openvino/docs/ops/detection/PriorBox_1.md

PriorBox

Versioned name: PriorBox-1

Category: Object detection

Short description: PriorBox operation generates prior boxes of specified sizes and aspect ratios across all dimensions.

Detailed description:

PriorBox computes coordinates of prior boxes by following:

1. First calculates center_x and center_y of prior box: \f[ W \equiv Width \quad Of \quad Image \ H \equiv Height \quad Of \quad Image \f]
   • If step equals 0: \f[ center_x=(w+0.5) \ center_y=(h+0.5) \f]
   • else: \f[ center_x=(w+offset)*step \ center_y=(h+offset)*step \ w \subset \left( 0, W \right ) \ h \subset \left( 0, H \right ) \f]
2. Then, for each \f$ s \subset \left( 0, min_sizes \right ) \f$ calculates coordinates of prior boxes: \f[ xmin = \frac{\frac{center_x - s}{2}}{W} \f] \f[ ymin = \frac{\frac{center_y - s}{2}}{H} \f] \f[ xmax = \frac{\frac{center_x + s}{2}}{W} \f] \f[ ymin = \frac{\frac{center_y + s}{2}}{H} \f]
3. If clip attribute is set to true, each output value is clipped between \f$ \left< 0, 1 \right> \f$.

Attributes:

• min_size (max_size)

  • Description: min_size (max_size) is the minimum (maximum) box size (in pixels).
  • Range of values: positive floating-point numbers
  • Type: float[]
  • Default value: []
  • Required: no

• aspect_ratio

  • Description: aspect_ratio is a variance of aspect ratios. Duplicate values are ignored.
  • Range of values: set of positive integer numbers
  • Type: float[]
  • Default value: []
  • Required: no

• flip

  • Description: flip is a flag that denotes that each aspect_ratio is duplicated and flipped. For example, flip equals 1 and aspect_ratio equals to [4.0,2.0] mean that aspect_ratio is equal to [4.0,2.0,0.25,0.5].
  • Range of values:
    • false or 0 - each aspect_ratio is flipped
    • true or 1 - each aspect_ratio is not flipped
  • Type: boolean
  • Default value: false
  • Required: no

• clip

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

• step

  • Description: step is a distance between box centers.
  • Range of values: floating-point non-negative number
  • Type: float
  • Default value: 0
  • Required: no

• offset

  • Description: offset is a shift of box respectively to top left corner.
  • Range of values: floating-point non-negative number
  • Type: float
  • Required: yes

• variance

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

• scale_all_sizes

  • Description: scale_all_sizes is a flag that denotes type of inference. For example, scale_all_sizes equals 0 means that max_size attribute is ignored.
  • Range of values:
    • false - max_size is ignored
    • true - max_size is used
  • Type: boolean
  • Default value: true
  • Required: no

• fixed_ratio

  • Description: fixed_ratio is an aspect ratio of a box.
  • Range of values: a list of positive floating-point numbers
  • Type: float[]
  • Default value: []
  • Required: no

• fixed_size

  • Description: fixed_size is an initial box size (in pixels).
  • Range of values: a list of positive floating-point numbers
  • Type: float[]
  • Default value: []
  • Required: no

• density

  • Description: density is the square root of the number of boxes of each type.
  • Range of values: a list of positive floating-point 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. Required.

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 operation attribute values.

Types

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

Example

<layer type="PriorBox" ...>
    <data aspect_ratio="2.0" clip="false" density="" fixed_ratio="" fixed_size="" flip="true" max_size="38.46" min_size="16.0" offset="0.5" step="16.0" variance="0.1,0.1,0.2,0.2"/>
    <input>
        <port id="0">
            <dim>2</dim>        <!-- values: [24, 42] -->
        </port>
        <port id="1">
            <dim>2</dim>        <!-- values: [384, 672] -->
        </port>
    </input>
    <output>
        <port id="2">
            <dim>2</dim>
            <dim>16128</dim>
        </port>
    </output>
</layer>