module onnxrt.ops_cpu.op_greater

Short summary

module mlprodict.onnxrt.ops_cpu.op_greater

Runtime operator.

source on GitHub

Classes

class	truncated documentation
Greater	Greater ======= Returns the tensor resulted from performing the greater logical operation elementwise on the input tensors …
GreaterOrEqual	GreaterOrEqual ============== Returns the tensor resulted from performing the greater_equal logical operation elementwise …

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	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_default_modified	Returns the list of modified parameters.
args_mandatory	Returns the list of optional arguments.
args_mandatory	Returns the list of optional arguments.
args_optional	Returns the list of optional arguments.
args_optional	Returns the list of optional arguments.
atts_value	Returns all parameters in a dictionary.
atts_value	Returns all parameters in a dictionary.

Methods

method	truncated documentation
__init__
__init__
_run
_run
to_python
to_python

Documentation

Runtime operator.

source on GitHub

class mlprodict.onnxrt.ops_cpu.op_greater.Greater(onnx_node, desc=None, **options)

Bases: OpRunBinaryComparison

---

Returns the tensor resulted from performing the greater logical operation elementwise on the input tensors A and B (with Numpy-style broadcasting support).

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

Inputs

• A (heterogeneous)T: First input operand for the logical operator.

• B (heterogeneous)T: Second input operand for the logical operator.

Outputs

• C (heterogeneous)T1: Result 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 types to all numeric tensors.

• T1 tensor(bool): Constrain output to boolean tensor.

Version

Onnx name: Greater

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

Runtime implementation: Greater

__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

class mlprodict.onnxrt.ops_cpu.op_greater.GreaterOrEqual(onnx_node, desc=None, **options)

Bases: OpRunBinaryComparison

---

Returns the tensor resulted from performing the greater_equal logical operation elementwise on the input tensors A and B (with Numpy-style broadcasting support).

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

Inputs

• A (heterogeneous)T: First input operand for the logical operator.

• B (heterogeneous)T: Second input operand for the logical operator.

Outputs

• C (heterogeneous)T1: Result 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 types to all numeric tensors.

• T1 tensor(bool): Constrain output to boolean tensor.

Version

Onnx name: GreaterOrEqual

This version of the operator has been available since version 16.

Runtime implementation: GreaterOrEqual

__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