Or

Or - 7

Version

  • name: Or (GitHub)

  • domain: main

  • since_version: 7

  • function: False

  • support_level: SupportType.COMMON

  • shape inference: True

This version of the operator has been available since version 7.

Summary

Returns the tensor resulted from performing the or logical operation elementwise on the input tensors A and B (with Numpy-style broadcasting support).

This operator supports multidirectional (i.e., Numpy-style) broadcasting; for more details please check Broadcasting in ONNX.

Inputs

  • A (heterogeneous) - T:

    First input operand for the logical operator.

  • B (heterogeneous) - T:

    Second input operand for the logical operator.

Outputs

  • C (heterogeneous) - T1:

    Result tensor.

Type Constraints

  • T in ( tensor(bool) ):

    Constrain input to boolean tensor.

  • T1 in ( tensor(bool) ):

    Constrain output to boolean tensor.

Examples

default

import numpy as np
import onnx

node = onnx.helper.make_node(
    "Or",
    inputs=["x", "y"],
    outputs=["or"],
)

# 2d
x = (np.random.randn(3, 4) > 0).astype(bool)
y = (np.random.randn(3, 4) > 0).astype(bool)
z = np.logical_or(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_or2d")

# 3d
x = (np.random.randn(3, 4, 5) > 0).astype(bool)
y = (np.random.randn(3, 4, 5) > 0).astype(bool)
z = np.logical_or(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_or3d")

# 4d
x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
y = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
z = np.logical_or(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_or4d")

_or_broadcast

import numpy as np
import onnx

node = onnx.helper.make_node(
    "Or",
    inputs=["x", "y"],
    outputs=["or"],
)

# 3d vs 1d
x = (np.random.randn(3, 4, 5) > 0).astype(bool)
y = (np.random.randn(5) > 0).astype(bool)
z = np.logical_or(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_or_bcast3v1d")

# 3d vs 2d
x = (np.random.randn(3, 4, 5) > 0).astype(bool)
y = (np.random.randn(4, 5) > 0).astype(bool)
z = np.logical_or(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_or_bcast3v2d")

# 4d vs 2d
x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
y = (np.random.randn(5, 6) > 0).astype(bool)
z = np.logical_or(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_or_bcast4v2d")

# 4d vs 3d
x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
y = (np.random.randn(4, 5, 6) > 0).astype(bool)
z = np.logical_or(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_or_bcast4v3d")

# 4d vs 4d
x = (np.random.randn(1, 4, 1, 6) > 0).astype(bool)
y = (np.random.randn(3, 1, 5, 6) > 0).astype(bool)
z = np.logical_or(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_or_bcast4v4d")

Or - 1

Version

  • name: Or (GitHub)

  • domain: main

  • since_version: 1

  • function: False

  • support_level: SupportType.COMMON

  • shape inference: True

This version of the operator has been available since version 1.

Summary

Returns the tensor resulted from performing the or logical operation elementwise on the input tensors A and B.

If broadcasting is enabled, the right-hand-side argument will be broadcasted to match the shape of left-hand-side argument. See the doc of Add for a detailed description of the broadcasting rules.

Attributes

  • axis - INT :

    If set, defines the broadcast dimensions.

  • broadcast - INT (default is 0):

    Enable broadcasting

Inputs

  • A (heterogeneous) - T:

    Left input tensor for the logical operator.

  • B (heterogeneous) - T:

    Right input tensor for the logical operator.

Outputs

  • C (heterogeneous) - T1:

    Result tensor.

Type Constraints

  • T in ( tensor(bool) ):

    Constrain input to boolean tensor.

  • T1 in ( tensor(bool) ):

    Constrain output to boolean tensor.