# 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")
```