openvino.runtime.opset8.prior_box_clustered¶
- openvino.runtime.opset8.prior_box_clustered(output_size: openvino._pyopenvino.Node, image_size: Union[openvino._pyopenvino.Node, int, float, numpy.ndarray], attrs: dict, name: Optional[str] = None) openvino._pyopenvino.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.