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
Then calculations of PriorBoxClustered can be written as
For each \(s = \overline{0, W - 1}\) calculates the prior boxes coordinates:
If clip is defined, the coordinates of prior boxes are recalculated with the formula: \(coordinate = \min(\max(coordinate,0), 1)\)
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. Thepriors_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>