NMSRotated

Versioned name: NMSRotated-13

Category: Sorting and maximization

Short description: NMSRotated performs non-maximum suppression of the rotated boxes with predicted scores.

Detailed description: NMSRotated performs regular non-maximum suppression, but the value of IoU is calculated for bounding boxes rotated by the corresponding angle.

The general algorithm is described below:

  1. Let B = [b_0,...,b_n] be the list of initial detection boxes, S = [s_0,...,s_N] be the list of corresponding scores.

  2. Let D = [] be an initial collection of resulting boxes.

  3. If B is empty then go to step 8.

  4. Take the box with the highest score. Suppose that it is the box b with the score s.

  5. Delete b from B.

  6. If the score s is greater or equal than score_threshold then add b to D else go to step 8.

  7. For each input box b_i from B and the corresponding score s_i, set s_i = s_i * func(rotated_iou(b_i, b)) and go to step 3.

  8. Return D, a collection of the corresponding scores S, and the number of elements in D.

Here func(rotated_iou(b_i, b)) = 1 if rotated_iou(b_i, b) <= iou_threshold else 0.

Having two bounding boxes B1 and B2 the following steps are performed to calculate rotated_iou(B1, B2):

  1. Calculate rotated vertices, (x, y) coordinates of the 4 corners of each box transformed by the corresponding angle in radians according to the direction specified by the clockwise attribute.

  2. Find all intersection points between edges of B1 and B2. Add them to the intersection_points.

  3. Find all corners of B1 within area of B2, and all corners of B2 within area of B1. Add them to the intersection_points.

  4. Calculate intersection_area of the polygon described by intersection_points (see Sholeace formula).

  5. Calculate union_area (the common area of B1 and B2), union_area = B1_area + B2_area.

  6. Return intersection over union rotated_iou = intersection_area / (union_area - intersection_area).

This algorithm is applied independently to each class of each batch element. The total number of output boxes for each class must not exceed max_output_boxes_per_class.

Attributes:

  • sort_result_descending

    • Description: sort_result_descending is a flag that specifies whether it is necessary to sort selected boxes across batches or not.

    • Range of values: true of false

      • true - sort selected boxes across batches.

      • false - do not sort selected boxes across batches (boxes are sorted per class).

    • Type: boolean

    • Default value: true

    • Required: no

  • output_type

    • Description: the output tensor type

    • Range of values: “i64” or “i32”

    • Type: string

    • Default value: “i64”

    • Required: no

  • clockwise

    • Description: the direction of angle

    • Range of values: true of false

      • true - positive value of the angle is clockwise.

      • false - positive value of the angle is counterclockwise.

    • Type: boolean

    • Default value: true

    • Required: no

Inputs:

  • 1: boxes - tensor of type T and shape [num_batches, num_boxes, 5]. The box data is supplied as [x_center, y_center, width, height, angle], the coordinates of the center, width (x), height (y) and the angle in radians. Required.

  • 2: scores - tensor of type T and shape [num_batches, num_classes, num_boxes] with box scores. Required.

  • 3: max_output_boxes_per_class - scalar or 1D tensor with 1 element of type T_MAX_BOXES specifying the maximum number of boxes to be selected per class. Required.

  • 4: iou_threshold - scalar or 1D tensor with 1 element of type T_THRESHOLDS specifying intersection over union threshold. Required.

  • 5: score_threshold - scalar or 1D tensor with 1 element of type T_THRESHOLDS specifying minimum score to consider box for the processing. Required.

Outputs:

  • 1: selected_indices - tensor of type output_type and shape [number of selected boxes, 3] containing information about selected boxes as triplets [batch_index, class_index, box_index].

  • 2: selected_scores - tensor of type T_THRESHOLDS and shape [number of selected boxes, 3] containing information about scores for each selected box as triplets [batch_index, class_index, box_score].

  • 3: valid_outputs - 1D tensor with 1 element of type output_type representing the total number of selected boxes.

Plugins that do not support dynamic output tensors produce selected_indices and selected_scores tensors of shape [min(num_boxes, max_output_boxes_per_class) * num_batches * num_classes, 3] which is an upper bound for the number of possible selected boxes. Output tensor elements following the really selected boxes are filled with value -1.

Types

  • T: floating-point type.

  • T_MAX_BOXES: integer type.

  • T_THRESHOLDS: floating-point type.

Example

<layer ... type="NMSRotated" ... >
    <data sort_result_descending="true" output_type="i64" clockwise="true"/>
    <input>
        <port id="0">
            <dim>3</dim>
            <dim>100</dim>
            <dim>5</dim>
        </port>
        <port id="1">
            <dim>3</dim>
            <dim>5</dim>
            <dim>100</dim>
        </port>
        <port id="2"/> <!-- 10 -->
        <port id="3"/>
        <port id="4"/>
    </input>
    <output>
        <port id="6" precision="I64">
            <dim>150</dim> <!-- min(100, 10) * 3 * 5 -->
            <dim>3</dim>
        </port>
        <port id="7" precision="FP32">
            <dim>150</dim> <!-- min(100, 10) * 3 * 5 -->
            <dim>3</dim>
        </port>
        <port id="8" precision="I64">
            <dim>1</dim>
        </port>
    </output>
</layer>