openvino/docs/ops/arithmetic/FloorMod_1.md

FloorMod

Versioned name: FloorMod-1

Category: Arithmetic binary operation

Short description: FloorMod performs an element-wise floor modulo operation with two given tensors applying broadcasting rule specified in the auto_broadcast attribute.

Detailed description As a first step input tensors a and b are broadcasted if their shapes differ. Broadcasting is performed according to auto_broadcast attribute specification. As a second step FloorMod operation is computed element-wise on the input tensors a and b according to the formula below:

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

FloorMod operation computes a reminder of a floored division. It is the same behaviour like in Python programming language: floor(x / y) * y + floor_mod(x, y) = x. The sign of the result is equal to a sign of a divisor. The result of division by zero is undefined.

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 must match
    • numpy - numpy broadcasting rules, 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 floor modulo operation. A tensor of type T with shape equal to broadcasted shape of two inputs.

Types

• T: any numeric type.

Examples

Example 1 - no broadcasting

<layer ... type="FloorMod">
    <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 broadcasting

<layer ... type="FloorMod">
    <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>