Tensor objects

Central to torch is the torch_tensor objects. torch_tensor’s are R objects very similar to R6 instances. Tensors have a large amount of methods that can be called using the $ operator.

Following is a list of all methods that can be called by tensor objects and their documentation. You can also look at PyTorch’s documentation for additional details.

T

Is this Tensor with its dimensions reversed.

If n is the number of dimensions in x, x$T is equivalent to x$permute(n-1, n-2, ..., 0).

abs

abs() -> Tensor

See ?torch_abs()

abs_

abs_() -> Tensor

In-place version of $abs

absolute

absolute() -> Tensor

Alias for [$abs()]

absolute_

absolute_() -> Tensor

In-place version of $absolute Alias for [$abs_()]

acos

acos() -> Tensor

See ?torch_acos()

acos_

acos_() -> Tensor

In-place version of $acos

acosh

acosh() -> Tensor

See ?torch_acosh()

acosh_

acosh_() -> Tensor

In-place version of $acosh

add

add(other, *, alpha=1) -> Tensor

Add a scalar or tensor to self tensor. If both alpha and other are specified, each element of other is scaled by alpha before being used.

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

See ?torch_add()

add_

add_(other, *, alpha=1) -> Tensor

In-place version of $add

addbmm

addbmm(batch1, batch2, *, beta=1, alpha=1) -> Tensor

See ?torch_addbmm()

addbmm_

addbmm_(batch1, batch2, *, beta=1, alpha=1) -> Tensor

In-place version of $addbmm

addcdiv

addcdiv(tensor1, tensor2, *, value=1) -> Tensor

See ?torch_addcdiv()

addcdiv_

addcdiv_(tensor1, tensor2, *, value=1) -> Tensor

In-place version of $addcdiv

addcmul

addcmul(tensor1, tensor2, *, value=1) -> Tensor

See ?torch_addcmul()

addcmul_

addcmul_(tensor1, tensor2, *, value=1) -> Tensor

In-place version of $addcmul

addmm

addmm(mat1, mat2, *, beta=1, alpha=1) -> Tensor

See ?torch_addmm()

addmm_

addmm_(mat1, mat2, *, beta=1, alpha=1) -> Tensor

In-place version of $addmm

addmv

addmv(mat, vec, *, beta=1, alpha=1) -> Tensor

See ?torch_addmv()

addmv_

addmv_(mat, vec, *, beta=1, alpha=1) -> Tensor

In-place version of $addmv

addr

addr(vec1, vec2, *, beta=1, alpha=1) -> Tensor

See ?torch_addr()

addr_

addr_(vec1, vec2, *, beta=1, alpha=1) -> Tensor

In-place version of $addr

align_as

align_as(other) -> Tensor

Permutes the dimensions of the self tensor to match the dimension order in the other tensor, adding size-one dims for any new names.

This operation is useful for explicit broadcasting by names (see examples).

All of the dims of self must be named in order to use this method. The resulting tensor is a view on the original tensor.

All dimension names of self must be present in other$names. other may contain named dimensions that are not in self$names; the output tensor has a size-one dimension for each of those new names.

To align a tensor to a specific order, use $align_to.

Examples:

# Example 1: Applying a mask
mask <- torch_randint(low = 0, high = 2, size = c(127, 128), dtype=torch_bool())$refine_names(c('W', 'H'))
imgs <- torch_randn(32, 128, 127, 3, names=c('N', 'H', 'W', 'C'))
imgs$masked_fill_(mask$align_as(imgs), 0)

# Example 2: Applying a per-channel-scale
scale_channels <- function(input, scale) {
  scale <- scale$refine_names("C")
  input * scale$align_as(input)
}

num_channels <- 3
scale <- torch_randn(num_channels, names='C')
imgs <- torch_rand(32, 128, 128, num_channels, names=c('N', 'H', 'W', 'C'))
more_imgs = torch_rand(32, num_channels, 128, 128, names=c('N', 'C', 'H', 'W'))
videos = torch_randn(3, num_channels, 128, 128, 128, names=c('N', 'C', 'H', 'W', 'D'))

# scale_channels is agnostic to the dimension order of the input
scale_channels(imgs, scale)
scale_channels(more_imgs, scale)
scale_channels(videos, scale)

Warning:

The named tensor API is experimental and subject to change.

align_to

Permutes the dimensions of the self tensor to match the order specified in names, adding size-one dims for any new names.

All of the dims of self must be named in order to use this method. The resulting tensor is a view on the original tensor.

All dimension names of self must be present in names. 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.

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 self.

Examples:

Warning:

The named tensor API is experimental and subject to change.

all

all() -> bool

Returns TRUE if all elements in the tensor are TRUE, FALSE otherwise.

Examples:

a <- torch_rand(1, 2)$to(dtype = torch_bool())
a
a$all()

all(dim, keepdim=FALSE, out=NULL) -> Tensor

Returns TRUE if all elements in each row of the tensor in the given dimension dim are TRUE, FALSE otherwise.

If keepdim is TRUE, the output tensor is of the same size as input except in the dimension dim where it is of size 1. Otherwise, dim is squeezed (see ?torch_squeeze()), resulting in the output tensor having 1 fewer dimension than input.

Arguments:

dim (int): the dimension to reduce
keepdim (bool): whether the output tensor has dim retained or not
out (Tensor, optional): the output tensor

Examples:

a <- torch_rand(4, 2)$to(dtype = torch_bool())
a
a$all(dim=2)
a$all(dim=1)

allclose

allclose(other, rtol=1e-05, atol=1e-08, equal_nan=FALSE) -> Tensor

See ?torch_allclose()

angle

angle() -> Tensor

See ?torch_angle()

any

any() -> bool

Returns TRUE if any elements in the tensor are TRUE, FALSE otherwise.

Examples:

a <- torch_rand(1, 2)$to(dtype = torch_bool())
a
a$any()

any(dim, keepdim=FALSE, out=NULL) -> Tensor

Returns TRUE if any elements in each row of the tensor in the given dimension dim are TRUE, FALSE otherwise.

Arguments:

dim (int): the dimension to reduce
keepdim (bool): whether the output tensor has dim retained or not
out (Tensor, optional): the output tensor

Examples:

a <- torch_randn(4, 2) < 0
a
a$any(2)
a$any(1)

apply_

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.

argmax

argmax(dim=NULL, keepdim=FALSE) -> LongTensor

See ?torch_argmax()

argmin

argmin(dim=NULL, keepdim=FALSE) -> LongTensor

See ?torch_argmin()

argsort

argsort(dim=-1, descending=FALSE) -> LongTensor

See ?torch_argsort()

as_strided

as_strided(size, stride, storage_offset=0) -> Tensor

See [torch_as_strided()]

as_subclass

as_subclass(cls) -> Tensor

Makes a cls instance with the same data pointer as self. Changes in the output mirror changes in self, and the output stays attached to the autograd graph. cls must be a subclass of Tensor.

asin

asin() -> Tensor

See ?torch_asin()

asin_

asin_() -> Tensor

In-place version of $asin

asinh

asinh() -> Tensor

See ?torch_asinh()

asinh_

asinh_() -> Tensor

In-place version of $asinh

atan

atan() -> Tensor

See ?torch_atan()

atan2

atan2(other) -> Tensor

See [torch_atan2()]

atan2_

atan2_(other) -> Tensor

In-place version of $atan2

atan_

atan_() -> Tensor

In-place version of $atan

atanh

atanh() -> Tensor

See ?torch_atanh()

atanh_

In-place version of $atanh

backward

Computes the gradient of current tensor w.r.t. graph leaves.

The graph is differentiated using the chain rule. If the tensor is non-scalar (i.e. its data has more than one element) and requires gradient, the function additionally requires specifying gradient. It should be a tensor of matching type and location, that contains the gradient of the differentiated function w.r.t. self.

This function accumulates gradients in the leaves - you might need to zero $grad attributes or set them to NULL before calling it. See Default gradient layouts<default-grad-layouts> for details on the memory layout of accumulated gradients.

Arguments:

gradient (Tensor or NULL): Gradient w.r.t. the tensor. If it is a tensor, it will be automatically converted to a Tensor that does not require grad unless create_graph is TRUE. NULL values can be specified for scalar Tensors or ones that don’t require grad. If a NULL value would be acceptable then this argument is optional.
retain_graph (bool, optional): If FALSE, the graph used to compute the grads will be freed. Note that in nearly all cases setting this option to TRUE is not needed and often can be worked around in a much more efficient way. Defaults to the value of create_graph.
create_graph (bool, optional): If TRUE, graph of the derivative will be constructed, allowing to compute higher order derivative products. Defaults to FALSE.

baddbmm

baddbmm(batch1, batch2, *, beta=1, alpha=1) -> Tensor

See ?torch_baddbmm()

baddbmm_

baddbmm_(batch1, batch2, *, beta=1, alpha=1) -> Tensor

In-place version of $baddbmm

bernoulli

bernoulli(*, generator=NULL) -> Tensor

Returns a result tensor where each \texttt{result[i]} is independently sampled from \text{Bernoulli}(\texttt{self[i]}). self must have floating point dtype, and the result will have the same dtype.

See ?torch_bernoulli()

bernoulli_

bernoulli_(p=0.5, *, generator=NULL) -> Tensor

Fills each location of self with an independent sample from \text{Bernoulli}(\texttt{p}). self can have integral dtype.

bernoulli_(p_tensor, *, generator=NULL) -> Tensor

p_tensor should be a tensor containing probabilities to be used for drawing the binary random number.

The \text{i}^{th} element of self tensor will be set to a value sampled from \text{Bernoulli}(\texttt{p\_tensor[i]}).

self can have integral dtype, but p_tensor must have floating point dtype.

See also $bernoulli and ?torch_bernoulli()

bfloat16

bfloat16(memory_format=torch_preserve_format) -> Tensor self$bfloat16() is equivalent to self$to(torch_bfloat16). See [to()].

Arguments:

memory_format (torch_memory_format, optional): the desired memory format of
returned Tensor. Default: torch_preserve_format.

bincount

bincount(weights=NULL, minlength=0) -> Tensor

See ?torch_bincount()

bitwise_and

bitwise_and() -> Tensor

See [torch_bitwise_and()]

bitwise_and_

bitwise_and_() -> Tensor

In-place version of $bitwise_and

bitwise_not

bitwise_not() -> Tensor

See [torch_bitwise_not()]

bitwise_not_

bitwise_not_() -> Tensor

In-place version of $bitwise_not

bitwise_or

bitwise_or() -> Tensor

See [torch_bitwise_or()]

bitwise_or_

bitwise_or_() -> Tensor

In-place version of $bitwise_or

bitwise_xor

bitwise_xor() -> Tensor

See [torch_bitwise_xor()]

bitwise_xor_

bitwise_xor_() -> Tensor

In-place version of $bitwise_xor

bmm

bmm(batch2) -> Tensor

See ?torch_bmm()

bool

bool(memory_format=torch_preserve_format) -> Tensor

self$bool() is equivalent to self$to(torch_bool). See [to()].

Arguments:

memory_format (torch_memory_format, optional): the desired memory format of
returned Tensor. Default: torch_preserve_format.

byte

byte(memory_format=torch_preserve_format) -> Tensor

self$byte() is equivalent to self$to(torch_uint8). See [to()].

Arguments:

memory_format (torch_memory_format, optional): the desired memory format of
returned Tensor. Default: torch_preserve_format.

cauchy_

cauchy_(median=0, sigma=1, *, generator=NULL) -> Tensor

Fills the tensor with numbers drawn from the Cauchy distribution:

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

ceil

ceil() -> Tensor

See ?torch_ceil()

ceil_

ceil_() -> Tensor

In-place version of $ceil

char

char(memory_format=torch_preserve_format) -> Tensor

self$char() is equivalent to self$to(torch_int8). See [to()].

Arguments:

memory_format (torch_memory_format, optional): the desired memory format of
returned Tensor. Default: torch_preserve_format.

cholesky

cholesky(upper=FALSE) -> Tensor

See ?torch_cholesky()

cholesky_inverse

cholesky_inverse(upper=FALSE) -> Tensor

See [torch_cholesky_inverse()]

cholesky_solve

cholesky_solve(input2, upper=FALSE) -> Tensor

See [torch_cholesky_solve()]

chunk

chunk(chunks, dim=0) -> List of Tensors

See ?torch_chunk()

clamp

clamp(min, max) -> Tensor

See ?torch_clamp()

clamp_

clamp_(min, max) -> Tensor

In-place version of $clamp

clone

clone(memory_format=torch_preserve_format) -> Tensor

Returns a copy of the self tensor. The copy has the same size and data type as self.

Note:

Unlike copy_(), this function is recorded in the computation graph. Gradients propagating to the cloned tensor will propagate to the original tensor.

Arguments:

memory_format (torch_memory_format, optional): the desired memory format of
returned Tensor. Default: torch_preserve_format.

conj

conj() -> Tensor

See ?torch_conj()

contiguous

contiguous(memory_format=torch_contiguous_format) -> Tensor

Returns a contiguous in memory tensor containing the same data as self tensor. If self tensor is already in the specified memory format, this function returns the self tensor.

Arguments:

memory_format (torch_memory_format, optional): the desired memory format of
returned Tensor. Default: torch_contiguous_format.

copy_

copy_(src, non_blocking=FALSE) -> Tensor

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

The src tensor must be :ref:broadcastable <broadcasting-semantics> with the self tensor. It may be of a different data type or reside on a different device.

Arguments:

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

cos() -> Tensor

See ?torch_cos()

cos_

cos_() -> Tensor

In-place version of $cos

cosh

cosh() -> Tensor

See ?torch_cosh()

cosh_

cosh_() -> Tensor

In-place version of $cosh

cpu

cpu(memory_format=torch_preserve_format) -> Tensor

Returns a copy of this object in CPU memory.

If this object is already in CPU memory and on the correct device, then no copy is performed and the original object is returned.

Arguments:

memory_format (torch_memory_format, optional): the desired memory format of
returned Tensor. Default: torch_preserve_format.

cross

cross(other, dim=-1) -> Tensor

See ?torch_cross()

cuda

cuda(device=NULL, non_blocking=FALSE, memory_format=torch_preserve_format) -> Tensor

Returns a copy of this object in CUDA memory.

If this object is already in CUDA memory and on the correct device, then no copy is performed and the original object is returned.

Arguments:

device (torch_device): The destination GPU device. Defaults to the current CUDA device.
non_blocking (bool): If TRUE and the source is in pinned memory, the copy will be asynchronous with respect to the host. Otherwise, the argument has no effect. Default: FALSE.
memory_format (torch_memory_format, optional): the desired memory format of
returned Tensor. Default: torch_preserve_format.

cummax

cummax(dim) -> (Tensor, Tensor)

See ?torch_cummax()

cummin

cummin(dim) -> (Tensor, Tensor)

See ?torch_cummin()

cumprod

cumprod(dim, dtype=NULL) -> Tensor

See ?torch_cumprod()

cumsum

cumsum(dim, dtype=NULL) -> Tensor

See ?torch_cumsum()

data_ptr

data_ptr() -> int

Returns the address of the first element of self tensor.

deg2rad

deg2rad() -> Tensor

See [torch_deg2rad()]

deg2rad_

deg2rad_() -> Tensor

In-place version of $deg2rad

dense_dim

dense_dim() -> int

If self is a sparse COO tensor (i.e., with torch_sparse_coo layout), this returns the number of dense dimensions. Otherwise, this throws an error.

dequantize

dequantize() -> Tensor

Given a quantized Tensor, dequantize it and return the dequantized float Tensor.

det

det() -> Tensor

See ?torch_det()

detach

Returns a new Tensor, detached from the current graph.

The result will never require gradient.

Note:

Returned Tensor shares the same storage with the original one. In-place modifications on either of them will be seen, and may trigger errors in correctness checks. IMPORTANT NOTE: Previously, in-place size / stride / storage changes (such as resize_ / resize_as_ / set_ / transpose_) to the returned tensor also update the original tensor. Now, these in-place changes will not update the original tensor anymore, and will instead trigger an error. For sparse tensors: In-place indices / values changes (such as zero_ / copy_ / add_) to the returned tensor will not update the original tensor anymore, and will instead trigger an error.

detach_

Detaches the Tensor from the graph that created it, making it a leaf. Views cannot be detached in-place.

device

Is the torch_device where this Tensor is.

diag

diag(diagonal=0) -> Tensor

See ?torch_diag()

diag_embed

diag_embed(offset=0, dim1=-2, dim2=-1) -> Tensor

See [torch_diag_embed()]

diagflat

diagflat(offset=0) -> Tensor

See ?torch_diagflat()

diagonal

diagonal(offset=0, dim1=0, dim2=1) -> Tensor

See ?torch_diagonal()

digamma

digamma() -> Tensor

See ?torch_digamma()

digamma_

digamma_() -> Tensor

In-place version of $digamma

dim

dim() -> int

Returns the number of dimensions of self tensor.

dist

dist(other, p=2) -> Tensor

See ?torch_dist()

div

div(value) -> Tensor

See ?torch_div()

div_

div_(value) -> Tensor

In-place version of $div

dot

dot(tensor2) -> Tensor

See ?torch_dot()

double

double(memory_format=torch_preserve_format) -> Tensor

self$double() is equivalent to self$to(torch_float64). See [to()].

Arguments:

memory_format (torch_memory_format, optional): the desired memory format of
returned Tensor. Default: torch_preserve_format.

eig

eig(eigenvectors=FALSE) -> (Tensor, Tensor)

See ?torch_eig()

element_size

element_size() -> int

Returns the size in bytes of an individual element.

Examples:

torch_tensor(c(1))$element_size()

eq

eq(other) -> Tensor

See ?torch_eq()

eq_

eq_(other) -> Tensor

In-place version of $eq

equal

equal(other) -> bool

See ?torch_equal()

erf

erf() -> Tensor

See ?torch_erf()

erf_

erf_() -> Tensor

In-place version of $erf

erfc

erfc() -> Tensor

See ?torch_erfc()

erfc_

erfc_() -> Tensor

In-place version of $erfc

erfinv

erfinv() -> Tensor

See ?torch_erfinv()

erfinv_

erfinv_() -> Tensor

In-place version of $erfinv

exp

exp() -> Tensor

See ?torch_exp()

exp_

exp_() -> Tensor

In-place version of $exp

expand

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.

Arguments:

sizes (torch_Size or int…): the desired expanded size

Warning:

More than one element of an expanded tensor may refer to a single memory location. As a result, in-place operations (especially ones that are vectorized) may result in incorrect behavior. If you need to write to the tensors, please clone them first.

Examples:

x <- torch_tensor(matrix(c(1,2,3), ncol = 1))
x$size()
x$expand(c(3, 4))
x$expand(c(-1, 4))  # -1 means not changing the size of that dimension

expand_as

expand_as(other) -> Tensor

Expand this tensor to the same size as other. self$expand_as(other) is equivalent to self$expand(other.size()).

Please see $expand for more information about expand.

Arguments:

other (`$): The result tensor has the same size
as other.

expm1

expm1() -> Tensor

See [torch_expm1()]

expm1_

expm1_() -> Tensor

In-place version of $expm1

exponential_

exponential_(lambd=1, *, generator=NULL) -> Tensor

Fills self tensor with elements drawn from the exponential distribution:

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

fft

fft(signal_ndim, normalized=FALSE) -> Tensor

See ?torch_fft()

fill_

fill_(value) -> Tensor

Fills self tensor with the specified value.

fill_diagonal_

fill_diagonal_(fill_value, wrap=FALSE) -> Tensor

Fill the main diagonal of a tensor that has at least 2-dimensions. When dims>2, all dimensions of input must be of equal length. This function modifies the input tensor in-place, and returns the input tensor.

Arguments:

fill_value (Scalar): the fill value
wrap (bool): the diagonal ‘wrapped’ after N columns for tall matrices.

Examples:

a <- torch_zeros(3, 3)
a$fill_diagonal_(5)
b <- torch_zeros(7, 3)
b$fill_diagonal_(5)
c <- torch_zeros(7, 3)
c$fill_diagonal_(5, wrap=TRUE)

flatten

flatten(input, start_dim=0, end_dim=-1) -> Tensor

see ?torch_flatten()

flip

flip(dims) -> Tensor

See ?torch_flip()

fliplr

fliplr() -> Tensor

See ?torch_fliplr()

flipud

flipud() -> Tensor

See ?torch_flipud()

float

float(memory_format=torch_preserve_format) -> Tensor

self$float() is equivalent to self$to(torch_float32). See [to()].

Arguments:

memory_format (torch_memory_format, optional): the desired memory format of
returned Tensor. Default: torch_preserve_format.

floor

floor() -> Tensor

See ?torch_floor()

floor_

floor_() -> Tensor

In-place version of $floor

floor_divide

floor_divide(value) -> Tensor

See [torch_floor_divide()]

floor_divide_

floor_divide_(value) -> Tensor

In-place version of $floor_divide

fmod

fmod(divisor) -> Tensor

See ?torch_fmod()

fmod_

fmod_(divisor) -> Tensor

In-place version of $fmod

frac

frac() -> Tensor

See ?torch_frac()

frac_

frac_() -> Tensor

In-place version of $frac

gather

gather(dim, index) -> Tensor

See ?torch_gather()

ge

ge(other) -> Tensor

See ?torch_ge()

ge_

ge_(other) -> Tensor

In-place version of $ge

geometric_

geometric_(p, *, generator=NULL) -> Tensor

Fills self tensor with elements drawn from the geometric distribution:

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

geqrf

geqrf() -> (Tensor, Tensor)

See ?torch_geqrf()

ger

ger(vec2) -> Tensor

See ?torch_ger()

get_device

get_device() -> Device ordinal (Integer)

For CUDA tensors, this function returns the device ordinal of the GPU on which the tensor resides. For CPU tensors, an error is thrown.

Examples:

x <- torch_randn(3, 4, 5, device='cuda:0')
x$get_device()
x$cpu()$get_device()  # RuntimeError: get_device is not implemented for type torch_FloatTensor

grad

This attribute is NULL by default and becomes a Tensor the first time a call to backward computes gradients for self. The attribute will then contain the gradients computed and future calls to [backward()] will accumulate (add) gradients into it.

gt

gt(other) -> Tensor

See ?torch_gt()

gt_

gt_(other) -> Tensor

In-place version of $gt

half

half(memory_format=torch_preserve_format) -> Tensor

self$half() is equivalent to self$to(torch_float16). See [to()].

Arguments:

memory_format (torch_memory_format, optional): the desired memory format of
returned Tensor. Default: torch_preserve_format.

hardshrink

hardshrink(lambd=0.5) -> Tensor

See [torch_nn.functional.hardshrink()]

has_names

Is TRUE if any of this tensor’s dimensions are named. Otherwise, is FALSE.

histc

histc(bins=100, min=0, max=0) -> Tensor

See ?torch_histc()

ifft

ifft(signal_ndim, normalized=FALSE) -> Tensor

See ?torch_ifft()

imag

Returns a new tensor containing imaginary values of the self tensor. The returned tensor and self share the same underlying storage.

Warning:

[imag()] is only supported for tensors with complex dtypes.

Examples:

x <- torch_randn(4, dtype=torch_cfloat())
x
x$imag

index_add

index_add(tensor1, dim, index, tensor2) -> Tensor

Out-of-place version of $index_add_. tensor1 corresponds to self in $index_add_.

index_add_

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 i th row of tensor is added to the j th row of self.

The dim th 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.

Note:

In some circumstances when using the CUDA backend with CuDNN, this operator may select a nondeterministic algorithm to increase performance. If this is undesirable, you can try to make the operation deterministic (potentially at a performance cost) by setting torch_backends.cudnn.deterministic = TRUE.

Arguments:

dim (int): dimension along which to index
index (LongTensor): indices of tensor to select from
tensor (Tensor): the tensor containing values to add

Examples:

x <- torch_ones(5, 3)
t <- torch_tensor(matrix(1:9, ncol = 3), dtype=torch_float())
index <- torch_tensor(c(1L, 4L, 3L))
x$index_add_(1, index, t)

index_copy

index_copy(tensor1, dim, index, tensor2) -> Tensor

Out-of-place version of $index_copy_. tensor1 corresponds to self in $index_copy_.

index_copy_

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 i th row of tensor is copied to the j th row of self.

The dim th 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.

Arguments:

dim (int): dimension along which to index
index (LongTensor): indices of tensor to select from
tensor (Tensor): the tensor containing values to copy

Examples:

x <- torch_zeros(5, 3)
t <- torch_tensor(matrix(1:9, ncol = 3), dtype=torch_float())
index <- torch_tensor(c(1, 5, 3))
x$index_copy_(1, index, t)

index_fill

index_fill(tensor1, dim, index, value) -> Tensor

Out-of-place version of $index_fill_. tensor1 corresponds to self in $index_fill_.

index_fill_

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.

Arguments:

dim (int): dimension along which to index
index (LongTensor): indices of self tensor to fill in
val (float): the value to fill with

Examples:

x <- torch_tensor(matrix(1:9, ncol = 3), dtype=torch_float())
index <- torch_tensor(c(1, 3), dtype = torch_long())
x$index_fill_(1, index, -1)

index_put

index_put(tensor1, indices, value, accumulate=FALSE) -> Tensor

Out-place version of $index_put_. tensor1 corresponds to self in $index_put_.

index_put_

index_put_(indices, value, accumulate=FALSE) -> Tensor

Puts values from the tensor value into the tensor self using the indices specified in indices (which is a tuple of Tensors). The expression tensor.index_put_(indices, value) is equivalent to tensor[indices] = value. Returns self.

If accumulate is TRUE, the elements in value are added to self. If accumulate is FALSE, the behavior is undefined if indices contain duplicate elements.

Arguments:

indices (tuple of LongTensor): tensors used to index into self.
value (Tensor): tensor of same dtype as self.
accumulate (bool): whether to accumulate into self

index_select

index_select(dim, index) -> Tensor

See [torch_index_select()]

indices

indices() -> Tensor

If self is a sparse COO tensor (i.e., with torch_sparse_coo layout), this returns a view of the contained indices tensor. Otherwise, this throws an error.

Note:

This method can only be called on a coalesced sparse tensor. See Tensor.coalesce for details.

int

int(memory_format=torch_preserve_format) -> Tensor

self$int() is equivalent to self$to(torch_int32). See [to()].

Arguments:

memory_format (torch_memory_format, optional): the desired memory format of
returned Tensor. Default: torch_preserve_format.

int_repr

int_repr() -> Tensor

Given a quantized Tensor, self$int_repr() returns a CPU Tensor with uint8_t as data type that stores the underlying uint8_t values of the given Tensor.

inverse

inverse() -> Tensor

See ?torch_inverse()

irfft

irfft(signal_ndim, normalized=FALSE, onesided=TRUE, signal_sizes=NULL) -> Tensor

See ?torch_irfft()

is_complex

is_complex() -> bool

Returns TRUE if the data type of self is a complex data type.

is_contiguous

is_contiguous(memory_format=torch_contiguous_format) -> bool

Returns TRUE if self tensor is contiguous in memory in the order specified by memory format.

Arguments:

memory_format (torch_memory_format, optional): Specifies memory allocation
order. Default: torch_contiguous_format.

is_cuda

Is TRUE if the Tensor is stored on the GPU, FALSE otherwise.

is_floating_point

is_floating_point() -> bool

Returns TRUE if the data type of self is a floating point data type.

is_leaf

All Tensors that have requires_grad which is FALSE will be leaf Tensors by convention.

For Tensors that have requires_grad which is TRUE, they will be leaf Tensors if they were created by the user. This means that they are not the result of an operation and so grad_fn is NULL.

Only leaf Tensors will have their grad populated during a call to [backward()]. To get grad populated for non-leaf Tensors, you can use [retain_grad()].

Examples:

a <- torch_rand(10, requires_grad=TRUE)
a$is_leaf()

# b <- torch_rand(10, requires_grad=TRUE)$cuda()
# b$is_leaf()
# FALSE
# b was created by the operation that cast a cpu Tensor into a cuda Tensor

c <- torch_rand(10, requires_grad=TRUE) + 2
c$is_leaf()
# c was created by the addition operation

# d <- torch_rand(10)$cuda()
# d$is_leaf()
# TRUE
# d does not require gradients and so has no operation creating it (that is tracked by the autograd engine)

# e <- torch_rand(10)$cuda()$requires_grad_()
# e$is_leaf()
# TRUE
# e requires gradients and has no operations creating it

# f <- torch_rand(10, requires_grad=TRUE, device="cuda")
# f$is_leaf
# TRUE
# f requires grad, has no operation creating it

is_meta

Is TRUE if the Tensor is a meta tensor, FALSE otherwise. Meta tensors are like normal tensors, but they carry no data.

is_pinned

Returns true if this tensor resides in pinned memory.

is_quantized

Is TRUE if the Tensor is quantized, FALSE otherwise.

is_set_to

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_shared

Checks if tensor is in shared memory.

This is always TRUE for CUDA tensors.

is_signed

is_signed() -> bool

Returns TRUE if the data type of self is a signed data type.

isclose

isclose(other, rtol=1e-05, atol=1e-08, equal_nan=FALSE) -> Tensor

See ?torch_isclose()

isfinite

isfinite() -> Tensor

See ?torch_isfinite()

isinf

isinf() -> Tensor

See ?torch_isinf()

isnan

isnan() -> Tensor

See ?torch_isnan()

istft

See ?torch_istft() ## item

item() -> number

Returns the value of this tensor as a standard Python number. This only works for tensors with one element. For other cases, see $tolist.

This operation is not differentiable.

Examples:

x <- torch_tensor(1.0)
x$item()

kthvalue

kthvalue(k, dim=NULL, keepdim=FALSE) -> (Tensor, LongTensor)

See ?torch_kthvalue()

le

le(other) -> Tensor

See ?torch_le()

le_

le_(other) -> Tensor

In-place version of $le

lerp

lerp(end, weight) -> Tensor

See ?torch_lerp()

lerp_

lerp_(end, weight) -> Tensor

In-place version of $lerp

lgamma

lgamma() -> Tensor

See ?torch_lgamma()

lgamma_

lgamma_() -> Tensor

In-place version of $lgamma

log

log() -> Tensor

See ?torch_log()

log10

log10() -> Tensor

See [torch_log10()]

log10_

log10_() -> Tensor

In-place version of $log10

log1p

log1p() -> Tensor

See [torch_log1p()]

log1p_

log1p_() -> Tensor

In-place version of $log1p

log2

log2() -> Tensor

See [torch_log2()]

log2_

log2_() -> Tensor

In-place version of $log2

log_

log_() -> Tensor

In-place version of $log

log_normal_

log_normal_(mean=1, std=2, *, generator=NULL)

Fills self tensor with numbers samples from the log-normal distribution parameterized by the given mean \mu and standard deviation \sigma. Note that mean and std are the mean and standard deviation of the underlying normal distribution, and not of the returned distribution:

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

logaddexp

logaddexp(other) -> Tensor

See ?torch_logaddexp()

logaddexp2

logaddexp2(other) -> Tensor

See [torch_logaddexp2()]

logcumsumexp

logcumsumexp(dim) -> Tensor

See ?torch_logcumsumexp()

logdet

logdet() -> Tensor

See ?torch_logdet()

logical_and

logical_and() -> Tensor

See [torch_logical_and()]

logical_and_

logical_and_() -> Tensor

In-place version of $logical_and

logical_not

logical_not() -> Tensor

See [torch_logical_not()]

logical_not_

logical_not_() -> Tensor

In-place version of $logical_not

logical_or

logical_or() -> Tensor

See [torch_logical_or()]

logical_or_

logical_or_() -> Tensor

In-place version of $logical_or

logical_xor

logical_xor() -> Tensor

See [torch_logical_xor()]

logical_xor_

logical_xor_() -> Tensor

In-place version of $logical_xor

logsumexp

logsumexp(dim, keepdim=FALSE) -> Tensor

See ?torch_logsumexp()

long

long(memory_format=torch_preserve_format) -> Tensor

self$long() is equivalent to self$to(torch_int64). See [to()].

Arguments:

memory_format (torch_memory_format, optional): the desired memory format of
returned Tensor. Default: torch_preserve_format.

lstsq

lstsq(A) -> (Tensor, Tensor)

See ?torch_lstsq()

lt

lt(other) -> Tensor

See ?torch_lt()

lt_

lt_(other) -> Tensor

In-place version of $lt

lu

See ?torch_lu() ## lu_solve

lu_solve(LU_data, LU_pivots) -> Tensor

See [torch_lu_solve()]

map_

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:

callable(a, b) -> number

masked_fill

masked_fill(mask, value) -> Tensor

Out-of-place version of $masked_fill_

masked_fill_

masked_fill_(mask, value)

Fills elements of self tensor with value where mask is TRUE. The shape of mask must be broadcastable <broadcasting-semantics> with the shape of the underlying tensor.

Arguments:

mask (BoolTensor): the boolean mask
value (float): the value to fill in with

masked_scatter

masked_scatter(mask, tensor) -> Tensor

Out-of-place version of $masked_scatter_

masked_scatter_

masked_scatter_(mask, source)

Copies elements from source into self tensor at positions where the mask is TRUE. The shape of mask must be :ref:broadcastable <broadcasting-semantics> with the shape of the underlying tensor. The source should have at least as many elements as the number of ones in mask

Arguments:

mask (BoolTensor): the boolean mask
source (Tensor): the tensor to copy from

Note:

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

masked_select

masked_select(mask) -> Tensor

See [torch_masked_select()]

matmul

matmul(tensor2) -> Tensor

See ?torch_matmul()

matrix_power

matrix_power(n) -> Tensor

See [torch_matrix_power()]

max

max(dim=NULL, keepdim=FALSE) -> Tensor or (Tensor, Tensor)

See ?torch_max()

mean

mean(dim=NULL, keepdim=FALSE) -> Tensor or (Tensor, Tensor)

See ?torch_mean()

median

median(dim=NULL, keepdim=FALSE) -> (Tensor, LongTensor)

See ?torch_median()

min

min(dim=NULL, keepdim=FALSE) -> Tensor or (Tensor, Tensor)

See ?torch_min()

mm

mm(mat2) -> Tensor

See ?torch_mm()

mode

mode(dim=NULL, keepdim=FALSE) -> (Tensor, LongTensor)

See ?torch_mode()

mul

mul(value) -> Tensor

See ?torch_mul()

mul_

mul_(value)

In-place version of $mul

multinomial

multinomial(num_samples, replacement=FALSE, *, generator=NULL) -> Tensor

See ?torch_multinomial()

mv

mv(vec) -> Tensor

See ?torch_mv()

mvlgamma

mvlgamma(p) -> Tensor

See ?torch_mvlgamma()

mvlgamma_

mvlgamma_(p) -> Tensor

In-place version of $mvlgamma

names

Stores names for each of this tensor’s dimensions.

names[idx] corresponds to the name of tensor dimension idx. Names are either a string if the dimension is named or NULL if the dimension is unnamed.

Dimension names may contain characters or underscore. Furthermore, a dimension name must be a valid Python variable name (i.e., does not start with underscore).

Tensors may not have two named dimensions with the same name.

Warning:

The named tensor API is experimental and subject to change.

narrow

narrow(dimension, start, length) -> Tensor

See ?torch_narrow()

Examples:

x <- torch_tensor(matrix(1:9, ncol = 3))
x$narrow(1, 1, 3)
x$narrow(1, 1, 2)

narrow_copy

narrow_copy(dimension, start, length) -> Tensor

Same as Tensor.narrow except returning a copy rather than shared storage. This is primarily for sparse tensors, which do not have a shared-storage narrow method. Calling narrow_copy` withdimemsion > self$sparse_dim()`` will return a copy with the relevant dense dimension narrowed, and ``self$shape`` updated accordingly.

ndim

Alias for $dim()

ndimension

ndimension() -> int

Alias for $dim()

ne

ne(other) -> Tensor

See ?torch_ne()

ne_

ne_(other) -> Tensor

In-place version of $ne

neg

neg() -> Tensor

See ?torch_neg()

neg_

neg_() -> Tensor

In-place version of $neg

nelement

nelement() -> int

Alias for $numel

new_empty

new_empty(size, dtype=NULL, device=NULL, requires_grad=FALSE) -> Tensor

Returns a Tensor of size size filled with uninitialized data. By default, the returned Tensor has the same torch_dtype and torch_device as this tensor.

Arguments:

dtype (torch_dtype, optional): the desired type of returned tensor. Default: if NULL, same torch_dtype as this tensor.
device (torch_device, optional): the desired device of returned tensor. Default: if NULL, same torch_device as this tensor.
requires_grad (bool, optional): If autograd should record operations on the
returned tensor. Default: FALSE.

Examples:

tensor <- torch_ones(5)
tensor$new_empty(c(2, 3))

new_full

new_full(size, fill_value, dtype=NULL, device=NULL, requires_grad=FALSE) -> Tensor

Returns a Tensor of size size filled with fill_value. By default, the returned Tensor has the same torch_dtype and torch_device as this tensor.

Arguments:

fill_value (scalar): the number to fill the output tensor with.
dtype (torch_dtype, optional): the desired type of returned tensor. Default: if NULL, same torch_dtype as this tensor.
device (torch_device, optional): the desired device of returned tensor. Default: if NULL, same torch_device as this tensor.
requires_grad (bool, optional): If autograd should record operations on the
returned tensor. Default: FALSE.

Examples:

tensor <- torch_ones(c(2), dtype=torch_float64())
tensor$new_full(c(3, 4), 3.141592)

new_ones

new_ones(size, dtype=NULL, device=NULL, requires_grad=FALSE) -> Tensor

Returns a Tensor of size size filled with 1. By default, the returned Tensor has the same torch_dtype and torch_device as this tensor.

Arguments:

size (int…): a list, tuple, or torch_Size of integers defining the
shape of the output tensor.
dtype (torch_dtype, optional): the desired type of returned tensor. Default: if NULL, same torch_dtype as this tensor.
device (torch_device, optional): the desired device of returned tensor. Default: if NULL, same torch_device as this tensor.
requires_grad (bool, optional): If autograd should record operations on the
returned tensor. Default: FALSE.

Examples:

tensor <- torch_tensor(c(2), dtype=torch_int32())
tensor$new_ones(c(2, 3))

new_tensor

new_tensor(data, dtype=NULL, device=NULL, requires_grad=FALSE) -> Tensor

Returns a new Tensor with data as the tensor data. By default, the returned Tensor has the same torch_dtype and torch_device as this tensor.

Warning:

new_tensor always copies data(). If you have a Tensordata` and want to avoid a copy, use [$requires_grad_()] or [$detach()]. If you have a numpy array and want to avoid a copy, use [torch_from_numpy()].

When data is a tensor x, [new_tensor()()] reads out ‘the data’ from whatever it is passed, and constructs a leaf variable. Therefore tensor$new_tensor(x) is equivalent to x$clone()$detach() and tensor$new_tensor(x, requires_grad=TRUE) is equivalent to x$clone()$detach()$requires_grad_(TRUE). The equivalents using clone() and detach() are recommended.

Arguments:

data (array_like): The returned Tensor copies data.
dtype (torch_dtype, optional): the desired type of returned tensor. Default: if NULL, same torch_dtype as this tensor.
device (torch_device, optional): the desired device of returned tensor. Default: if NULL, same torch_device as this tensor.
requires_grad (bool, optional): If autograd should record operations on the
returned tensor. Default: FALSE.

Examples:

tensor <- torch_ones(c(2), dtype=torch_int8)
data <- matrix(1:4, ncol = 2)
tensor$new_tensor(data)

new_zeros

new_zeros(size, dtype=NULL, device=NULL, requires_grad=FALSE) -> Tensor

Returns a Tensor of size size filled with 0. By default, the returned Tensor has the same torch_dtype and torch_device as this tensor.

Arguments:

size (int…): a list, tuple, or torch_Size of integers defining the
shape of the output tensor.
dtype (torch_dtype, optional): the desired type of returned tensor. Default: if NULL, same torch_dtype as this tensor.
device (torch_device, optional): the desired device of returned tensor. Default: if NULL, same torch_device as this tensor.
requires_grad (bool, optional): If autograd should record operations on the
returned tensor. Default: FALSE.

Examples:

tensor <- torch_tensor(c(1), dtype=torch_float64())
tensor$new_zeros(c(2, 3))

nonzero

nonzero() -> LongTensor

See ?torch_nonzero()

norm

See ?torch_norm() ## normal_

normal_(mean=0, std=1, *, generator=NULL) -> Tensor

Fills self tensor with elements samples from the normal distribution parameterized by mean and std.

numel

numel() -> int

See ?torch_numel()

numpy

numpy() -> numpy.ndarray

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

orgqr

orgqr(input2) -> Tensor

See ?torch_orgqr()

ormqr

ormqr(input2, input3, left=TRUE, transpose=FALSE) -> Tensor

See ?torch_ormqr()

permute

permute(*dims) -> Tensor

Returns a view of the original tensor with its dimensions permuted.

Arguments:

dims (int…): The desired ordering of dimensions

Examples:

x <- torch_randn(2, 3, 5)
x$size()
x$permute(c(3, 1, 2))$size()

pin_memory

pin_memory() -> Tensor

Copies the tensor to pinned memory, if it’s not already pinned.

pinverse

pinverse() -> Tensor

See ?torch_pinverse()

polygamma

polygamma(n) -> Tensor

See ?torch_polygamma()

polygamma_

polygamma_(n) -> Tensor

In-place version of $polygamma

pow

pow(exponent) -> Tensor

See ?torch_pow()

pow_

pow_(exponent) -> Tensor

In-place version of $pow

prod

prod(dim=NULL, keepdim=FALSE, dtype=NULL) -> Tensor

See ?torch_prod()

put_

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 contain duplicate elements.

Arguments:

indices (LongTensor): the indices into self
tensor (Tensor): the tensor containing values to copy from
accumulate (bool): whether to accumulate into self

Examples:

src <- torch_tensor(matrix(3:8, ncol = 3))
src$put_(torch_tensor(1:2), torch_tensor(9:10))

q_per_channel_axis

q_per_channel_axis() -> int

Given a Tensor quantized by linear (affine) per-channel quantization, returns the index of dimension on which per-channel quantization is applied.

q_per_channel_scales

q_per_channel_scales() -> Tensor

Given a Tensor quantized by linear (affine) per-channel quantization, returns a Tensor of scales of the underlying quantizer. It has the number of elements that matches the corresponding dimensions (from q_per_channel_axis) of the tensor.

q_per_channel_zero_points

q_per_channel_zero_points() -> Tensor

Given a Tensor quantized by linear (affine) per-channel quantization, returns a tensor of zero_points of the underlying quantizer. It has the number of elements that matches the corresponding dimensions (from q_per_channel_axis) of the tensor.

q_scale

q_scale() -> float

Given a Tensor quantized by linear(affine) quantization, returns the scale of the underlying quantizer().

q_zero_point

q_zero_point() -> int

Given a Tensor quantized by linear(affine) quantization, returns the zero_point of the underlying quantizer().

qr

qr(some=TRUE) -> (Tensor, Tensor)

See ?torch_qr()

qscheme

qscheme() -> torch_qscheme

Returns the quantization scheme of a given QTensor.

rad2deg

rad2deg() -> Tensor

See [torch_rad2deg()]

rad2deg_

rad2deg_() -> Tensor

In-place version of $rad2deg

random_

random_(from=0, to=NULL, *, generator=NULL) -> 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_tensor(1, dtype=torch_double).random_() will be uniform in [0, 2^53].

real

Returns a new tensor containing real values of the self tensor. The returned tensor and self share the same underlying storage.

Warning:

[real()] is only supported for tensors with complex dtypes.

Examples:

x <- torch_randn(4, dtype=torch_cfloat())
x
x$real

reciprocal

reciprocal() -> Tensor

See ?torch_reciprocal()

reciprocal_

reciprocal_() -> Tensor

In-place version of $reciprocal

record_stream

record_stream(stream)

Ensures that the tensor memory is not reused for another tensor until all current work queued on stream are complete.

Note:

The caching allocator is aware of only the stream where a tensor was allocated. Due to the awareness, it already correctly manages the life cycle of tensors on only one stream. But if a tensor is used on a stream different from the stream of origin, the allocator might reuse the memory unexpectedly. Calling this method lets the allocator know which streams have used the tensor.

refine_names

Refines the dimension names of self according to names.

Refining is a special case of renaming that “lifts” unnamed dimensions. A NULL 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.

names may contain up to one Ellipsis (...). The Ellipsis is expanded greedily; it is expanded in-place to fill names to the same length as self$dim() using names from the corresponding indices of self$names.

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$refine_names(c('N', 'C', 'H', 'W'))
named_imgs$names

register_hook

Registers a backward hook.

The hook will be called every time a gradient with respect to the Tensor is computed. The hook should have the following signature::

hook(grad) -> Tensor or NULL

The hook should not modify its argument, but it can optionally return a new gradient which will be used in place of grad.

This function returns a handle with a method handle$remove() that removes the hook from the module.

Example

v <- torch_tensor(c(0., 0., 0.), requires_grad=TRUE)
h <- v$register_hook(function(grad) grad * 2)  # double the gradient
v$backward(torch_tensor(c(1., 2., 3.)))
v$grad
h$remove()

remainder

remainder(divisor) -> Tensor

See ?torch_remainder()

remainder_

remainder_(divisor) -> Tensor

In-place version of $remainder

rename

Renames dimension names of self.

There are two main usages:

self$rename(**rename_map) returns a view on tensor that has dims renamed as specified in the mapping rename_map.

self$rename(*names) returns a view on tensor, renaming all dimensions positionally using names. Use self$rename(NULL) to drop names on a tensor.

One cannot specify both positional args names and keyword args rename_map.

Examples:

imgs <- torch_rand(2, 3, 5, 7, names=c('N', 'C', 'H', 'W'))
renamed_imgs <- imgs$rename(c("Batch", "Channels", "Height", "Width"))

rename_

In-place version of $rename.

renorm

renorm(p, dim, maxnorm) -> Tensor

See ?torch_renorm()

renorm_

renorm_(p, dim, maxnorm) -> Tensor

In-place version of $renorm

repeat

repeat(*sizes) -> Tensor

Repeats this tensor along the specified dimensions.

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

Arguments:

sizes (torch_Size or int…): The number of times to repeat this tensor along each
dimension

Examples:

x <- torch_tensor(c(1, 2, 3))
x$`repeat`(c(4, 2))
x$`repeat`(c(4, 2, 1))$size()

repeat_interleave

repeat_interleave(repeats, dim=NULL) -> Tensor

See [torch_repeat_interleave()].

requires_grad

Is TRUE if gradients need to be computed for this Tensor, FALSE otherwise.

Note:

The fact that gradients need to be computed for a Tensor do not mean that the grad attribute will be populated, see is_leaf for more details.

requires_grad_

requires_grad_(requires_grad=TRUE) -> Tensor

Change if autograd should record operations on this tensor: sets this tensor’s requires_grad attribute in-place. Returns this tensor.

[requires_grad_()]’s main use case is to tell autograd to begin recording operations on a Tensor tensor. If tensor has requires_grad=FALSE (because it was obtained through a DataLoader, or required preprocessing or initialization), tensor.requires_grad_() makes it so that autograd will begin to record operations on tensor.

Arguments:

requires_grad (bool): If autograd should record operations on this tensor. Default: TRUE.

Examples:

# Let's say we want to preprocess some saved weights and use
# the result as new weights.
saved_weights <- c(0.1, 0.2, 0.3, 0.25)
loaded_weights <- torch_tensor(saved_weights)
weights <- preprocess(loaded_weights)  # some function
weights

# Now, start to record operations done to weights
weights$requires_grad_()
out <- weights$pow(2)$sum()
out$backward()
weights$grad

reshape

reshape(*shape) -> Tensor

Returns a tensor with the same data and number of elements as self but with the specified shape. This method returns a view if shape is compatible with the current shape. See $view on when it is possible to return a view.

See ?torch_reshape()

Arguments:

shape (tuple of ints or int…): the desired shape

reshape_as

reshape_as(other) -> Tensor

Returns this tensor as the same shape as other. self$reshape_as(other) is equivalent to self$reshape(other.sizes()). This method returns a view if other.sizes() is compatible with the current shape. See $view on when it is possible to return a view.

Please see reshape for more information about reshape.

Arguments:

other (`$): The result tensor has the same shape
as other.

resize_

resize_(*sizes, memory_format=torch_contiguous_format) -> 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.

Warning:

This is a low-level method. The storage is reinterpreted as C-contiguous, ignoring the current strides (unless the target size equals the current size, in which case the tensor is left unchanged). For most purposes, you will instead want to use $view(), which checks for contiguity, or $reshape(), which copies data if needed. To change the size in-place with custom strides, see $set_().

Arguments:

sizes (torch_Size or int…): the desired size
memory_format (torch_memory_format, optional): the desired memory format of Tensor. Default: torch_contiguous_format. Note that memory format of self is going to be unaffected if self$size() matches sizes.

Examples:

x <- torch_tensor(matrix(1:6, ncol = 2))
x$resize_(c(2, 2))

resize_as_

resize_as_(tensor, memory_format=torch_contiguous_format) -> Tensor

Resizes the self tensor to be the same size as the specified tensor. This is equivalent to self$resize_(tensor.size()).

Arguments:

memory_format (torch_memory_format, optional): the desired memory format of Tensor. Default: torch_contiguous_format. Note that memory format of self is going to be unaffected if self$size() matches tensor.size().

retain_grad

Enables $grad attribute for non-leaf Tensors.

rfft

rfft(signal_ndim, normalized=FALSE, onesided=TRUE) -> Tensor

See ?torch_rfft()

roll

roll(shifts, dims) -> Tensor

See ?torch_roll()

rot90

rot90(k, dims) -> Tensor

See [torch_rot90()]

round

round() -> Tensor

See ?torch_round()

round_

round_() -> Tensor

In-place version of $round

rsqrt

rsqrt() -> Tensor

See ?torch_rsqrt()

rsqrt_

rsqrt_() -> Tensor

In-place version of $rsqrt

scatter

scatter(dim, index, src) -> Tensor

Out-of-place version of $scatter_

scatter_

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 (if it is a Tensor) should have same number of dimensions. It is also required that index.size(d) <= src.size(d) for all dimensions d, and that index.size(d) <= self$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.

Arguments:

dim (int): the axis along which to index
index (LongTensor): the indices of elements to scatter,
can be either empty or the same size of src. When empty, the operation returns identity
src (Tensor): the source element(s) to scatter,
incase value is not specified
value (float): the source element(s) to scatter,
incase src is not specified

Examples:

x <- torch_rand(2, 5)
x
torch_zeros(3, 5)$scatter_(
        1, 
        torch_tensor(rbind(c(2, 3, 3, 1, 1), c(3, 1, 1, 2, 3)), x)
)

z <- torch_zeros(2, 4)$scatter_(
        2, 
        torch_tensor(matrix(3:4, ncol = 1)), 1.23
)

scatter_add

scatter_add(dim, index, src) -> Tensor

Out-of-place version of $scatter_add_

scatter_add_

scatter_add_(dim, index, src) -> Tensor

Adds all values from the tensor other into self at the indices specified in the index tensor in a similar fashion as ~$scatter_. For each value in src, it is added to an index in self which 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

self, index and src should have same number of dimensions. It is also required that index.size(d) <= src.size(d) for all dimensions d, and that index.size(d) <= self$size(d) for all dimensions d != dim.

Note:

Arguments:

dim (int): the axis along which to index
index (LongTensor): the indices of elements to scatter and add,
can be either empty or the same size of src. When empty, the operation returns identity.
src (Tensor): the source elements to scatter and add

Examples:

x <- torch_rand(2, 5)
x
torch_ones(3, 5)$scatter_add_(1, torch_tensor(rbind(c(0, 1, 2, 0, 0), c(2, 0, 0, 1, 2))), x)

select

select(dim, index) -> Tensor

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

Arguments:

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_

set_(source=NULL, storage_offset=0, size=NULL, stride=NULL) -> 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.

Arguments:

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

short(memory_format=torch_preserve_format) -> Tensor

self$short() is equivalent to self$to(torch_int16). See [to()].

Arguments:

memory_format (torch_memory_format, optional): the desired memory format of
returned Tensor. Default: torch_preserve_format.

sigmoid

sigmoid() -> Tensor

See ?torch_sigmoid()

sigmoid_

sigmoid_() -> Tensor

In-place version of $sigmoid

sign

sign() -> Tensor

See ?torch_sign()

sign_

sign_() -> Tensor

In-place version of $sign

sin

sin() -> Tensor

See ?torch_sin()

sin_

sin_() -> Tensor

In-place version of $sin

sinh

sinh() -> Tensor

See ?torch_sinh()

sinh_

sinh_() -> Tensor

In-place version of $sinh

size

size() -> torch_Size

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

Examples:

torch_empty(3, 4, 5)$size()

slogdet

slogdet() -> (Tensor, Tensor)

See ?torch_slogdet()

solve

solve(A) -> Tensor, Tensor

See ?torch_solve()

sort

sort(dim=-1, descending=FALSE) -> (Tensor, LongTensor)

See ?torch_sort()

sparse_dim

sparse_dim() -> int

If self is a sparse COO tensor (i.e., with torch_sparse_coo layout), this returns the number of sparse dimensions. Otherwise, this throws an error.

sparse_mask

sparse_mask(input, mask) -> Tensor

Returns a new SparseTensor with values from Tensor input filtered by indices of mask and values are ignored. input and mask must have the same shape.

Arguments:

input (Tensor): an input Tensor
mask (SparseTensor): a SparseTensor which we filter input based on its indices

split

See ?torch_split()

sqrt

sqrt() -> Tensor

See ?torch_sqrt()

sqrt_

sqrt_() -> Tensor

In-place version of $sqrt

square

square() -> Tensor

See ?torch_square()

square_

square_() -> Tensor

In-place version of $square

squeeze

squeeze(dim=NULL) -> Tensor

See ?torch_squeeze()

squeeze_

squeeze_(dim=NULL) -> Tensor

In-place version of $squeeze

std

std(dim=NULL, unbiased=TRUE, keepdim=FALSE) -> Tensor

See ?torch_std()

stft

See ?torch_stft()

storage

storage() -> torch_Storage

Returns the underlying storage.

storage_offset

storage_offset() -> int

Returns self tensor’s offset in the underlying storage in terms of number of storage elements (not bytes).

Examples:

x <- torch_tensor(c(1, 2, 3, 4, 5))
x$storage_offset()
x[3:N]$storage_offset()

storage_type

storage_type() -> type

Returns the type of the underlying storage.

stride

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.

Arguments:

dim (int, optional): the desired dimension in which stride is required

Examples:

x <- torch_tensor(matrix(1:10, nrow = 2))
x$stride()
x$stride(1)
x$stride(-1)

sub

sub(other, *, alpha=1) -> Tensor

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

When other is a tensor, the shape of other must be broadcastable <broadcasting-semantics> with the shape of the underlying tensor.

sub_

sub_(other, *, alpha=1) -> Tensor

In-place version of $sub

sum

sum(dim=NULL, keepdim=FALSE, dtype=NULL) -> Tensor

See ?torch_sum()

sum_to_size

sum_to_size(*size) -> Tensor

Sum this tensor to size. size must be broadcastable to this tensor size.

Arguments:

size (int…): a sequence of integers defining the shape of the output tensor.

svd

svd(some=TRUE, compute_uv=TRUE) -> (Tensor, Tensor, Tensor)

See ?torch_svd()

symeig

symeig(eigenvectors=FALSE, upper=TRUE) -> (Tensor, Tensor)

See ?torch_symeig()

t

t() -> Tensor

See ?torch_t()

t_

t_() -> Tensor

In-place version of $t

take

take(indices) -> Tensor

See ?torch_take()

tan

tan() -> Tensor

See ?torch_tan()

tan_

tan_() -> Tensor

In-place version of $tan

tanh

tanh() -> Tensor

See ?torch_tanh()

tanh_

tanh_() -> Tensor

In-place version of $tanh

to

to(*args, **kwargs) -> Tensor

Performs Tensor dtype and/or device conversion. A torch_dtype and :class:torch_device are inferred from the arguments of self$to(*args, **kwargs).

Note:

If the self Tensor already has the correct torch_dtype and :class:torch_device, then self is returned. Otherwise, the returned tensor is a copy of self with the desired torch_dtype and :class:torch_device.

Here are the ways to call to:

to(dtype, non_blocking=FALSE, copy=FALSE, memory_format=torch_preserve_format) -> Tensor

Returns a Tensor with the specified dtype

Arguments:

memory_format (torch_memory_format, optional): the desired memory format of returned Tensor. Default: torch_preserve_format.

to(device=NULL, dtype=NULL, non_blocking=FALSE, copy=FALSE, memory_format=torch_preserve_format) -> Tensor

Returns a Tensor with the specified device and (optional) dtype. If dtype is NULL it is inferred to be self$dtype. When non_blocking, tries to convert asynchronously with respect to the host if possible, e.g., converting a CPU Tensor with pinned memory to a CUDA Tensor.

When copy is set, a new Tensor is created even when the Tensor already matches the desired conversion.

Arguments:

memory_format (torch_memory_format, optional): the desired memory format of returned Tensor. Default: torch_preserve_format.

function:: to(other, non_blocking=FALSE, copy=FALSE) -> Tensor

Returns a Tensor with same torch_dtype and :class:torch_device as the Tensor other. When non_blocking, tries to convert asynchronously with respect to the host if possible, e.g., converting a CPU Tensor with pinned memory to a CUDA Tensor.

When copy is set, a new Tensor is created even when the Tensor already matches the desired conversion.

Examples:

tensor <- torch_randn(2, 2)  # Initially dtype=float32, device=cpu
tensor$to(dtype = torch_float64())

other <- torch_randn(1, dtype=torch_float64())
tensor$to(other = other, non_blocking=TRUE)

to_mkldnn

to_mkldnn() -> Tensor Returns a copy of the tensor in torch_mkldnn layout.

to_sparse

to_sparse(sparseDims) -> Tensor Returns a sparse copy of the tensor. PyTorch supports sparse tensors in coordinate format <sparse-docs>.

Arguments:

sparseDims (int, optional): the number of sparse dimensions to include in the new sparse tensor

tolist

tolist() -> list or number

Returns the tensor as a (nested) list. For scalars, a standard Python number is returned, just like with $item. Tensors are automatically moved to the CPU first if necessary.

This operation is not differentiable.

topk

topk(k, dim=NULL, largest=TRUE, sorted=TRUE) -> (Tensor, LongTensor)

See ?torch_topk()

trace

trace() -> Tensor

See ?torch_trace()

transpose

transpose(dim0, dim1) -> Tensor

See ?torch_transpose()

transpose_

transpose_(dim0, dim1) -> Tensor

In-place version of $transpose

triangular_solve

triangular_solve(A, upper=TRUE, transpose=FALSE, unitriangular=FALSE) -> (Tensor, Tensor)

See [torch_triangular_solve()]

tril

tril(k=0) -> Tensor

See ?torch_tril()

tril_

tril_(k=0) -> Tensor

In-place version of $tril

triu

triu(k=0) -> Tensor

See ?torch_triu()

triu_

triu_(k=0) -> Tensor

In-place version of $triu

true_divide

true_divide(value) -> Tensor

See [torch_true_divide()]

true_divide_

true_divide_(value) -> Tensor

In-place version of $true_divide_

trunc

trunc() -> Tensor

See ?torch_trunc()

trunc_

trunc_() -> Tensor

In-place version of $trunc

type

type(dtype=NULL, 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.

Arguments:

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

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())

Arguments:

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

unbind

unbind(dim=0) -> seq

See ?torch_unbind()

unflatten

Unflattens the named dimension dim, viewing it in the shape specified by namedshape.

Arguments:

namedshape: (iterable of (name, size) tuples).

unfold

unfold(dimension, size, step) -> Tensor

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

Step between two slices is given by step.

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

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

Arguments:

dimension (int): dimension in which unfolding happens
size (int): the size of each slice that is unfolded
step (int): the step between each slice

uniform_

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

Returns the unique elements of the input tensor.

See ?torch_unique()

unique_consecutive

Eliminates all but the first element from every consecutive group of equivalent elements.

See [torch_unique_consecutive()]

unsqueeze

unsqueeze(dim) -> Tensor

See ?torch_unsqueeze()

unsqueeze_

unsqueeze_(dim) -> Tensor

In-place version of $unsqueeze

values

values() -> Tensor

If self is a sparse COO tensor (i.e., with torch_sparse_coo layout), this returns a view of the contained values tensor. Otherwise, this throws an error.

Note:

This method can only be called on a coalesced sparse tensor. See Tensor$coalesce for details.

var

var(dim=NULL, unbiased=TRUE, keepdim=FALSE) -> Tensor

See ?torch_var()

view

view(*shape) -> Tensor

Returns a new tensor with the same data as the self tensor but of a different shape.

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 = d, \dots, d+k-1,

\[ \text{stride}[i] = \text{stride}[i+1] \times \text{size}[i+1] \]

Otherwise, it will not be possible to view self tensor as shape without copying it (e.g., via contiguous). When it is unclear whether a view can be performed, it is advisable to use :meth:reshape, which returns a view if the shapes are compatible, and copies (equivalent to calling contiguous) otherwise.

Arguments:

shape (torch_Size or int…): the desired size