ngraph.opset2.prior_box_clustered

ngraph.opset2.prior_box_clustered(output_size: _pyngraph.Node, image_size: Union[_pyngraph.Node, int, float, numpy.ndarray], attrs: dict, name: Optional[str] = None) _pyngraph.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.