module onnxrt.ops_cpu.op_reduce_sum_square#

Inheritance diagram of mlprodict.onnxrt.ops_cpu.op_reduce_sum_square

Short summary#

module mlprodict.onnxrt.ops_cpu.op_reduce_sum_square

Runtime operator.

source on GitHub

Classes#

class

truncated documentation

ReduceSumSquare

ReduceSumSquare =============== Computes the sum square of the input tensor’s element along the provided axes. The resulted …

Properties#

property

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args_default

Returns the list of arguments as well as the list of parameters with the default values (close to the signature). …

args_default_modified

Returns the list of modified parameters.

args_mandatory

Returns the list of optional arguments.

args_optional

Returns the list of optional arguments.

atts_value

Returns all parameters in a dictionary.

Methods#

method

truncated documentation

__init__

_run

Documentation#

Runtime operator.

source on GitHub

class mlprodict.onnxrt.ops_cpu.op_reduce_sum_square.ReduceSumSquare(onnx_node, desc=None, **options)#

Bases: mlprodict.onnxrt.ops_cpu._op.OpRunReduceNumpy

Computes the sum square of the input tensor’s element along the provided axes. The resulted tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, then the resulted tensor have the reduced dimension pruned.

The above behavior is similar to numpy, with the exception that numpy default keepdims to False instead of True.

Attributes

  • axes: A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data). default value cannot be automatically retrieved (INTS)

  • keepdims: Keep the reduced dimension or not, default 1 mean keep reduced dimension. Default value is namekeepdimsi1typeINT (INT)

Inputs

  • data (heterogeneous)T: An input tensor.

Outputs

  • reduced (heterogeneous)T: Reduced output tensor.

Type Constraints

  • T tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16): Constrain input and output types to high-precision numeric tensors.

Version

Onnx name: ReduceSumSquare

This version of the operator has been available since version 13.

Runtime implementation: ReduceSumSquare

Parameters

  • onnx_node – onnx node

  • desc – internal representation

  • expected_attributes – expected attributes for this node

  • options – runtime options

source on GitHub

__init__(onnx_node, desc=None, **options)#
Parameters

  • onnx_node – onnx node

  • desc – internal representation

  • expected_attributes – expected attributes for this node

  • options – runtime options

source on GitHub

_run(data)#

Should be overwritten.

source on GitHub

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