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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 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. 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:

    \[W \equiv Width \quad Of \quad Image\]
    \[H \equiv Height \quad Of \quad Image\]

    • If step equals 0:

      \[center_x=(w+0.5)\]
      \[center_y=(h+0.5)\]

    • else:

      \[center_x=(w+offset)*step\]
      \[center_y=(h+offset)*step\]
      \[w \subset \left( 0, W \right )\]
      \[h \subset \left( 0, H \right )\]

  2. Then, for each \(s \subset \left( 0, min_sizes \right )\) calculates coordinates of prior boxes:

    \[xmin = \frac{\frac{center_x - s}{2}}{W}\]
    \[ymin = \frac{\frac{center_y - s}{2}}{H}\]
    \[xmax = \frac{\frac{center_x + s}{2}}{W}\]
    \[ymin = \frac{\frac{center_y + s}{2}}{H}\]

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>
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