openvino/docs/ops/arithmetic/Divide_1.md

Divide

Versioned name: Divide-1

Category: Arithmetic binary

Short description: Divide performs element-wise division operation with two given tensors applying broadcasting rule specified in the auto_broacast attribute.

Detailed description Before performing arithmetic operation, input tensors a and b are broadcasted if their shapes are different and auto_broadcast attribute is not none. Broadcasting is performed according to auto_broadcast value. After broadcasting Divide performs division operation for the input tensors a and b using the formula below:

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

The result of division by zero is undefined.

Attributes:

• pythondiv

  • Description: specifies if floor division should be calculate. This attribute is supported only for integer data types.
  • Range of values:
    • false - regular division
    • true - floor division
  • Type: boolean
  • Default value: true
  • Required: no

• auto_broadcast

  • Description: specifies rules used for auto-broadcasting of input tensors.
  • Range of values:
    • none - no auto-broadcasting is allowed, all input shapes must 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 and rank. Required.
• 2: A tensor of type T and arbitrary shape and rank. Required.

Outputs

• 1: The result of element-wise division operation. A tensor of type T with shape equal to broadcasted shape of the two inputs.

Types

• T: any numeric type.

Examples

Example 1

<layer ... type="Divide">
    <data auto_broadcast="none" m_pythondiv="true"/>
    <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: broadcast

<layer ... type="Divide">
    <data auto_broadcast="numpy" m_pythondiv="false"/>
    <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>