🥮 mooncakes.io
README

mizchi/webnn/litert does not have a README file

#
LiteRtError

source
pub suberror LiteRtError {
  LiteRtError(String)
}

#
LiteRtError::to_string

source
fn LiteRtError::to_string(self : LiteRtError) -> String

#
LiteRtLoweredValue

source
pub struct LiteRtLoweredValue[T] {
  name_ : String
  tensor_ : T
}

#
LiteRtLoweredValue::name

source
fn[T] LiteRtLoweredValue::name(self : LiteRtLoweredValue[T]) -> String

#
LiteRtLoweredValue::tensor

source
fn[T] LiteRtLoweredValue::tensor(self : LiteRtLoweredValue[T]) -> T

#
LiteRtModel

source
pub struct LiteRtModel {
  values_ : Array[LiteRtValue]
  nodes_ : Array[LiteRtNode]
  output_names_ : Array[String]
}

#
LiteRtModel::inputs

source
fn LiteRtModel::inputs(self : LiteRtModel) -> Array[LiteRtValue]

#
LiteRtModel::lower

source
fn[T : 
TensorOps] LiteRtModel::lower(self : LiteRtModel, make_input : (String, 
Shape) -> T raise, make_constant : (String, 
Shape, Array[Float]) -> T raise) -> Array[LiteRtLoweredValue[T]] raise

#
LiteRtModel::new

source
fn LiteRtModel::new(values : Array[LiteRtValue], nodes : Array[LiteRtNode], output_names : Array[String]) -> LiteRtModel raise LiteRtError

#
LiteRtNode

source
pub(all) enum LiteRtNode {
  Add(String, String, String)
  Sub(String, String, String)
  Mul(String, String, String)
  Div(String, String, String)
  ReduceMean(String, String, Array[Int], Bool)
  Gather(String, String, Array[Int], 
Shape, Int)
  Slice(String, String, Array[Int], Array[Int])
  Gelu(String, String)
  LayerNormalization(String, String, String, String, Array[Int], Float)
  Concat(String, String, String, Int)
  Matmul(String, String, String)
  Conv2d(String, String, String, 
Conv2dOptions)
  MaxPool2d(String, String, 
Pool2dOptions)
  AveragePool2d(String, String, 
Pool2dOptions)
  Sigmoid(String, String)
  Tanh(String, String)
  Clamp(String, String, Float, Float)
  Relu(String, String)
  Softmax(String, String, Int)
  Reshape(String, String)
  Transpose(String, String, Array[Int])
}

#
LiteRtValue

source
pub struct LiteRtValue {
  name_ : String
  shape_ : 
Shape
  source_ : LiteRtValueSource
}

#
LiteRtValue::constant

source
fn LiteRtValue::constant(name : String, shape : 
Shape, values : Array[Float]) -> LiteRtValue raise LiteRtError

#
LiteRtValue::input

source
fn LiteRtValue::input(name : String, shape : 
Shape) -> LiteRtValue raise LiteRtError

#
LiteRtValue::intermediate

source
fn LiteRtValue::intermediate(name : String, shape : 
Shape) -> LiteRtValue raise LiteRtError

#
LiteRtValue::name

source
fn LiteRtValue::name(self : LiteRtValue) -> String

#
LiteRtValue::shape

source

#
LiteRtValueSource

source
pub enum LiteRtValueSource {
  Input
  Constant(Array[Float])
  Intermediate
}

#
TfliteModel

source
pub(all) struct TfliteModel {
}

#
TfliteModel::parse

source
fn TfliteModel::parse(bytes : Bytes) -> LiteRtModel raise LiteRtError

#
TfliteModel::parse_runtime

source
fn TfliteModel::parse_runtime(bytes : Bytes) -> TfliteRuntimeModel raise LiteRtError

Parse a TFLite model while retaining UINT8/INT8 runtime I/O metadata.

#
TfliteRuntimeDataType

source
pub enum TfliteRuntimeDataType {
  Float32
  UInt8(Array[Float], Array[Int], Int)
  Int8(Array[Float], Array[Int], Int)
}

#
TfliteRuntimeModel

source
pub struct TfliteRuntimeModel {
  model_ : LiteRtModel
  inputs_ : Array[TfliteRuntimeTensor]
  outputs_ : Array[TfliteRuntimeTensor]
}

#
TfliteRuntimeModel::inputs

source

#
TfliteRuntimeModel::model

source

#
TfliteRuntimeModel::outputs

source

#
TfliteRuntimeTensor

source
pub struct TfliteRuntimeTensor {
  name_ : String
  shape_ : 
Shape
  data_type_ : TfliteRuntimeDataType
}

#
TfliteRuntimeTensor::data_type

source

#
TfliteRuntimeTensor::dequantize

source
fn TfliteRuntimeTensor::dequantize(self : TfliteRuntimeTensor, values : Array[Int]) -> Array[Float] raise LiteRtError

Dequantize raw runtime values according to this tensor's TFLite metadata.

#
TfliteRuntimeTensor::name

source
fn TfliteRuntimeTensor::name(self : TfliteRuntimeTensor) -> String

#
TfliteRuntimeTensor::requantize

source
fn TfliteRuntimeTensor::requantize(self : TfliteRuntimeTensor, values : Array[Float]) -> Array[Int] raise LiteRtError

Requantize float32 backend results according to this tensor's TFLite metadata.

#
TfliteRuntimeTensor::shape

source

#
dequantize_int8

source
fn dequantize_int8(values : Array[Int], scale : Float, zero_point : Int) -> Array[Float] raise LiteRtError

Convert signed 8-bit affine values to their float32 values.

#
dequantize_uint8

source
fn dequantize_uint8(values : Array[Int], scale : Float, zero_point : Int) -> Array[Float] raise LiteRtError

Convert unsigned 8-bit affine values to their float32 values.

#
requantize_int8

source
fn requantize_int8(values : Array[Float], scale : Float, zero_point : Int) -> Array[Int] raise LiteRtError

Convert float32 values to signed 8-bit affine values with saturation.

#
requantize_uint8

source
fn requantize_uint8(values : Array[Float], scale : Float, zero_point : Int) -> Array[Int] raise LiteRtError

Convert float32 values to unsigned 8-bit affine values with saturation.

Source Files

Powered by MoonBit

Site sourceReport issuePackagesBuild queueSkillsStatistics

© 2026 mooncakes.io