Struct tract_core::ops::downsample::Downsample

pub struct Downsample {
    pub axis: usize,
    pub stride: usize,
    pub modulo: usize,
}

Fields

axis: usize

stride: usize

modulo: usize

Methods

impl Downsample

pub fn new(axis: usize, stride: usize, modulo: usize) -> Self

Constructs a new Downsample.

Trait Implementations

impl Clone for Downsample

fn clone(&self) -> Downsample

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for Downsample

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

Formats the value using the given formatter.

impl Default for Downsample

fn default() -> Downsample

Returns the "default value" for a type.

impl Op for Downsample

fn name(&self) -> Cow<str>

fn info(&self) -> TractResult<Vec<String>>

Short (one-line) strings giving hints on internal implementation or important configuration details to be displayed in dumps.

fn same_as(&self, other: &dyn Op) -> bool

Compare two ops.

fn as_typed(&self) -> Option<&dyn TypedOp>

fn as_pulsed(&self) -> Option<&dyn PulsedOp>

fn nested_models(&self) -> Vec<(Cow<str>, &dyn Model, Vec<String>, Vec<String>)>

Nested models, with label (for audit).

fn validation(&self) -> Validation

The kind of accuracy check that should be performed on operation when testing them.

fn is_canonic(&self) -> bool

impl PartialEq<Downsample> for Downsample

fn eq(&self, other: &Downsample) -> bool

This method tests for self and other values to be equal, and is used by ==.

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

This method tests for !=.

impl PulsedOp for Downsample

fn pulsed_output_facts(
    &self,
    inputs: &[&PulsedFact]
) -> TractResult<TVec<PulsedFact>>

Deduce output facts from input facts.

fn as_op(&self) -> &dyn Op

Reinterpret the PulsedOp as an Op.

fn as_op_mut(&mut self) -> &mut dyn Op

Reinterpret the PulsedOp as an Op, mutably.

fn to_typed(&self) -> Box<dyn TypedOp>

Reinterpret the PulsedOp as an TypedOp.

impl StatelessOp for Downsample

fn eval(&self, inputs: TVec<Arc<Tensor>>) -> TractResult<TVec<Arc<Tensor>>>

impl StructuralPartialEq for Downsample

impl TypedOp for Downsample

fn output_facts(&self, inputs: &[&TypedFact]) -> TractResult<TVec<TypedFact>>

Deduce output facts from input facts.

fn declutter(
    &self,
    model: &TypedModel,
    node: &TypedNode
) -> TractResult<Option<TypedModelPatch>>

Declutter the op to the tract_core operator set as much as possible.

fn pulsify(
    &self,
    _source: &NormalizedModel,
    node: &NormalizedNode,
    target: &mut PulsedModel,
    mapping: &HashMap<OutletId, OutletId>,
    _pulse: usize
) -> TractResult<TVec<OutletId>>

Translate an op in a normalized network (no constants) to a pulsing form, if possible.

fn as_op(&self) -> &dyn Op

Reinterpret the TypedOp as an Op.

fn as_op_mut(&mut self) -> &mut dyn Op

Reinterpret the TypedOp as an Op, mutably.

fn invariants(
    &self,
    model: &TypedModel,
    node: &TypedNode
) -> TractResult<Invariants>

fn fuse(
    &self,
    _model: &TypedModel,
    _node: &TypedNode
) -> TractResult<Option<TypedModelPatch>>

Fuse op after codegen to deal with local optimisations.

fn cost(&self, _inputs: &[&TypedFact]) -> TractResult<TVec<(Cost, TDim)>>

Computes a cost hint of the operation.

fn suggested_axis_changes(&self) -> TractResult<TVec<(InOut, AxisOp)>>

fn change_axes(
    &self,
    model: &TypedModel,
    node: &TypedNode,
    io: InOut,
    change: &AxisOp
) -> TractResult<Option<AxisChangeConsequence>>

fn slice_output(
    &self,
    model: &TypedModel,
    node: &TypedNode,
    patch: &mut TypedModelPatch,
    output_slot: usize,
    axis: usize,
    start: usize,
    end: usize
) -> TractResult<Option<OutletId>>

fn quantize(
    &self,
    model: &TypedModel,
    node: &TypedNode,
    dt: DatumType,
    scale: f32,
    zero_point: i32
) -> TractResult<Option<Box<dyn TypedOp>>>

Transforms the op in an equivalent one, operating on dt (i8 or u8).

fn codegen(
    &self,
    _model: &TypedModel,
    _node: &TypedNode
) -> TractResult<Option<TypedModelPatch>>

Translate the op into the most efficient form possible for execution.

fn nested_model_multipliers(
    &self,
    inputs: &[&TypedFact]
) -> Vec<(Cow<str>, f32)>

Nested model multipliers, with label (for profiling).