openvino.runtime.opset4.prior_box_clustered

openvino.runtime.opset4.prior_box_clustered(output_size: Node, image_size: Node | int | float | ndarray, attrs: dict, name: str | None = None) → Node

Generate prior boxes of specified sizes normalized to the input image size.

Parameters:

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

• image_size – 1D tensor with two integer elements [image_height, image_width] that specifies shape of the image for which boxes are generated.

• attrs – The dictionary containing key, value pairs for attributes.

• name – Optional name for the output node.

Returns:

Node representing PriorBoxClustered operation.

Available attributes are:

• widths Specifies desired boxes widths in pixels.

  Range of values: floating point positive numbers. Default value: 1.0 Required: no

• heights Specifies desired boxes heights in pixels.

  Range of values: floating point positive numbers. Default value: 1.0 Required: no

• clip The flag that denotes if each value in the output tensor should be clipped

  within [0,1]. Range of values: {True, False} Default value: True Required: no

• step_widths The distance between box centers.

  Range of values: floating point positive number Default value: 0.0 Required: no

• step_heights The distance between box centers.

  Range of values: floating point positive number Default value: 0.0 Required: no

• offset The shift of box respectively to the top left corner.

  Range of values: floating point positive number Default value: None Required: yes

• variance Denotes a variance of adjusting bounding boxes.

  Range of values: floating point positive numbers Default value: [] Required: no

Example of attribute dictionary:

# just required ones
attrs = {
    'offset': 85,
}

attrs = {
    'offset': 85,
    'clip': False,
    'step_widths': [1.5, 2.0, 2.5]
}

Optional attributes which are absent from dictionary will be set with corresponding default.