Struct tract_hir::ops::cnn::AvgPool

pub struct AvgPool {
    pub pool_spec: PoolSpec,
    pub count_include_pad: bool,
}

Fields

pool_spec: PoolSpec

count_include_pad: bool

Implementations

impl AvgPool

pub fn new(pool_spec: PoolSpec, count_include_pad: bool) -> AvgPool

Constructs a new AvgPool.

Trait Implementations

impl Clone for AvgPool

fn clone(&self) -> AvgPool

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for AvgPool

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

Formats the value using the given formatter.

impl Default for AvgPool

fn default() -> AvgPool

Returns the "default value" for a type.

impl DynHash for AvgPool

fn dyn_hash(&self, state: &mut dyn Hasher)

impl Hash for AvgPool

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

Feeds this value into the given [Hasher].

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

Feeds a slice of this type into the given [Hasher].

impl InferenceRulesOp for AvgPool

fn rules<'r, 'p: 'r, 's: 'r>(
    &'s self,
    s: &mut Solver<'r>,
    inputs: &'p [TensorProxy],
    outputs: &'p [TensorProxy]
) -> InferenceResult

Registers the inference rules of the operator.

fn as_op(&self) -> &dyn Op

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

fn to_typed(
    &self,
    _source: &InferenceModel,
    node: &InferenceNode,
    target: &mut TypedModel,
    mapping: &HashMap<OutletId, OutletId>
) -> TractResult<TVec<OutletId>>

fn nboutputs(&self) -> TractResult<usize>

fn incorporate(
    &self,
    model: &InferenceModel,
    node: &InferenceNode
) -> TractResult<Option<InferenceModelPatch>>

impl Op for AvgPool

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

fn info(&self) -> Result<Vec<String>, TractError>

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

fn validation(&self) -> Validation

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

fn is_canonic(&self) -> bool

fn op_families(&self) -> &'static [&'static str]

Vector of short strings defining what families the op belongs too. tract-core defines "core", "mir", "lir".

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

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

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

Nested models, with label (for audit).

fn same_as(&self, _other: &(dyn Op + 'static)) -> bool

Compare two ops.

impl PulsedOp for AvgPool

fn pulsed_output_facts(
    &self,
    inputs: &[&PulsedFact]
) -> Result<SmallVec<[PulsedFact; 4]>, TractError>

Deduce output facts from input facts.

fn as_op(&self) -> &(dyn Op + 'static)

Reinterpret the PulsedOp as an Op.

fn as_op_mut(&mut self) -> &mut (dyn Op + 'static)

Reinterpret the PulsedOp as an Op, mutably.

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

Reinterpret the PulsedOp as an TypedOp.

impl StatelessOp for AvgPool

fn eval(
    &self,
    inputs: SmallVec<[Arc<Tensor>; 4]>
) -> Result<SmallVec<[Arc<Tensor>; 4]>, TractError>

impl TypedOp for AvgPool

fn output_facts(
    &self,
    inputs: &[&TypedFact]
) -> Result<SmallVec<[TypedFact; 4]>, TractError>

Deduce output facts from input facts.

fn pulsify(
    &self,
    source: &ModelImpl<NormalizedFact, Box<dyn TypedOp + 'static>>,
    node: &BaseNode<NormalizedFact, Box<dyn TypedOp + 'static>>,
    target: &mut ModelImpl<PulsedFact, Box<dyn PulsedOp + 'static>>,
    mapping: &HashMap<OutletId, OutletId, RandomState>,
    _pulse: usize
) -> Result<SmallVec<[OutletId; 4]>, TractError>

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

fn codegen(
    &self,
    model: &ModelImpl<TypedFact, Box<dyn TypedOp + 'static>>,
    node: &BaseNode<TypedFact, Box<dyn TypedOp + 'static>>
) -> Result<Option<ModelPatch<TypedFact, Box<dyn TypedOp + 'static>>>, TractError>

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

fn as_op(&self) -> &(dyn Op + 'static)

Reinterpret the TypedOp as an Op.

fn as_op_mut(&mut self) -> &mut (dyn Op + 'static)

Reinterpret the TypedOp as an Op, mutably.

fn invariants(
    &self,
    model: &ModelImpl<TypedFact, Box<dyn TypedOp + 'static>>,
    node: &BaseNode<TypedFact, Box<dyn TypedOp + 'static>>
) -> Result<Invariants, TractError>

fn fuse(
    &self,
    _model: &ModelImpl<TypedFact, Box<dyn TypedOp + 'static>>,
    _node: &BaseNode<TypedFact, Box<dyn TypedOp + 'static>>
) -> Result<Option<ModelPatch<TypedFact, Box<dyn TypedOp + 'static>>>, TractError>

Fuse op after codegen to deal with local optimisations.

fn declutter(
    &self,
    model: &ModelImpl<TypedFact, Box<dyn TypedOp + 'static>>,
    node: &BaseNode<TypedFact, Box<dyn TypedOp + 'static>>
) -> Result<Option<ModelPatch<TypedFact, Box<dyn TypedOp + 'static>>>, TractError>

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

fn cost(
    &self,
    _inputs: &[&TypedFact]
) -> Result<SmallVec<[(Cost, TDim); 4]>, TractError>

Computes a cost hint of the operation.

fn suggested_axis_changes(
    &self
) -> Result<SmallVec<[(InOut, AxisOp); 4]>, TractError>

fn change_axes(
    &self,
    model: &ModelImpl<TypedFact, Box<dyn TypedOp + 'static>>,
    node: &BaseNode<TypedFact, Box<dyn TypedOp + 'static>>,
    io: InOut,
    change: &AxisOp
) -> Result<Option<AxisChangeConsequence>, TractError>

fn slice_output(
    &self,
    model: &ModelImpl<TypedFact, Box<dyn TypedOp + 'static>>,
    node: &BaseNode<TypedFact, Box<dyn TypedOp + 'static>>,
    patch: &mut ModelPatch<TypedFact, Box<dyn TypedOp + 'static>>,
    output_slot: usize,
    axis: usize,
    start: usize,
    end: usize
) -> Result<Option<OutletId>, TractError>

fn quantize(
    &self,
    model: &ModelImpl<TypedFact, Box<dyn TypedOp + 'static>>,
    node: &BaseNode<TypedFact, Box<dyn TypedOp + 'static>>,
    dt: DatumType,
    scale: f32,
    zero_point: i32
) -> Result<Option<Box<dyn TypedOp + 'static>>, TractError>

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

fn nested_model_multipliers(
    &self,
    inputs: &[&TypedFact]
) -> Vec<(Cow<str>, f64)>

Nested model multipliers, with label (for profiling).