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.

Attributes:

• min_size (max_size)

  • Description: min_size (max_size) is the minimum (maximum) box size (in pixels). For example, min_size (max_size) equal 15 means that the minimum (maximum) box size is 15.
  • 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. For example, aspect_ratio equal "2.0,3.0" means that for the first box aspect_ratio is equal to 2.0 and for the second box is 3.0.
  • 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 - each aspect_ratio is flipped
    • true - 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 - clipping is not performed
    • true - 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. For example, step equal 85 means that the distance between neighborhood prior boxes centers is 85.
  • 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. For example, offset equal 85 means that the shift of neighborhood prior boxes centers is 85.
  • Range of values: floating point non-negative number
  • Type: float
  • Default value: None
  • 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 the PriorBox layer is inferred in MXNet-like manner. In particular, 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. For example, fixed_ratio equal to 2.000000 means that the aspect ratio for the first box aspect ratio is 2.
  • Range of values: a list of positive floating-point numbers
  • Type: float[]
  • Default value: None
  • Required: no

• fixed_size

  • Description: fixed_size is an initial box size (in pixels). For example, fixed_size equal to 15 means that the initial box size is 15.
  • Range of values: a list of positive floating-point numbers
  • Type: float[]
  • Default value: None
  • Required: no

• density

  • Description: density is the square root of the number of boxes of each type. For example, density equal to 2 means that the first box generates four boxes of the same size and with the same shifted centers.
  • Range of values: a list of positive floating-point numbers
  • Type: float[]
  • Default value: None
  • Required: no

Inputs:

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

• 2: image_size - 1D tensor with two integer 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] with box coordinates. The priors_per_point is the number of boxes generated per each grid element. The number depends on layer attribute values.

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 \f] \f[ H \equiv Height \quad Of \quad Image \f]
   • If step equals 0: \f[ center_x=(w+0.5) \f] \f[ center_y=(h+0.5) \f]
   • else: \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]
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]

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>