module onnxrt.ops_cpu.op_if#

Inheritance diagram of mlprodict.onnxrt.ops_cpu.op_if

Short summary#

module mlprodict.onnxrt.ops_cpu.op_if

Runtime operator.

source on GitHub

Classes#

class

truncated documentation

If

If == If conditional Attributes

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__

_infer_shapes

_infer_types

_pick_shape

_pick_type

_run

Documentation#

Runtime operator.

source on GitHub

class mlprodict.onnxrt.ops_cpu.op_if.If(onnx_node, desc=None, **options)#

Bases: mlprodict.onnxrt.ops_cpu._op.OpRun

==

If conditional

Attributes

  • else_branch (required): Graph to run if condition is false. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the then_branch. default value cannot be automatically retrieved (GRAPH)

  • then_branch (required): Graph to run if condition is true. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the else_branch. default value cannot be automatically retrieved (GRAPH)

Inputs

  • cond (heterogeneous)B: Condition for the if

Outputs

Between 1 and 2147483647 outputs.

  • outputs (variadic)V: Values that are live-out to the enclosing scope. The return values in the then_branch and else_branch must be of the same data type. The then_branch and else_branch may produce tensors with the same element type and different shapes. If corresponding outputs from the then-branch and the else-branch have static shapes S1 and S2, then the shape of the corresponding output variable of the if-node (if present) must be compatible with both S1 and S2 as it represents the union of both possible shapes.For example, if in a model file, the the first output of then_branch is typed float tensor with shape [2] and the first output of else_branch is another float tensor with shape [3], If’s first output should have (a) no shape set, or (b) a shape of rank 1 with neither dim_value nor dim_param set, or (c) a shape of rank 1 with a unique dim_param. In contrast, the first output cannot have the shape [2] since [2] and [3] are not compatible.

Type Constraints

  • V tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(bfloat16)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128)), optional(seq(tensor(uint8))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(int8))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(bfloat16))), optional(seq(tensor(float16))), optional(seq(tensor(float))), optional(seq(tensor(double))), optional(seq(tensor(string))), optional(seq(tensor(bool))), optional(seq(tensor(complex64))), optional(seq(tensor(complex128))), optional(tensor(uint8)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(int8)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(bfloat16)), optional(tensor(float16)), optional(tensor(float)), optional(tensor(double)), optional(tensor(string)), optional(tensor(bool)), optional(tensor(complex64)), optional(tensor(complex128)): All Tensor, Sequence(Tensor), Optional(Tensor), and Optional(Sequence(Tensor)) types

  • B tensor(bool): Only bool

Version

Onnx name: If

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

Runtime implementation: If

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

_infer_shapes(cond, named_inputs=None)#

Should be overwritten.

source on GitHub

_infer_types(cond, named_inputs=None)#

Should be overwritten.

source on GitHub

_pick_shape(res, name)#
_pick_type(res, name)#
_run(cond, named_inputs=None)#

Should be overwritten.

source on GitHub

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