module onnxrt.ops_cpu.op_reduce_log_sum_exp#

Inheritance diagram of mlprodict.onnxrt.ops_cpu.op_reduce_log_sum_exp

Short summary#

module mlprodict.onnxrt.ops_cpu.op_reduce_log_sum_exp

Runtime operator.

source on GitHub

Classes#

class

truncated documentation

ReduceLogSumExp

ReduceLogSumExp =============== Computes the log sum exponent of the input tensor’s element along the provided axes. The …

Properties#

property

truncated documentation

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_log_sum_exp.ReduceLogSumExp(onnx_node, desc=None, **options)#

Bases: OpRunReduceNumpy

Computes the log sum exponent of the input tensor’s element along the provided axes. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then the resulting tensor has the reduced dimension pruned.

The above behavior is similar to numpy, with the exception that numpy defaults 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 means 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: ReduceLogSumExp

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

Runtime implementation: ReduceLogSumExp

__init__(onnx_node, desc=None, **options)#
_run(data, attributes=None, verbose=0, fLOG=None)#

Should be overwritten.

source on GitHub