Struct caffe2op_lpnorm::LpNormOp
source · pub struct LpNormOp<T, Context> { /* private fields */ }
Expand description
| This op computes the $L_p$ norm of the | one dimensional input tensor $X$, and | outputs a one dimensional output tensor | $Y$. Here, the $L_p$ norm is calculated | as | | $$L_p(\mathbf{x}) = \sum_i x_i^p$$ | | This op supports $p$ values of 1 or 2. | If the average argument is set, the norm | is calculated as | | Lp_averaged_norm(x) is defined as | | Lp_averaged_norm(x) = LpNorm(x) / | size(x). | | Github Links: | | - https://github.com/pytorch/pytorch/blob/master/caffe2/operators/lpnorm_op.h | //- https://github.com/pytorch/pytorch/blob/master/caffe2/operators/lpnorm_op.cc |
Implementations§
source§impl LpNormOp<f32, CPUContext>
impl LpNormOp<f32, CPUContext>
pub fn run_on_device(&mut self) -> bool
Auto Trait Implementations§
impl<T, Context> !RefUnwindSafe for LpNormOp<T, Context>
impl<T, Context> !Send for LpNormOp<T, Context>
impl<T, Context> !Sync for LpNormOp<T, Context>
impl<T, Context> Unpin for LpNormOp<T, Context>where Context: Unpin, T: Unpin,
impl<T, Context> !UnwindSafe for LpNormOp<T, Context>
Blanket Implementations§
§impl<T> Pointable for T
impl<T> Pointable for T
§impl<SS, SP> SupersetOf<SS> for SPwhere
SS: SubsetOf<SP>,
impl<SS, SP> SupersetOf<SS> for SPwhere SS: SubsetOf<SP>,
§fn to_subset(&self) -> Option<SS>
fn to_subset(&self) -> Option<SS>
The inverse inclusion map: attempts to construct
self
from the equivalent element of its
superset. Read more§fn is_in_subset(&self) -> bool
fn is_in_subset(&self) -> bool
Checks if
self
is actually part of its subset T
(and can be converted to it).§fn to_subset_unchecked(&self) -> SS
fn to_subset_unchecked(&self) -> SS
Use with care! Same as
self.to_subset
but without any property checks. Always succeeds.§fn from_subset(element: &SS) -> SP
fn from_subset(element: &SS) -> SP
The inclusion map: converts
self
to the equivalent element of its superset.