Struct Tensor 

pub struct Tensor<D: Device, T: Scalar> { /* private fields */ }

A tensor on either CPU or GPU.

Implementations

impl<F: Float> Tensor<Cpu<F>, F>

pub fn statistics(&self) -> MatrixStatistics

impl<D: Device, T: Scalar> Tensor<D, T>

pub fn len(&self) -> usize

pub fn is_empty(&self) -> bool

pub fn size(&self) -> usize

Size of the tensor in bytes.

pub fn offset(index: usize) -> usize

The offset in bytes for a linear index.

pub fn data(&self) -> &D::Data

pub fn id(&self) -> Id<TensorId>

impl<D: Device, F: Float> Tensor<D, F>

pub const fn def(&self) -> &'static str

impl<T: Scalar, K: Kind> Tensor<Gpu<K>, T>

pub fn from_data_u8( context: &Context, shape: impl Into<Shape>, contents: &[u8], ) -> Result<Self, TensorError>

pub fn back_in_place(&self) -> TensorCpu<T>

pub async fn back(&self) -> TensorCpu<T>

impl<T: Scalar, K: Kind> Tensor<Gpu<K>, T>

pub fn context(&self) -> &Context

pub fn load(&self, host: &TensorCpu<T>) -> Result<(), TensorError>

pub fn load_batch( &self, host: &TensorCpu<T>, batch: usize, ) -> Result<(), TensorError>

pub fn destroy(self)

impl<T: Scalar> Tensor<Gpu<Uniform>, T>

pub fn layout(&self, binding: u32) -> BindGroupLayoutEntry

impl<T: Scalar> Tensor<Gpu<ReadWrite>, T>

pub fn meta_layout(&self, binding: u32) -> BindGroupLayoutEntry

pub fn layout(&self, binding: u32, read_only: bool) -> BindGroupLayoutEntry

impl<T: Scalar> Tensor<Cpu<T>, T>

pub fn map<U: Scalar>(self, f: impl FnMut(&T) -> U) -> TensorCpu<U>

Apply a map f to every element in the tensor.

pub fn pad(self, multiples: impl Into<Shape>) -> Self

Pad each dimension to multiples with zeros.

pub fn repeat(self, axis: usize, repeat: usize) -> Self

Repeat the tensor along a given axis.

pub fn stack(batches: Vec<Self>, axis: usize) -> Result<Self, TensorError>

Concat a batch of tensors.

pub fn split(self, axis: usize) -> Result<Vec<Self>, TensorError>

Split the tensor along the batch axis.

pub fn slice( &self, x: impl TensorAxis, y: impl TensorAxis, z: impl TensorAxis, w: impl TensorAxis, ) -> Result<Self, TensorError>

pub fn into_slice( self, x: impl TensorAxis, y: impl TensorAxis, z: impl TensorAxis, w: impl TensorAxis, ) -> Result<Self, TensorError>

impl<T: Scalar> Tensor<Gpu<ReadWrite>, T>

pub fn view( &self, x: impl TensorAxis, y: impl TensorAxis, z: impl TensorAxis, w: impl TensorAxis, ) -> Result<TensorGpuView<'_, T>, TensorError>

Create a view for the tensor.

Trait Implementations

impl<D: Device, T: Scalar> Clone for Tensor<D, T>

fn clone(&self) -> Self

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<D: Debug + Device, T: Debug + Scalar> Debug for Tensor<D, T>

where D::Data: Debug,

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

Formats the value using the given formatter.

impl<D: Device, T: Scalar> Deref for Tensor<D, T>

type Target = <D as Device>::Data

The resulting type after dereferencing.

fn deref(&self) -> &Self::Target

Dereferences the value.

impl<'a, T: Scalar> From<&'a Tensor<Gpu<ReadWrite>, T>> for TensorGpuView<'a, T>

fn from(value: &'a TensorGpu<T, ReadWrite>) -> Self

Converts to this type from the input type.

impl<T: Scalar> From<Tensor<Cpu<T>, T>> for Vec<T>

fn from(value: TensorCpu<T>) -> Self

Converts to this type from the input type.

impl<T: Scalar> TensorInto<Tensor<Cpu<T>, T>> for TensorCpu<T>

fn to(self, _: &Context) -> Self

impl<T: Scalar, K: Kind> TensorInto<Tensor<Gpu<K>, T>> for TensorCpu<T>

fn to(self, context: &Context) -> TensorGpu<T, K>

impl<T: Scalar> TensorInto<Tensor<Gpu<ReadWrite>, T>> for TensorGpu<T, ReadWrite>

Available on non-WebAssembly only.

fn to(self, context: &Context) -> Self

impl<D: Device, T: Scalar> TensorScalar for Tensor<D, T>

type T = T