Struct tract_hir::ops::cnn::ConvUnary

pub struct ConvUnary {
    pub pool_spec: PoolSpec,
    pub kernel_fmt: KernelFormat,
    pub kernel: Arc<Tensor>,
    pub group: usize,
    pub bias: Option<Arc<Tensor>>,
    pub q_params: Option<(DatumType, MatMulQParams)>,
}

Fields

pool_spec: PoolSpec

kernel_fmt: KernelFormat

kernel: Arc<Tensor>

group: usize

bias: Option<Arc<Tensor>>

q_params: Option<(DatumType, MatMulQParams)>

Implementations

impl ConvUnary

pub fn kernel_as_group_o_ihw(&self) -> Result<Arc<Tensor>, Error>

pub unsafe fn wire_as_quant_im2col(
    &self,
    model: &mut Graph<TypedFact, Box<dyn TypedOp + 'static, Global>>,
    name: &str,
    b_dt: DatumType,
    wires: &[OutletId]
) -> Result<OutletId, Error>

pub unsafe fn wire_as_im2col_pair(
    &self,
    model: &mut Graph<TypedFact, Box<dyn TypedOp + 'static, Global>>,
    name: &str,
    wire: OutletId
) -> Result<OutletId, Error>

pub unsafe fn wire_as_direct(
    &self,
    model: &mut Graph<TypedFact, Box<dyn TypedOp + 'static, Global>>,
    name: &str,
    wire: OutletId,
    input_shape: &[usize]
) -> Result<OutletId, Error>

pub fn to_depth_wise<T>(
    &self,
    input: &TypedFact
) -> Result<Box<dyn TypedOp + 'static, Global>, Error> where
    T: Datum + Clone + LinalgScalar + PartialEq<T> + Sum<T>, 

impl ConvUnary

pub fn new(
    pool_spec: PoolSpec,
    kernel_fmt: KernelFormat,
    kernel: Arc<Tensor>,
    group: usize,
    bias: Option<Arc<Tensor>>,
    q_params: Option<(DatumType, MatMulQParams)>
) -> ConvUnary

Constructs a new ConvUnary.

Trait Implementations

impl Clone for ConvUnary

pub fn clone(&self) -> ConvUnary

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for ConvUnary

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

Formats the value using the given formatter.

impl DynHash for ConvUnary

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

impl EvalOp for ConvUnary

pub fn is_stateless(&self) -> bool

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

fn state(
    &self,
    session: &mut SessionState,
    node_id: usize
) -> Result<Option<Box<dyn OpState + 'static, Global>>, Error>

impl Hash for ConvUnary

pub 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 Op for ConvUnary

pub fn name(&self) -> Cow<'_, str>

pub fn info(&self) -> Result<Vec<String, Global>, Error>

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

pub fn validation(&self) -> Validation

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

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

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

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

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

Compare two ops.

impl TypedOp for ConvUnary

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

Deduce output facts from input facts.

pub fn invariants(
    &self,
    inputs: &[&TypedFact],
    _outputs: &[&TypedFact]
) -> Result<Invariants, Error>

pub fn declutter(
    &self,
    model: &Graph<TypedFact, Box<dyn TypedOp + 'static, Global>>,
    node: &Node<TypedFact, Box<dyn TypedOp + 'static, Global>>
) -> Result<Option<ModelPatch<TypedFact, Box<dyn TypedOp + 'static, Global>>>, Error>

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

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

Computes a cost hint of the operation.

pub fn change_axes(
    &self,
    model: &Graph<TypedFact, Box<dyn TypedOp + 'static, Global>>,
    node: &Node<TypedFact, Box<dyn TypedOp + 'static, Global>>,
    _io: InOut,
    change: &AxisOp
) -> Result<Option<AxisChangeConsequence>, Error>

pub fn codegen(
    &self,
    model: &Graph<TypedFact, Box<dyn TypedOp + 'static, Global>>,
    node: &Node<TypedFact, Box<dyn TypedOp + 'static, Global>>
) -> Result<Option<ModelPatch<TypedFact, Box<dyn TypedOp + 'static, Global>>>, Error>

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

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

Reinterpret the TypedOp as an Op.

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

Reinterpret the TypedOp as an Op, mutably.

fn fuse(
    &self,
    _model: &Graph<TypedFact, Box<dyn TypedOp + 'static, Global>>,
    _node: &Node<TypedFact, Box<dyn TypedOp + 'static, Global>>
) -> Result<Option<ModelPatch<TypedFact, Box<dyn TypedOp + 'static, Global>>>, Error>

Fuse op after codegen to deal with local optimisations.

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

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

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

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

fn concretize_dims(
    &self,
    source: &Graph<TypedFact, Box<dyn TypedOp + 'static, Global>>,
    node: &Node<TypedFact, Box<dyn TypedOp + 'static, Global>>,
    target: &mut Graph<TypedFact, Box<dyn TypedOp + 'static, Global>>,
    mapping: &HashMap<OutletId, OutletId, RandomState>,
    values: &SymbolValues
) -> Result<SmallVec<[OutletId; 4]>, Error>

Transform the op into by providing a value to one or more symbols.

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

Nested model multipliers, with label (for profiling).