Versioned name: PriorBoxClustered-1
Category: Object detection
Short description: PriorBoxClustered operation generates prior boxes of specified sizes normalized to the input image size.
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 - clipping is not performed
- True - 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
- Default value: None
- Required: yes
- variance
- Description: variance denotes a variance of adjusting bounding boxes.
- Range of values: floating point positive numbers
- Type: float[]
- Default value: []
- Required: no
- img_h (img_w)
- Description: img_h (img_w) specifies height (width) of input image. These attributes are taken from the second input
image_sizeheight(width) unless provided explicitly as the value for this attributes. - Range of values: floating point positive number
- Type: float
- Default value: 0
- Required: no
- Description: img_h (img_w) specifies height (width) of input image. These attributes are taken from the second input
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. Optional.
Outputs:
- 1: 2D tensor of shape
[2, 4 * height * width * priors_per_point]with box coordinates. Thepriors_per_pointis the number of boxes generated per each grid element. The number depends on layer attribute values.
Detailed description
PriorBoxClustered computes coordinates of prior boxes by following:
- Calculates the center_x and center_y of prior box:
![\[ W \equiv Width \quad Of \quad Image \]](form_58.png)
![\[ H \equiv Height \quad Of \quad Image \]](form_59.png)
![\[ center_x=(w+offset)*step \]](form_136.png)
![\[ center_y=(h+offset)*step \]](form_137.png)
![\[ w \subset \left( 0, W \right ) \]](form_64.png)
![\[ h \subset \left( 0, H \right ) \]](form_65.png)
- For each
calculates the prior boxes coordinates: ![\[ xmin = \frac{center_x - \frac{width_s}{2}}{W} \]](form_72.png)
![\[ ymin = \frac{center_y - \frac{height_s}{2}}{H} \]](form_73.png)
![\[ xmax = \frac{center_x - \frac{width_s}{2}}{W} \]](form_74.png)
If clip is defined, the coordinates of prior boxes are recalculated with the formula:![\[ ymax = \frac{center_y - \frac{height_s}{2}}{H} \]](form_75.png)
Example
<layer type="PriorBoxClustered" ... >
<data clip="0" flip="1" 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>
</port>
<port id="1">
<dim>2</dim>
</port>
</input>
<output>
<port id="2">
<dim>2</dim>
<dim>6840</dim>
</port>
</output>
</layer>