If#

  • Domain: ai.onnx

  • Since version: 13

If conditional

Inputs

  • cond (B): Condition for the if. The tensor must contain a single element.

Outputs

  • outputs (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 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: All Tensor and Sequence types Allowed types: seq(tensor(bool)), seq(tensor(complex128)), seq(tensor(complex64)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(string)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8)), tensor(bool), tensor(complex128), tensor(complex64), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8).

  • B: Only bool Allowed types: tensor(bool).

Examples#

test_cc_if

Attributes:
  then_branch = <subgraph>
  else_branch = <subgraph>

Inputs:
  cond: shape=(), dtype=bool
    True

Outputs:
  res: shape=(2,), dtype=float32
    [1., 2.]

test_cc_if_else

Attributes:
  then_branch = <subgraph>
  else_branch = <subgraph>

Inputs:
  cond: shape=(), dtype=bool
    False

Outputs:
  res: shape=(2,), dtype=float32
    [3., 4.]

Differences with previous version (11)#

SchemaDiff: If (domain 'ai.onnx')

  • old version: 11

  • new version: 13

  • breaking: no

Type constraints:

  • changed ‘V’: added types: [‘seq(tensor(bool))’, ‘seq(tensor(complex128))’, ‘seq(tensor(complex64))’, ‘seq(tensor(double))’, ‘seq(tensor(float))’, ‘seq(tensor(float16))’, ‘seq(tensor(int16))’, ‘seq(tensor(int32))’, ‘seq(tensor(int64))’, ‘seq(tensor(int8))’, ‘seq(tensor(string))’, ‘seq(tensor(uint16))’, ‘seq(tensor(uint32))’, ‘seq(tensor(uint64))’, ‘seq(tensor(uint8))’]

Version History#