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__

_pick_type

_run

need_context

Tells the runtime if this node needs the context (all the results produced so far) as it may silently access …

Documentation#

Runtime operator.

source on GitHub

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

Bases: 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

__init__(onnx_node, desc=None, **options)#
_pick_type(res, name)#
_run(cond, named_inputs=None, context=None, attributes=None, verbose=0, fLOG=None)#

Should be overwritten.

source on GitHub

need_context()#

Tells the runtime if this node needs the context (all the results produced so far) as it may silently access one of them (operator Loop). The default answer is False.

source on GitHub