IsInf

IsInf - 20

Version

  • name: IsInf (GitHub)

  • domain: main

  • since_version: 20

  • function: False

  • support_level: SupportType.COMMON

  • shape inference: True

This version of the operator has been available since version 20.

Summary

Map infinity to true and other values to false.

Attributes

  • detect_negative - INT (default is 1):

    (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 - INT (default is 1):

    (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(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz) ):

    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.inf, 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.inf, 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.inf, np.inf], dtype=np.float32)
y = np.isneginf(x)
expect(node, inputs=[x], outputs=[y], name="test_isinf_negative")

_infinity_float16

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.inf, np.inf], dtype=np.float16)
y = np.isinf(x)
expect(node, inputs=[x], outputs=[y], name="test_isinf_float16")

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 - INT (default is 1):

    (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 - INT (default is 1):

    (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.