mean

Classes

MeanOperator

MeanOperator(ident: int, op: OperatorT, model: ModelT, subgraph: SubGraphT, prefix: str = 'aot', attributes: dict[str, str] = {})

MEAN operator.

This operator computes the mean of the input tensor along specified axes.

Parameters:

• ident

  (int) –

  Operator identifier.

• op

  (OperatorT) –

  Operator object from LiteRT model.

• model

  (ModelT) –

  LiteRT model object.

• subgraph

  (SubGraphT) –

  Subgraph object from LiteRT model.

• prefix

  (str, default: 'aot' ) –

  Prefix for generated files. Defaults to "aot".

• attributes

  (dict[str, str], default: {} ) –

  Attributes for template values. Defaults to {}.

Attributes

name property

name: str

Return the operator name.

input_indices property

input_indices: list[int]

Return the input tensor indices.

output_indices property

output_indices: list[int]

Return the output tensor indices.

input_tensors property

input_tensors: list[TensorT]

Return the input tensors.

output_tensors property

output_tensors: list[TensorT]

Return the output tensors.

Functions

compute_values

compute_values() -> dict[str, str]

Compute the values for the operator.

Returns:

• dict[str, str] –

  dict[str, str]: Computed values for the operator.

generate_source_code

generate_source_code(save_path: Path)

Generate the source code for the operator.

Parameters:

• save_path

  (Path) –

  Path to save the generated code.

Functions

fold_mean_multiplier

fold_mean_multiplier(base_multiplier, base_shift, count)

Fold 1 / count into base_multiplier and shift, such that the result is compatible with arm_nn_requantize().

Ensures shift is in [0, 31] and multiplier fits in Q31.

Parameters:

• base_multiplier

  (int) –

  Base multiplier to adjust.

• base_shift

  (int) –

  Base shift to adjust.

• count

  (int) –

  Count to fold into the multiplier.