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.
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).
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
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 $align_to.
# Example 1: Applying a mask mask <- torch_randint(low = 0, high = 2, size = c(127, 128), dtype=torch_bool())$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)
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$names; the output tensor has a size-one dimension for each of those new names.
all() -> bool
Returns TRUE if all elements in the tensor are TRUE, FALSE otherwise.
a <- torch_rand(1, 2)$to(dtype = torch_bool()) a a$all()
all(dim, keepdim=FALSE, out=NULL) -> Tensor
Returns TRUE if all elements in each row of the tensor in the given dimension dim are TRUE, FALSE otherwise.
If keepdim is TRUE, the output tensor is of the same size as input except in the dimension dim where it is of size 1. Otherwise, dim is squeezed (see ?torch_squeeze()), resulting in the output tensor having 1 fewer dimension than input.
dim retained or nota <- torch_rand(4, 2)$to(dtype = torch_bool()) a a$all(dim=2) a$all(dim=1)
any() -> bool
Returns TRUE if any elements in the tensor are TRUE, FALSE otherwise.
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.
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.
dim retained or nota <- torch_randn(4, 2) < 0 a a$any(2) a$any(1)
apply_(callable) -> Tensor
Applies the function callable to each element in the tensor, replacing each element with the value returned by callable.
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.
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.
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.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.TRUE, graph of the derivative will be constructed, allowing to compute higher order derivative products. Defaults to FALSE.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_(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 $bernoulli and ?torch_bernoulli
bfloat16(memory_format=torch_preserve_format) -> Tensor self$bfloat16() is equivalent to self$to(torch_bfloat16). See [to()].
bool(memory_format=torch_preserve_format) -> Tensor
self$bool() is equivalent to self$to(torch_bool). See [to()].
byte(memory_format=torch_preserve_format) -> Tensor
self$byte() is equivalent to self$to(torch_uint8). See [to()].
cauchy_(median=0, sigma=1, *, generator=NULL) -> Tensor
Fills the tensor with numbers drawn from the Cauchy distribution:
\[ f(x) = \dfrac{1}{\pi} \dfrac{\sigma}{(x - \text{median})^2 + \sigma^2} \]
char(memory_format=torch_preserve_format) -> Tensor
self$char() is equivalent to self$to(torch_int8). See [to()].
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.
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.
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.
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.
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.
torch_device): The destination GPU device. Defaults to the current CUDA device.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.torch_memory_format, optional): the desired memory format oftorch_preserve_format.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.
See also $sparse_dim.
dequantize() -> Tensor
Given a quantized Tensor, dequantize it and return the dequantized float Tensor.
Returns a new Tensor, detached from the current graph.
The result will never require gradient.
Returned Tensor shares the same storage with the original one. In-place modifications on either of them will be seen, and may trigger errors in correctness checks. IMPORTANT NOTE: Previously, in-place size / stride / storage changes (such as resize_ / resize_as_ / set_ / transpose_) to the returned tensor also update the original tensor. Now, these in-place changes will not update the original tensor anymore, and will instead trigger an error. For sparse tensors: In-place indices / values changes (such as zero_ / copy_ / add_) to the returned tensor will not update the original tensor anymore, and will instead trigger an error.
Detaches the Tensor from the graph that created it, making it a leaf. Views cannot be detached in-place.
double(memory_format=torch_preserve_format) -> Tensor
self$double() is equivalent to self$to(torch_float64). See [to()].
element_size() -> int
Returns the size in bytes of an individual element.
torch_tensor(c(1))$element_size()
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.
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.
x <- torch_tensor(matrix(c(1,2,3), ncol = 1)) x$size() x$expand(c(3, 4)) x$expand(c(-1, 4)) # -1 means not changing the size of that dimension
expand_as(other) -> Tensor
Expand this tensor to the same size as other. self$expand_as(other) is equivalent to self$expand(other.size()).
Please see $expand for more information about expand.
exponential_(lambd=1, *, generator=NULL) -> Tensor
Fills self tensor with elements drawn from the exponential distribution:
\[ f(x) = \lambda e^{-\lambda x} \]
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.
a <- torch_zeros(3, 3) a$fill_diagonal_(5) b <- torch_zeros(7, 3) b$fill_diagonal_(5) c <- torch_zeros(7, 3) c$fill_diagonal_(5, wrap=TRUE)
float(memory_format=torch_preserve_format) -> Tensor
self$float() is equivalent to self$to(torch_float32). See [to()].
geometric_(p, *, generator=NULL) -> Tensor
Fills self tensor with elements drawn from the geometric distribution:
\[ f(X=k) = p^{k - 1} (1 - p) \]
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.
x <- torch_randn(3, 4, 5, device='cuda:0') x$get_device() x$cpu()$get_device() # RuntimeError: get_device is not implemented for type torch_FloatTensor
This attribute is NULL by default and becomes a Tensor the first time a call to backward computes gradients for self. The attribute will then contain the gradients computed and future calls to [backward()] will accumulate (add) gradients into it.
half(memory_format=torch_preserve_format) -> Tensor
self$half() is equivalent to self$to(torch_float16). See [to()].
Returns a new tensor containing imaginary values of the self tensor. The returned tensor and self share the same underlying storage.
x <- torch_randn(4, dtype=torch_cfloat()) x x$imag
index_add(tensor1, dim, index, tensor2) -> Tensor
Out-of-place version of $index_add_. tensor1 corresponds to self in $index_add_.
index_add_(dim, index, tensor) -> Tensor
Accumulate the elements of tensor into the self tensor by adding to the indices in the order given in index. For example, if dim == 0 and index[i] == j, then the i th row of tensor is added to the j th row of self.
The dim th dimension of tensor must have the same size as the length of index (which must be a vector), and all other dimensions must match self, or an error will be raised.
In some circumstances when using the CUDA backend with CuDNN, this operator may select a nondeterministic algorithm to increase performance. If this is undesirable, you can try to make the operation deterministic (potentially at a performance cost) by setting torch_backends.cudnn.deterministic = TRUE.
tensor to select fromx <- torch_ones(5, 3) t <- torch_tensor(matrix(1:9, ncol = 3), dtype=torch_float()) index <- torch_tensor(c(1L, 4L, 3L)) x$index_add_(1, index, t)
index_copy(tensor1, dim, index, tensor2) -> Tensor
Out-of-place version of $index_copy_. tensor1 corresponds to self in $index_copy_.
index_copy_(dim, index, tensor) -> Tensor
Copies the elements of tensor into the self tensor by selecting the indices in the order given in index. For example, if dim == 0 and index[i] == j, then the i th row of tensor is copied to the j th row of self.
The dim th dimension of tensor must have the same size as the length of index (which must be a vector), and all other dimensions must match self, or an error will be raised.
tensor to select fromx <- torch_zeros(5, 3) t <- torch_tensor(matrix(1:9, ncol = 3), dtype=torch_float()) index <- torch_tensor(c(1, 5, 3)) x$index_copy_(1, index, t)
index_fill(tensor1, dim, index, value) -> Tensor
Out-of-place version of $index_fill_. tensor1 corresponds to self in $index_fill_.
index_fill_(dim, index, val) -> Tensor
Fills the elements of the self tensor with value val by selecting the indices in the order given in index.
self tensor to fill inx <- torch_tensor(matrix(1:9, ncol = 3), dtype=torch_float()) index <- torch_tensor(c(1, 3), dtype = torch_long()) x$index_fill_(1, index, -1)
index_put(tensor1, indices, value, accumulate=FALSE) -> Tensor
Out-place version of $index_put_. tensor1 corresponds to self in $index_put_.
index_put_(indices, value, accumulate=FALSE) -> Tensor
Puts values from the tensor value into the tensor self using the indices specified in indices (which is a tuple of Tensors). The expression tensor.index_put_(indices, value) is equivalent to tensor[indices] = value. Returns self.
If accumulate is TRUE, the elements in value are added to self. If accumulate is FALSE, the behavior is undefined if indices contain duplicate elements.
indices() -> Tensor
If self is a sparse COO tensor (i.e., with torch_sparse_coo layout), this returns a view of the contained indices tensor. Otherwise, this throws an error.
See also Tensor.values.
int(memory_format=torch_preserve_format) -> Tensor
self$int() is equivalent to self$to(torch_int32). See [to()].
int_repr() -> Tensor
Given a quantized Tensor, self$int_repr() returns a CPU Tensor with uint8_t as data type that stores the underlying uint8_t values of the given Tensor.
irfft(signal_ndim, normalized=FALSE, onesided=TRUE, signal_sizes=NULL) -> Tensor
See ?torch_irfft
is_contiguous(memory_format=torch_contiguous_format) -> bool
Returns TRUE if self tensor is contiguous in memory in the order specified by memory format.
is_floating_point() -> bool
Returns TRUE if the data type of self is a floating point data type.
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()].
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 TRUE if the Tensor is a meta tensor, FALSE otherwise. Meta tensors are like normal tensors, but they carry no data.
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.
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 $tolist.
This operation is not differentiable.
x <- torch_tensor(1.0) x$item()
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:
\[ f(x) = \dfrac{1}{x \sigma \sqrt{2\pi}}\ e^{-\frac{(\ln x - \mu)^2}{2\sigma^2}} \]
long(memory_format=torch_preserve_format) -> Tensor
self$long() is equivalent to self$to(torch_int64). See [to()].
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_(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.
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
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.
narrow(dimension, start, length) -> Tensor
See ?torch_narrow
x <- torch_tensor(matrix(1:9, ncol = 3)) x$narrow(1, 1, 3) x$narrow(1, 1, 2)
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.
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.
torch_dtype, optional): the desired type of returned tensor. Default: if NULL, same torch_dtype as this tensor.torch_device, optional): the desired device of returned tensor. Default: if NULL, same torch_device as this tensor.FALSE.tensor <- torch_ones(5) tensor$new_empty(c(2, 3))
new_full(size, fill_value, dtype=NULL, device=NULL, requires_grad=FALSE) -> Tensor
Returns a Tensor of size size filled with fill_value. By default, the returned Tensor has the same torch_dtype and torch_device as this tensor.
torch_dtype, optional): the desired type of returned tensor. Default: if NULL, same torch_dtype as this tensor.torch_device, optional): the desired device of returned tensor. Default: if NULL, same torch_device as this tensor.FALSE.tensor <- torch_ones(c(2), dtype=torch_float64()) tensor$new_full(c(3, 4), 3.141592)
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.
torch_Size of integers defining thetorch_dtype, optional): the desired type of returned tensor. Default: if NULL, same torch_dtype as this tensor.torch_device, optional): the desired device of returned tensor. Default: if NULL, same torch_device as this tensor.FALSE.tensor <- torch_tensor(c(2), dtype=torch_int32()) tensor$new_ones(c(2, 3))
new_tensor(data, dtype=NULL, device=NULL, requires_grad=FALSE) -> Tensor
Returns a new Tensor with data as the tensor data. By default, the returned Tensor has the same torch_dtype and torch_device as this tensor.
new_tensor always copies data(). If you have a Tensordata` and want to avoid a copy, use [$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.
data.torch_dtype, optional): the desired type of returned tensor. Default: if NULL, same torch_dtype as this tensor.torch_device, optional): the desired device of returned tensor. Default: if NULL, same torch_device as this tensor.FALSE.tensor <- torch_ones(c(2), dtype=torch_int8) data <- matrix(1:4, ncol = 2) tensor$new_tensor(data)
new_zeros(size, dtype=NULL, device=NULL, requires_grad=FALSE) -> Tensor
Returns a Tensor of size size filled with 0. By default, the returned Tensor has the same torch_dtype and torch_device as this tensor.
torch_Size of integers defining thetorch_dtype, optional): the desired type of returned tensor. Default: if NULL, same torch_dtype as this tensor.torch_device, optional): the desired device of returned tensor. Default: if NULL, same torch_device as this tensor.FALSE.tensor <- torch_tensor(c(1), dtype=torch_float64()) tensor$new_zeros(c(2, 3))
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.
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.
permute(*dims) -> Tensor
Returns a view of the original tensor with its dimensions permuted.
x <- torch_randn(2, 3, 5) x$size() x$permute(c(3, 1, 2))$size()
put_(indices, tensor, accumulate=FALSE) -> Tensor
Copies the elements from tensor into the positions specified by indices. For the purpose of indexing, the self tensor is treated as if it were a 1-D tensor.
If accumulate is TRUE, the elements in tensor are added to self. If accumulate is FALSE, the behavior is undefined if indices contain duplicate elements.
src <- torch_tensor(matrix(3:8, ncol = 3)) src$put_(torch_tensor(1:2), torch_tensor(9:10))
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() -> 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() -> 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() -> float
Given a Tensor quantized by linear(affine) quantization, returns the scale of the underlying quantizer().
q_zero_point() -> int
Given a Tensor quantized by linear(affine) quantization, returns the zero_point of the underlying quantizer().
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].
Returns a new tensor containing real values of the self tensor. The returned tensor and self share the same underlying storage.
x <- torch_randn(4, dtype=torch_cfloat()) x x$real
record_stream(stream)
Ensures that the tensor memory is not reused for another tensor until all current work queued on stream are complete.
The caching allocator is aware of only the stream where a tensor was allocated. Due to the awareness, it already correctly manages the life cycle of tensors on only one stream. But if a tensor is used on a stream different from the stream of origin, the allocator might reuse the memory unexpectedly. Calling this method lets the allocator know which streams have used the tensor.
Refines the dimension names of self according to names.
Refining is a special case of renaming that “lifts” unnamed dimensions. A NULL dim can be refined to have any name; a named dim can only be refined to have the same name.
Because named tensors can coexist with unnamed tensors, refining names gives a nice way to write named-tensor-aware code that works with both named and unnamed tensors.
names may contain up to one Ellipsis (...). The Ellipsis is expanded greedily; it is expanded in-place to fill names to the same length as self$dim() using names from the corresponding indices of self$names.
imgs <- torch_randn(32, 3, 128, 128) named_imgs <- imgs$refine_names(c('N', 'C', 'H', 'W')) named_imgs$names
Registers a backward hook.
The hook will be called every time a gradient with respect to the Tensor is computed. The hook should have the following signature::
hook(grad) -> Tensor or NULL
The hook should not modify its argument, but it can optionally return a new gradient which will be used in place of grad.
This function returns a handle with a method handle$remove() that removes the hook from the module.
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()
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$rename(NULL) to drop names on a tensor.
One cannot specify both positional args names and keyword args rename_map.
imgs <- torch_rand(2, 3, 5, 7, names=c('N', 'C', 'H', 'W')) renamed_imgs <- imgs$rename(c("Batch", "Channels", "Height", "Width"))
repeat(*sizes) -> Tensor
Repeats this tensor along the specified dimensions.
Unlike $expand, this function copies the tensor’s data.
x <- torch_tensor(c(1, 2, 3)) x$`repeat`(c(4, 2)) x$`repeat`(c(4, 2, 1))$size()
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.
TRUE.# 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(*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 $view on when it is possible to return a view.
See ?torch_reshape
reshape_as(other) -> Tensor
Returns this tensor as the same shape as other. self$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.
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.
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 $set_().
torch_memory_format, optional): the desired memory format of Tensor. Default: torch_contiguous_format. Note that memory format of self is going to be unaffected if self$size() matches sizes.x <- torch_tensor(matrix(1:6, ncol = 2)) x$resize_(c(2, 2))
resize_as_(tensor, memory_format=torch_contiguous_format) -> Tensor
Resizes the self tensor to be the same size as the specified tensor. This is equivalent to self$resize_(tensor.size()).
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 == 2This 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.
value is not specifiedsrc is not specifiedx <- 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_(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 ~$scatter_. For each value in src, it is added to an index in self which is specified by its index in src for dimension != dim and by the corresponding value in index for dimension = dim.
For a 3-D tensor, self is updated as::
self[index[i][j][k]][j][k] += src[i][j][k] # if dim == 0
self[i][index[i][j][k]][k] += src[i][j][k] # if dim == 1
self[i][j][index[i][j][k]] += src[i][j][k] # if dim == 2self, index and src should have same number of dimensions. It is also required that index.size(d) <= src.size(d) for all dimensions d, and that index.size(d) <= self$size(d) for all dimensions d != dim.
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.
x <- torch_rand(2, 5) x torch_ones(3, 5)$scatter_add_(1, torch_tensor(rbind(c(0, 1, 2, 0, 0), c(2, 0, 0, 1, 2))), x)
select(dim, index) -> Tensor
Slices the self tensor along the selected dimension at the given index. This function returns a view of the original tensor with the given dimension removed.
set_(source=NULL, storage_offset=0, size=NULL, stride=NULL) -> Tensor
Sets the underlying storage, size, and strides. If source is a tensor, self tensor will share the same storage and have the same size and strides as source. Changes to elements in one tensor will be reflected in the other.
short(memory_format=torch_preserve_format) -> Tensor
self$short() is equivalent to self$to(torch_int16). See [to()].
size() -> torch_Size
Returns the size of the self tensor. The returned value is a subclass of tuple.
torch_empty(3, 4, 5)$size()
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.
See also Tensor.dense_dim.
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.
See ?torch_split
See ?torch_stft
storage_offset() -> int
Returns self tensor’s offset in the underlying storage in terms of number of storage elements (not bytes).
x <- torch_tensor(c(1, 2, 3, 4, 5)) x$storage_offset() x[3:N]$storage_offset()
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.
x <- torch_tensor(matrix(1:10, nrow = 2)) x$stride() x$stride(1) x$stride(-1)
sub(other, *, alpha=1) -> Tensor
Subtracts a scalar or tensor from self tensor. If both alpha and other are specified, each element of other is scaled by alpha before being used.
When other is a tensor, the shape of other must be broadcastable <broadcasting-semantics> with the shape of the underlying tensor.
sum_to_size(*size) -> Tensor
Sum this tensor to size. size must be broadcastable to this tensor size.
to(*args, **kwargs) -> Tensor
Performs Tensor dtype and/or device conversion. A torch_dtype and :class:torch_device are inferred from the arguments of self$to(*args, **kwargs).
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
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$dtype. When non_blocking, tries to convert asynchronously with respect to the host if possible, e.g., converting a CPU Tensor with pinned memory to a CUDA Tensor.
When copy is set, a new Tensor is created even when the Tensor already matches the desired conversion.
torch_memory_format, optional): the desired memory format of returned Tensor. Default: torch_preserve_format.function:: to(other, non_blocking=FALSE, copy=FALSE) -> Tensor
Returns a Tensor with same torch_dtype and :class:torch_device as the Tensor other. When non_blocking, tries to convert asynchronously with respect to the host if possible, e.g., converting a CPU Tensor with pinned memory to a CUDA Tensor.
When copy is set, a new Tensor is created even when the Tensor already matches the desired conversion.
tensor <- torch_randn(2, 2) # Initially dtype=float32, device=cpu tensor$to(dtype = torch_float64()) other <- torch_randn(1, dtype=torch_float64()) tensor$to(other = other, non_blocking=TRUE)
to_sparse(sparseDims) -> Tensor Returns a sparse copy of the tensor. PyTorch supports sparse tensors in coordinate format <sparse-docs>.
tolist() -> list or number
Returns the tensor as a (nested) list. For scalars, a standard Python number is returned, just like with $item. Tensors are automatically moved to the CPU first if necessary.
This operation is not differentiable.
triangular_solve(A, upper=TRUE, transpose=FALSE, unitriangular=FALSE) -> (Tensor, Tensor)
See [torch_triangular_solve()]
type(dtype=NULL, non_blocking=FALSE, **kwargs) -> str or Tensor Returns the type if dtype is not provided, else casts this object to the specified type.
If this is already of the correct type, no copy is performed and the original object is returned.
TRUE, and the source is in pinned memoryasync in place ofnon_blocking argument. The async arg is deprecated.type_as(tensor) -> Tensor
Returns this tensor cast to the type of the given tensor.
This is a no-op if the tensor is already of the correct type. This is equivalent to self$type(tensor.type())
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.
uniform_(from=0, to=1) -> Tensor
Fills self tensor with numbers sampled from the continuous uniform distribution:
\[ P(x) = \dfrac{1}{\text{to} - \text{from}} \]
Eliminates all but the first element from every consecutive group of equivalent elements.
See [torch_unique_consecutive()]
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.
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.
view_as(other) -> Tensor
View this tensor as the same size as other. self$view_as(other) is equivalent to self$view(other.size()).
Please see $view for more information about view.
where(condition, y) -> Tensor
self$where(condition, y) is equivalent to torch_where(condition, self, y). See ?torch_where