Struct burn::backend::libtorch::TchTensor

pub struct TchTensor<E>
where
    E: Element,
{
    pub tensor: Tensor,
    pub storage: Storage,
    /* private fields */
}

A tensor using the tch backend.

Fields

tensor: Tensor

Handle to the tensor. Call methods on this field.

storage: Storage

The tensor’s storage

Implementations

impl<E> TchTensor<E>

where E: Element,

pub fn new(tensor: Tensor) -> TchTensor<E>

Create a new tensor.

Note that if the tensor was created from an operation that may reuse the same tensor storage as the parent, you should use from_existing instead.

pub fn from_existing(tensor: Tensor, storage_parent: Storage) -> TchTensor<E>

Create a tensor that was created from an operation executed on a parent tensor.

If the child tensor shared the same storage as its parent, it will be cloned, effectively tracking how much tensors point to the same memory space.

pub fn partial(tensor: Tensor, storage_parent: Storage) -> TchTensor<E>

Create a tensor that uses a part of its parent tensor such as slice and narrow.

impl<P> TchTensor<P>

where P: Element,

pub fn can_mut(&self) -> bool

Checks if the tensor can be mutated in-place.

Returns true if the tensor’s stride does not contain zero (no broadcasting) and the storage can be mutated.

pub fn mut_ops<F, EOut>(&mut self, func: F) -> Option<TchTensor<EOut>>

where F: Fn(&mut Tensor) -> Tensor, EOut: Element,

Executes an operation on a tensor if the data can be reused.

pub fn unary_ops<FOwn, FRef, EOut>( self, fown: FOwn, fref: FRef, ) -> TchTensor<EOut>

where EOut: Element, FOwn: Fn(Tensor) -> Tensor, FRef: Fn(&Tensor) -> Tensor,

Executes a unary operation, reusing the tensor data if possible.

pub fn binary_ops_tensor<FLMut, FRMut, FRef, EOut>( lhs: TchTensor<P>, rhs: TchTensor<P>, flmut: FLMut, frmut: FRMut, fref: FRef, ) -> TchTensor<EOut>

where EOut: Element, FLMut: Fn(&mut Tensor, &Tensor) -> Tensor, FRMut: Fn(&Tensor, &mut Tensor) -> Tensor, FRef: Fn(&Tensor, &Tensor) -> Tensor,

Executes a binary operation, reusing the tensor data if possible.

impl<E> TchTensor<E>

where E: Element + Default + Element,

pub fn from_data(data: TensorData, device: Device) -> TchTensor<E>

Creates a new tensor from a shape and a device.

Arguments

• data - The tensor’s data.
• device - The device on which the tensor will be allocated.

Returns

A new tensor.

impl<E> TchTensor<E>

where E: Element + Default + Copy + Debug,

pub fn empty(shape: Shape, device: LibTorchDevice) -> TchTensor<E>

Creates an empty tensor from a shape and a device.

Arguments

• shape - The shape of the tensor.
• device - The device to create the tensor on.

Returns

A new empty tensor.

Trait Implementations

impl<P> Clone for TchTensor<P>

where P: Element,

fn clone(&self) -> TchTensor<P>

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl<E> Debug for TchTensor<E>

where E: Debug + Element,

fn fmt(&self, f: &mut Formatter<'_>) -> Result<(), Error>

Formats the value using the given formatter.

impl<E> PartialEq for TchTensor<E>

where E: PartialEq + Element,

fn eq(&self, other: &TchTensor<E>) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl<E> Send for TchTensor<E>

where E: Element,

impl<E> StructuralPartialEq for TchTensor<E>

where E: Element,

impl<E> Sync for TchTensor<E>

where E: Element,