Equal#

Equal - 13#

Version

  • name: Equal (GitHub)

  • domain: main

  • since_version: 13

  • function: False

  • support_level: SupportType.COMMON

  • shape inference: True

This version of the operator has been available since version 13.

Summary

Returns the tensor resulted from performing the equal 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(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8) ): Constrain input types to all numeric tensors.

  • T1 in ( tensor(bool) ): Constrain output to boolean tensor.

Examples

default

import numpy as np
import onnx

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

x = (np.random.randn(3, 4, 5) * 10).astype(np.int32)
y = (np.random.randn(3, 4, 5) * 10).astype(np.int32)
z = np.equal(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_equal")

_equal_broadcast

import numpy as np
import onnx

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

x = (np.random.randn(3, 4, 5) * 10).astype(np.int32)
y = (np.random.randn(5) * 10).astype(np.int32)
z = np.equal(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_equal_bcast")

Equal - 11#

Version

  • name: Equal (GitHub)

  • domain: main

  • since_version: 11

  • function: False

  • support_level: SupportType.COMMON

  • shape inference: True

This version of the operator has been available since version 11.

Summary

Returns the tensor resulted from performing the equal 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), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8) ): Constrain input types to all numeric tensors.

  • T1 in ( tensor(bool) ): Constrain output to boolean tensor.

Equal - 7#

Version

  • name: Equal (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 equal 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), tensor(int32), tensor(int64) ): Constrain input to integral tensors.

  • T1 in ( tensor(bool) ): Constrain output to boolean tensor.

Equal - 1#

Version

  • name: Equal (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 equal 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: If set, defines the broadcast dimensions.

  • broadcast: 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), tensor(int32), tensor(int64) ): Constrain input to integral tensors.

  • T1 in ( tensor(bool) ): Constrain output to boolean tensor.