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')