module onnxrt.ops_cpu.op_cum_sum#

Inheritance diagram of mlprodict.onnxrt.ops_cpu.op_cum_sum

Short summary#

module mlprodict.onnxrt.ops_cpu.op_cum_sum

Runtime operator.

source on GitHub

Classes#

class

truncated documentation

CumSum

CumSum ====== Performs cumulative sum of the input elements along the given axis. By default, it will do the sum inclusively …

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

to_python

Documentation#

Runtime operator.

source on GitHub

class mlprodict.onnxrt.ops_cpu.op_cum_sum.CumSum(onnx_node, desc=None, **options)#

Bases: OpRun

Performs cumulative sum of the input elements along the given axis. By default, it will do the sum inclusively meaning the first element is copied as is. Through an exclusive attribute, this behavior can change to exclude the first element. It can also perform summation in the opposite direction of the axis. For that, set reverse attribute to 1.

Example: `` input_x = [1, 2, 3] axis=0 output = [1, 3, 6] exclusive=1 output = [0, 1, 3] exclusive=0 reverse=1 output = [6, 5, 3] exclusive=1 reverse=1 output = [5, 3, 0] ``

Attributes

  • exclusive: If set to 1 will return exclusive sum in which the top element is not included. In other terms, if set to 1, the j-th output element would be the sum of the first (j-1) elements. Otherwise, it would be the sum of the first j elements. Default value is nameexclusivei0typeINT (INT)

  • reverse: If set to 1 will perform the sums in reverse direction. Default value is namereversei0typeINT (INT)

Inputs

  • x (heterogeneous)T: An input tensor that is to be processed.

  • axis (heterogeneous)T2: A 0-D tensor. Must be in the range [-rank(x), rank(x)-1]. Negative value means counting dimensions from the back.

Outputs

  • y (heterogeneous)T: Output tensor of the same type as ‘x’ with cumulative sums of the x’s elements

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.

  • T2 tensor(int32), tensor(int64): axis tensor can be int32 or int64 only

Version

Onnx name: CumSum

This version of the operator has been available since version 14.

Runtime implementation: CumSum

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

Should be overwritten.

source on GitHub

to_python(inputs)#

Returns a python code equivalent to this operator.

Parameters:

inputs – inputs name

Returns:

imports, python code, both as strings

source on GitHub