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>

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