Enum KernelLayout 

pub enum KernelLayout {
    OIHW,
    HWIO,
    OIL,
    LIO,
}

Kernel (weight) layout for convolution filters.

Variants

OIHW

OutChannels, InChannels, Height, Width (PyTorch default for 2D).

HWIO

Height, Width, InChannels, OutChannels (TensorFlow default for 2D).

OIL

OutChannels, InChannels, Length (PyTorch default for 1D).

LIO

Length, InChannels, OutChannels (TensorFlow default for 1D).

Trait Implementations

impl Clone for KernelLayout

fn clone(&self) -> KernelLayout

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for KernelLayout

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

Formats the value using the given formatter.

impl Display for KernelLayout

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

Formats the value using the given formatter.

impl Hash for KernelLayout

fn hash<__H: Hasher>(&self, state: &mut __H)

Feeds this value into the given Hasher.

fn hash_slice<H>(data: &[Self], state: &mut H)

where H: Hasher, Self: Sized,

Feeds a slice of this type into the given Hasher.

impl PartialEq for KernelLayout

fn eq(&self, other: &KernelLayout) -> 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 Copy for KernelLayout

impl Eq for KernelLayout

impl StructuralPartialEq for KernelLayout