IsInf#

IsInf - 10#

Version

  • name: IsInf (GitHub)

  • domain: main

  • since_version: 10

  • function: False

  • support_level: SupportType.COMMON

  • shape inference: True

This version of the operator has been available since version 10.

Summary

Map infinity to true and other values to false.

Attributes

  • detect_negative: (Optional) Whether map negative infinity to true. Default to 1 so that negative infinity induces true. Set this attribute to 0 if negative infinity should be mapped to false. Default value is 1.

  • detect_positive: (Optional) Whether map positive infinity to true. Default to 1 so that positive infinity induces true. Set this attribute to 0 if positive infinity should be mapped to false. Default value is 1.

Inputs

  • X (heterogeneous) - T1: input

Outputs

  • Y (heterogeneous) - T2: output

Type Constraints

  • T1 in ( tensor(double), tensor(float) ): Constrain input types to float tensors.

  • T2 in ( tensor(bool) ): Constrain output types to boolean tensors.

Examples

_infinity

node = onnx.helper.make_node('IsInf',
                             inputs=['x'],
                             outputs=['y'],
                             )

x = np.array([-1.2, np.nan, np.inf, 2.8, np.NINF, np.inf],
             dtype=np.float32)
y = np.isinf(x)
expect(node, inputs=[x], outputs=[y], name='test_isinf')

_positive_infinity_only

node = onnx.helper.make_node('IsInf',
                             inputs=['x'],
                             outputs=['y'],
                             detect_negative=0
                             )

x = np.array([-1.7, np.nan, np.inf, 3.6, np.NINF, np.inf],
             dtype=np.float32)
y = np.isposinf(x)
expect(node, inputs=[x], outputs=[y], name='test_isinf_positive')

_negative_infinity_only

node = onnx.helper.make_node('IsInf',
                             inputs=['x'],
                             outputs=['y'],
                             detect_positive=0
                             )

x = np.array([-1.7, np.nan, np.inf, -3.6, np.NINF, np.inf],
             dtype=np.float32)
y = np.isneginf(x)
expect(node, inputs=[x], outputs=[y], name='test_isinf_negative')