module onnxrt.ops_cpu.op_non_max_suppression#

Inheritance diagram of mlprodict.onnxrt.ops_cpu.op_non_max_suppression

Short summary#

module mlprodict.onnxrt.ops_cpu.op_non_max_suppression

Runtime operator.

source on GitHub

Classes#

class

truncated documentation

NonMaxSuppression

NonMaxSuppression ================= Filter out boxes that have high intersection-over-union (IOU) overlap with previously …

Properties#

property

truncated documentation

args_default

Returns the list of arguments as well as the list of parameters with the default values (close to the signature). …

args_default_modified

Returns the list of modified parameters.

args_mandatory

Returns the list of optional arguments.

args_optional

Returns the list of optional arguments.

atts_value

Returns all parameters in a dictionary.

Methods#

method

truncated documentation

__init__

_infer_shapes

_run

Documentation#

Runtime operator.

source on GitHub

class mlprodict.onnxrt.ops_cpu.op_non_max_suppression.NonMaxSuppression(onnx_node, desc=None, **options)#

Bases: OpRun

Filter out boxes that have high intersection-over-union (IOU) overlap with previously selected boxes. Bounding boxes with score less than score_threshold are removed. Bounding box format is indicated by attribute center_point_box. Note that this algorithm is agnostic to where the origin is in the coordinate system and more generally is invariant to orthogonal transformations and translations of the coordinate system; thus translating or reflections of the coordinate system result in the same boxes being selected by the algorithm. The selected_indices output is a set of integers indexing into the input collection of bounding boxes representing the selected boxes. The bounding box coordinates corresponding to the selected indices can then be obtained using the Gather or GatherND operation.

Attributes

  • center_point_box: Integer indicate the format of the box data. The default is 0. 0 - the box data is supplied as [y1, x1, y2, x2] where (y1, x1) and (y2, x2) are the coordinates of any diagonal pair of box corners and the coordinates can be provided as normalized (i.e., lying in the interval [0, 1]) or absolute. Mostly used for TF models. 1 - the box data is supplied as [x_center, y_center, width, height]. Mostly used for Pytorch models. Default value is namecenterpointboxi0typeINT (INT)

Inputs

Between 2 and 5 inputs.

  • boxes (heterogeneous)tensor(float): An input tensor with shape [num_batches, spatial_dimension, 4]. The single box data format is indicated by center_point_box.

  • scores (heterogeneous)tensor(float): An input tensor with shape [num_batches, num_classes, spatial_dimension]

  • max_output_boxes_per_class (optional, heterogeneous)tensor(int64): Integer representing the maximum number of boxes to be selected per batch per class. It is a scalar. Default to 0, which means no output.

  • iou_threshold (optional, heterogeneous)tensor(float): Float representing the threshold for deciding whether boxes overlap too much with respect to IOU. It is scalar. Value range [0, 1]. Default to 0.

  • score_threshold (optional, heterogeneous)tensor(float): Float representing the threshold for deciding when to remove boxes based on score. It is a scalar.

Outputs

  • selected_indices (heterogeneous)tensor(int64): selected indices from the boxes tensor. [num_selected_indices, 3], the selected index format is [batch_index, class_index, box_index].

Version

Onnx name: NonMaxSuppression

This version of the operator has been available since version 11.

Runtime implementation: NonMaxSuppression

__init__(onnx_node, desc=None, **options)#
_infer_shapes(boxes, scores, max_output_boxes_per_class=None, iou_threshold=None, score_threshold=None)#

Should be overwritten.

source on GitHub

_run(boxes, scores, max_output_boxes_per_class=None, iou_threshold=None, score_threshold=None, attributes=None, verbose=0, fLOG=None)#

Should be overwritten.

source on GitHub