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.
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.

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

import numpy as np
import onnx

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

import numpy as np
import onnx

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

import numpy as np
import onnx

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")

IsInf#

IsInf - 10#

IsInf - 10 #