrac{(\ln x - \mu)^2}{2\sigma^2}}\]

long()¶

lt(other) → Tensor¶: See torch.lt()

lt_(other) → Tensor¶: In-place version of lt()

map_(tensor, callable)¶

Applies callable for each element in self tensor and the given tensor and stores the results in self tensor. self tensor and the given tensor must be broadcastable.

The callable should have the signature:

def callable(a, b) -> number

masked_scatter_(mask, source)¶

Copies elements from source into self tensor at positions where the mask is one. The shape of mask must be broadcastable with the shape of the underlying tensor. The source should have at least as many elements as the number of ones in mask

Parameters:	mask (ByteTensor) – the binary mask source (Tensor) – the tensor to copy from

Note

The mask operates on the self tensor, not on the given source tensor.

masked_fill_(mask, value)¶

Fills elements of self tensor with value where mask is one. The shape of mask must be broadcastable with the shape of the underlying tensor.

Parameters:	mask (ByteTensor) – the binary mask value (float) – the value to fill in with

masked_select(mask) → Tensor¶: See torch.masked_select()

matmul(tensor2) → Tensor¶: See torch.matmul()

max(dim=None, keepdim=False) -> Tensor or (Tensor, Tensor)¶: See torch.max()

mean(dim=None, keepdim=False) -> Tensor or (Tensor, Tensor)¶: See torch.mean()

median(dim=None, keepdim=False) -> (Tensor, LongTensor)¶: See torch.median()

min(dim=None, keepdim=False) -> Tensor or (Tensor, Tensor)¶: See torch.min()

mm(mat2) → Tensor¶: See torch.mm()

mode(dim=None, keepdim=False) -> (Tensor, LongTensor)¶: See torch.mode()

mul(value) → Tensor¶: See torch.mul()

mul_(value)¶: In-place version of mul()

multinomial(num_samples, replacement=False, *, generator=None) → Tensor¶: See torch.multinomial()

mv(vec) → Tensor¶: See torch.mv()

narrow(dimension, start, length) → Tensor¶

Returns a new tensor that is a narrowed version of self tensor. The dimension dim is narrowed from start to start + length. The returned tensor and self tensor share the same underlying storage.

Parameters:	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 (2,3)]

>>> x.narrow(1, 1, 2)

 2  3
 5  6
 8  9
[torch.FloatTensor of size (3,2)]

ndimension() → int¶: Alias for dim()

ne(other) → Tensor¶: See torch.ne()

ne_(other) → Tensor¶: In-place version of ne()

neg() → Tensor¶: See torch.neg()

neg_() → Tensor¶: In-place version of neg()

nelement() → int¶: Alias for numel()

new()¶

nonzero() → LongTensor¶: See torch.nonzero()

norm(p=2, dim=None, keepdim=False) → Tensor¶: See torch.norm()

normal_(mean=0, std=1, *, generator=None) → Tensor¶: Fills self tensor with elements samples from the normal distribution parameterized by mean and std.

numel() → int¶: See torch.numel()

numpy() → numpy.ndarray¶: Returns self tensor as a NumPy ndarray. This tensor and the returned ndarray share the same underlying storage. Changes to self tensor will be reflected in the ndarray and vice versa.

orgqr(input2) → Tensor¶: See torch.orgqr()

ormqr(input2, input3, left=True, transpose=False) → Tensor¶: See torch.ormqr()

permute()¶

pin_memory()¶

potrf(upper=True) → Tensor¶: See torch.potrf()

potri(upper=True) → Tensor¶: See torch.potri()

potrs(input2, upper=True) → Tensor¶: See torch.potrs()

pow(exponent) → Tensor¶: See torch.pow()

pow_(exponent) → Tensor¶: In-place version of pow()

prod(dim=None, keepdim=False) → Tensor¶: See torch.prod()

pstrf(upper=True, tol=-1) -> (Tensor, IntTensor)¶: See torch.pstrf()

put_(indices, tensor, accumulate=False) → Tensor¶

Copies the elements from tensor into the positions specified by indices. For the purpose of indexing, the self tensor is treated as if it were a 1-D tensor.

If accumulate is True, the elements in tensor are added to self. If accumulate is False, the behavior is undefined if indices contains duplicate elements.

Parameters:	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 (2,3)]

qr() -> (Tensor, Tensor)¶: See torch.qr()

random_(from=0, to=None, *, generator=None) → Tensor¶: Fills self tensor with numbers sampled from the discrete uniform distribution over [from, to - 1]. If not specified, the values are usually only bounded by 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].

reciprocal() → Tensor¶: See torch.reciprocal()

reciprocal_() → Tensor¶: In-place version of reciprocal()

remainder(divisor) → Tensor¶: See torch.remainder()

remainder_(divisor) → Tensor¶: In-place version of remainder()

renorm(p, dim, maxnorm) → Tensor¶: See torch.renorm()

renorm_(p, dim, maxnorm) → Tensor¶: In-place version of renorm()

repeat(*sizes) → Tensor¶

Repeats this tensor along the specified dimensions.

Unlike expand(), this function copies the tensor’s data.

Parameters:	sizes (torch.Size or int...) – The number of times to repeat this tensor along each dimension

Example:

>>> x = torch.Tensor([1, 2, 3])
>>> x.repeat(4, 2)

 1  2  3  1  2  3
 1  2  3  1  2  3
 1  2  3  1  2  3
 1  2  3  1  2  3
[torch.FloatTensor of size (4,6)]

>>> x.repeat(4, 2, 1).size()

torch.Size([4, 2, 3])

reshape(*shape) → Tensor¶

Returns a tensor with the same data and number of elements as self, but with the specified shape.

Parameters:	shape (tuple of python:ints or int...) – the desired shape

See torch.reshape()

resize_(*sizes) → Tensor¶

Resizes 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.

Parameters:	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 (2,2)]

resize_as_(tensor) → Tensor¶: Resizes the self tensor to be the same size as the specified tensor. This is equivalent to self.resize_(tensor.size()).

round() → Tensor¶: See torch.round()

round_() → Tensor¶: In-place version of round()

rsqrt() → Tensor¶: See torch.rsqrt()

rsqrt_() → Tensor¶: In-place version of rsqrt()

scatter_(dim, index, src) → Tensor¶

Writes all values from the tensor src into self at the indices specified in the index tensor. For each value in src, its output index is specified by its index in src for dimension != dim and by the corresponding value in index for dimension = dim.

For a 3-D tensor, 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 gather().

self, index and 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 gather(), the values of index must be between 0 and (self.size(dim) -1) inclusive, and all values in a row along the specified dimension dim must be unique.

Parameters:	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 (2,5)]

>>> 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 (3,5)]

>>> 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 (2,4)]

select(dim, index) → Tensor¶

Slices the self tensor along the selected dimension at the given index. This function returns a tensor with the given dimension removed.

Parameters:	dim (int) – the dimension to slice index (int) – the index to select with

Note

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].

set_(source=None, storage_offset=0, size=None, stride=None) → Tensor¶

Sets the underlying storage, size, and strides. If source is a tensor, self tensor will share the same storage and have the same size and strides as source. Changes to elements in one tensor will be reflected in the other.

If source is a Storage, the method sets the underlying storage, offset, size, and stride.

Parameters:	source (Tensor or Storage) – the tensor or storage to use storage_offset (int, optional) – the offset in the storage size (torch.Size, optional) – the desired size. Defaults to the size of the source. stride (tuple, optional) – the desired stride. Defaults to C-contiguous strides.

share_memory_()¶

Moves the underlying storage to shared memory.

This is a no-op if the underlying storage is already in shared memory and for CUDA tensors. Tensors in shared memory cannot be resized.

short()¶

sigmoid() → Tensor¶: See torch.sigmoid()

sigmoid_() → Tensor¶: In-place version of sigmoid()

sign() → Tensor¶: See torch.sign()

sign_() → Tensor¶: In-place version of sign()

sin() → Tensor¶: See torch.sin()

sin_() → Tensor¶: In-place version of sin()

sinh() → Tensor¶: See torch.sinh()

sinh_() → Tensor¶: In-place version of sinh()

size() → torch.Size¶

Returns the size of the self tensor. The returned value is a subclass of tuple.

Example:

>>> torch.Tensor(3, 4, 5).size()
torch.Size([3, 4, 5])

slogdet() -> (Tensor, Tensor)¶: See torch.slogdet()

sort(dim=None, descending=False) -> (Tensor, LongTensor)¶: See torch.sort()

split(split_size, dim=0)¶: See torch.split()

sqrt() → Tensor¶: See torch.sqrt()

sqrt_() → Tensor¶: In-place version of sqrt()

squeeze(dim=None) → Tensor¶: See torch.squeeze()

squeeze_(dim=None) → Tensor¶: In-place version of squeeze()

std(dim=None, unbiased=True, keepdim=False) → Tensor¶: See torch.std()

storage() → torch.Storage¶: Returns the underlying storage

storage_offset() → int¶

Returns 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

storage_type()¶

stride(dim) → tuple or int¶

Returns the stride of self tensor.

Stride is the jump necessary to go from one element to the next one in the specified dimension 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 dim.

Parameters:	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

sub(value, other) → Tensor¶

Subtracts a scalar or tensor from self tensor. If both value and other are specified, each element of other is scaled by value before being used.

When other is a tensor, the shape of other must be broadcastable with the shape of the underlying tensor.

sub_(x) → Tensor¶: In-place version of sub()

sum(dim=None, keepdim=False) → Tensor¶: See torch.sum()

svd(some=True) -> (Tensor, Tensor, Tensor)¶: See torch.svd()

symeig(eigenvectors=False, upper=True) -> (Tensor, Tensor)¶: See torch.symeig()

t() → Tensor¶: See torch.t()

t_() → Tensor¶: In-place version of t()

take(indices) → Tensor¶: See torch.take()

tan()¶

tan_() → Tensor¶: In-place version of tan()

tanh() → Tensor¶: See torch.tanh()

tanh_() → Tensor¶: In-place version of tanh()

tolist()¶

topk(k, dim=None, largest=True, sorted=True) -> (Tensor, LongTensor)¶: See torch.topk()

trace() → Tensor¶: See torch.trace()

transpose(dim0, dim1) → Tensor¶: See torch.transpose()

transpose_(dim0, dim1) → Tensor¶: In-place version of transpose()

tril(k=0) → Tensor¶: See torch.tril()

tril_(k=0) → Tensor¶: In-place version of tril()

triu(k=0) → Tensor¶: See torch.triu()

triu_(k=0) → Tensor¶: In-place version of triu()

trtrs(A, upper=True, transpose=False, unitriangular=False) -> (Tensor, Tensor)¶: See torch.trtrs()

trunc() → Tensor¶: See torch.trunc()

trunc_() → Tensor¶: In-place version of trunc()

type(dtype=None, non_blocking=False, **kwargs) → str or Tensor¶

Returns the type if dtype is not provided, else casts this object to the specified type.

If this is already of the correct type, no copy is performed and the original object is returned.

Parameters:

Parameters:	dtype (type or string) – The desired type non_blocking (bool) – If `True`, and the source is in pinned memory and destination is on the GPU or vice versa, the copy is performed asynchronously with respect to the host. Otherwise, the argument has no effect. **kwargs – For compatibility, may contain the key `async` in place of the `non_blocking` argument. The `async` arg is deprecated.

dtype (type or string) – The desired type
non_blocking (bool) – If True, and the source is in pinned memory and destination is on the GPU or vice versa, the copy is performed asynchronously with respect to the host. Otherwise, the argument has no effect.
**kwargs – For compatibility, may contain the key async in place of the non_blocking argument. The async arg is deprecated.

type_as(tensor) → Tensor¶

Returns this tensor cast to the type of the given tensor.

This is a no-op if the tensor is already of the correct type. This is equivalent to:

self.type(tensor.type())

Params:: tensor (Tensor): the tensor which has the desired type

unfold(dim, size, step) → Tensor¶

Returns a tensor which contains all slices of size size from self tensor in the dimension dim.

Step between two slices is given by step.

If sizedim is the size of dimension dim for self, the size of dimension dim in the returned tensor will be (sizedim - size) / step + 1.

An additional dimension of size size is appended in the returned tensor.

Parameters:	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 (6,2)]

>>> x.unfold(0, 2, 2)

 1  2
 3  4
 5  6
[torch.FloatTensor of size (3,2)]

uniform_(from=0, to=1) → Tensor¶: Fills self tensor with numbers sampled from the continuous uniform distribution:

\[P(x) = \dfrac{1}{\text{to} - \text{from}}\]

unique(sorted=False, return_inverse=False)¶

Returns the unique scalar elements of the tensor as a 1-D tensor.

See torch.unique()

unsqueeze(dim) → Tensor¶: See torch.unsqueeze()

unsqueeze_(dim) → Tensor¶: In-place version of unsqueeze()

var(dim=None, unbiased=True, keepdim=False) → Tensor¶: See torch.var()

view(*args) → Tensor¶

Returns a new tensor with the same data as the 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 \(d, d+1, \dots, d+k\) that satisfy the following contiguity-like condition that \(\forall i = 0, \dots, k-1\),

\[stride[i] = stride[i+1] \times size[i+1]\]

Otherwise, contiguous() needs to be called before the tensor can be viewed.

Parameters:	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])

view_as(tensor)¶

zero_() → Tensor¶: Fills self tensor with zeros.

class torch.ByteTensor¶

The following methods are unique to torch.ByteTensor.

all() → bool¶: Returns True if all elements in the tensor are non-zero, False otherwise.

any() → bool¶: Returns True if any elements in the tensor are non-zero, False otherwise.