module onnxrt.ops_cpu.op_mod#

Inheritance diagram of mlprodict.onnxrt.ops_cpu.op_mod

Short summary#

module mlprodict.onnxrt.ops_cpu.op_mod

Runtime operator.

source on GitHub

Classes#

class

truncated documentation

Mod

Mod === Performs element-wise binary modulus (with Numpy-style broadcasting support). The sign of the remainder is 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

to_python

Documentation#

Runtime operator.

source on GitHub

class mlprodict.onnxrt.ops_cpu.op_mod.Mod(onnx_node, desc=None, **options)#

Bases: OpRun

===

Performs element-wise binary modulus (with Numpy-style broadcasting support).

The sign of the remainder is the same as that of the Divisor.

Mod operator can also behave like C fmod() or numpy.fmod. In this case, the sign of the remainder however, will be the same as the Dividend (in contrast to integer mod). To force a behavior like numpy.fmod() an ‘fmod’ Attribute is provided. This attribute is set to 0 by default causing the behavior to be like integer mod. Setting this attribute to 1 causes the remainder to be calculated similar to that of numpy.fmod().

If the input type is floating point, then fmod attribute must be set to 1.

In case of dividend being zero, the results will be platform dependent.

This operator supports multidirectional (i.e., Numpy-style) broadcasting; for more details please check Broadcasting in ONNX.

Attributes

  • fmod: Whether the operator should behave like fmod (default=0 meaning it will do integer mods); Set this to 1 to force fmod treatment Default value is namefmodi0typeINT (INT)

Inputs

  • A (heterogeneous)T: Dividend tensor

  • B (heterogeneous)T: Divisor tensor

Outputs

  • C (heterogeneous)T: Remainder tensor

Type Constraints

  • T tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), 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: Mod

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

Runtime implementation: Mod

__init__(onnx_node, desc=None, **options)#
_run(a, b, 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