openvino/docs/ops/detection/PriorBoxClustered_1.md

PriorBoxClustered

Versioned name: PriorBoxClustered-1

Category: Object detection

Short description: PriorBoxClustered operation generates prior boxes of specified sizes normalized to the input image size.

Detailed description

Let \f[ W \equiv image_width, \quad H \equiv image_height. \f]

Then calculations of PriorBoxClustered can be written as \f[ center_x=(w+offset)*step \f] \f[ center_y=(h+offset)*step \f] \f[ w \subset \left( 0, W \right ) \f] \f[ h \subset \left( 0, H \right ) \f] For each \f$s = \overline{0, W - 1}\f$ calculates the prior boxes coordinates: \f[ xmin = \frac{center_x - \frac{width_s}{2}}{W} \f] \f[ ymin = \frac{center_y - \frac{height_s}{2}}{H} \f] \f[ xmax = \frac{center_x - \frac{width_s}{2}}{W} \f] \f[ ymax = \frac{center_y - \frac{height_s}{2}}{H} \f] If clip is defined, the coordinates of prior boxes are recalculated with the formula: \f$coordinate = \min(\max(coordinate,0), 1)\f$

Attributes

• width (height)

  • Description: width (height) specifies desired boxes widths (heights) in pixels.
  • Range of values: floating-point positive numbers
  • Type: float[]
  • Default value: 1.0
  • Required: no

• clip

  • Description: clip is a flag that denotes if each value in the output tensor should be clipped within [0,1].
  • Range of values:
    • false or 0 - clipping is not performed
    • true or 1 - each value in the output tensor is within [0,1]
  • Type: boolean
  • Default value: true
  • Required: no

• step (step_w, step_h)

  • Description: step (step_w, step_h) is a distance between box centers. For example, step equal 85 means that the distance between neighborhood prior boxes centers is 85. If both step_h and step_w are 0 then they are updated with value of step. If after that they are still 0 then they are calculated as input image width(height) divided with first input width(height).
  • Range of values: floating-point positive number
  • Type: float
  • Default value: 0.0
  • Required: no

• offset

  • Description: offset is a shift of box respectively to top left corner. For example, offset equal 85 means that the shift of neighborhood prior boxes centers is 85.
  • Range of values: floating-point positive number
  • Type: float
  • Required: yes

• variance

  • Description: variance denotes a variance of adjusting bounding boxes. The attribute could be 0, 1 or 4 elements.
  • Range of values: floating-point positive numbers
  • Type: float[]
  • Default value: []
  • Required: no

Inputs:

• 1: output_size - 1D tensor of type T_INT with two elements [height, width]. Specifies the spatial size of generated grid with boxes. Required.

• 2: image_size - 1D tensor of type T_INT with two elements [image_height, image_width] that specifies shape of the image for which boxes are generated. Optional.

Outputs:

• 1: 2D tensor of shape [2, 4 * height * width * priors_per_point] and type T_OUT with box coordinates. The priors_per_point is the number of boxes generated per each grid element. The number depends on layer attribute values.

Types

• T_INT: any supported integer type.
• T_OUT: supported floating-point type.

Example

<layer type="PriorBoxClustered" ... >
    <data clip="false" height="44.0,10.0,30.0,19.0,94.0,32.0,61.0,53.0,17.0" offset="0.5" step="16.0" variance="0.1,0.1,0.2,0.2" width="86.0,13.0,57.0,39.0,68.0,34.0,142.0,50.0,23.0"/>
    <input>
        <port id="0">
            <dim>2</dim>        <!-- [10, 19] -->
        </port>
        <port id="1">
            <dim>2</dim>        <!-- [180, 320] -->
        </port>
    </input>
    <output>
        <port id="2">
            <dim>2</dim>
            <dim>6840</dim>
        </port>
    </output>
</layer>