Equal

Versioned name: Equal-1

Category: Comparison binary

Short description: Equal performs element-wise comparison operation with two given input tensors applying multi-directional broadcast rules specified in the auto_broadcast attribute.

Detailed description Before performing arithmetic operation, input tensors a and b are broadcasted if their shapes are different and auto_broadcast attributes is not none. Broadcasting is performed according to auto_broadcast value.

After broadcasting Equal does the following with the input tensors a and b:

\[o_{i} = a_{i} == b_{i}\]

Attributes:

• auto_broadcast

  • Description: specifies rules used for auto-broadcasting of input tensors.

  • Range of values:

    • none - no auto-broadcasting is allowed, all input shapes should match,

    • numpy - numpy broadcasting rules, description is available in Broadcast Rules For Elementwise Operations,

    • pdpd - PaddlePaddle-style implicit broadcasting, description is available in Broadcast Rules For Elementwise Operations.

  • Type: string

  • Default value: “numpy”

  • Required: no

Inputs

• 1: A tensor of type T and arbitrary shape. Required.

• 2: A tensor of type T and arbitrary shape. Required.

Outputs

• 1: The result of element-wise comparison operation applied to the input tensors. A tensor of type T_BOOL and the same shape equal to broadcasted shape of two inputs.

Types

• T: arbitrary supported type.

• T_BOOL: boolean.

Examples

Example 1: no broadcast

<layer ... type="Equal">
    <data auto_broadcast="none"/>
    <input>
        <port id="0">
            <dim>256</dim>
            <dim>56</dim>
        </port>
        <port id="1">
            <dim>256</dim>
            <dim>56</dim>
        </port>
    </input>
    <output>
        <port id="2">
            <dim>256</dim>
            <dim>56</dim>
        </port>
    </output>
</layer>

Example 2: numpy broadcast

<layer ... type="Equal">
    <data auto_broadcast="numpy"/>
    <input>
        <port id="0">
            <dim>8</dim>
            <dim>1</dim>
            <dim>6</dim>
            <dim>1</dim>
        </port>
        <port id="1">
            <dim>7</dim>
            <dim>1</dim>
            <dim>5</dim>
        </port>
    </input>
    <output>
        <port id="2">
            <dim>8</dim>
            <dim>7</dim>
            <dim>6</dim>
            <dim>5</dim>
        </port>
    </output>
</layer>