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Proposal

Versioned name : Proposal-4

Category : Object detection

Short description : Proposal operation filters bounding boxes and outputs only those with the highest prediction confidence.

Detailed description

Proposal has three inputs: a 4D tensor of shape [num_batches, 2\*K, H, W] with probabilities whether particular bounding box corresponds to background or foreground, a 4D tensor of shape [num_batches, 4\*K, H, W] with deltas for each of the bound box, and a tensor with input image size in the [image_height, image_width, scale_height_and_width] or [image_height, image_width, scale_height, scale_width] format. K is number of anchors and H, W are height and width of the feature map. Operation produces two tensors: the first mandatory tensor of shape [batch_size \* post_nms_topn, 5] with proposed boxes and the second optional tensor of shape [batch_size \* post_nms_topn] with probabilities (sometimes referred as scores).

Proposal layer does the following with the input tensor:

  1. Generates initial anchor boxes. Left top corner of all boxes is at (0, 0). Width and height of boxes are calculated from base_size with scale and ratio attributes.

  2. For each point in the first input tensor:

    • pins anchor boxes to the image according to the second input tensor that contains four deltas for each box: for x and y of center, for width and for height

    • finds out score in the first input tensor

  3. Filters out boxes with size less than min_size

  4. Sorts all proposals (box, score) by score from highest to lowest

  5. Takes top pre_nms_topn proposals

  6. Calculates intersections for boxes and filter out all boxes with \(intersection/union > nms\_thresh\)

  7. Takes top post_nms_topn proposals

  8. Returns the results:

    • Top proposals, if there is not enough proposals to fill the whole output tensor, the valid proposals will be terminated with a single -1.

    • Optionally returns probabilities for each proposal, which are not terminated by any special value.

Attributes :

  • base_size

    • Description : base_size is the size of the anchor to which scale and ratio attributes are applied.

    • Range of values : a positive integer number

    • Type : int

    • Required : yes

  • pre_nms_topn

    • Description : pre_nms_topn is the number of bounding boxes before the NMS operation. For example, pre_nms_topn equal to 15 means to take top 15 boxes with the highest scores.

    • Range of values : a positive integer number

    • Type : int

    • Required : yes

  • post_nms_topn

    • Description : post_nms_topn is the number of bounding boxes after the NMS operation. For example, post_nms_topn equal to 15 means to take after NMS top 15 boxes with the highest scores.

    • Range of values : a positive integer number

    • Type : int

    • Required : yes

  • nms_thresh

    • Description : nms_thresh is the minimum value of the proposal to be taken into consideration. For example, nms_thresh equal to 0.5 means that all boxes with prediction probability less than 0.5 are filtered out.

    • Range of values : a positive floating-point number

    • Type : float

    • Required : yes

  • feat_stride

    • Description : feat_stride is the step size to slide over boxes (in pixels). For example, feat_stride equal to 16 means that all boxes are analyzed with the slide 16.

    • Range of values : a positive integer

    • Type : int

    • Required : yes

  • min_size

    • Description : min_size is the minimum size of box to be taken into consideration. For example, min_size equal 35 means that all boxes with box size less than 35 are filtered out.

    • Range of values : a positive integer number

    • Type : int

    • Required : yes

  • ratio

    • Description : ratio is the ratios for anchor generation.

    • Range of values : a list of floating-point numbers

    • Type : float[]

    • Required : yes

  • scale

    • Description : scale is the scales for anchor generation.

    • Range of values : a list of floating-point numbers

    • Type : float[]

    • Required : yes

  • clip_before_nms

    • Description : clip_before_nms flag that specifies whether to perform clip bounding boxes before non-maximum suppression or not.

    • Range of values : true or false

    • Type : boolean

    • Default value : true

    • Required : no

  • clip_after_nms

    • Description : clip_after_nms is a flag that specifies whether to perform clip bounding boxes after non-maximum suppression or not.

    • Range of values : true or false

    • Type : boolean

    • Default value : false

    • Required : no

  • normalize

    • Description : normalize is a flag that specifies whether to perform normalization of output boxes to [0,1] interval or not.

    • Range of values : true or false

    • Type : boolean

    • Default value : false

    • Required : no

  • box_size_scale

    • Description : box_size_scale specifies the scale factor applied to box sizes before decoding.

    • Range of values : a positive floating-point number

    • Type : float

    • Default value : 1.0

    • Required : no

  • box_coordinate_scale

    • Description : box_coordinate_scale specifies the scale factor applied to box coordinates before decoding.

    • Range of values : a positive floating-point number

    • Type : float

    • Default value : 1.0

    • Required : no

  • framework

    • Description : framework specifies how the box coordinates are calculated.

    • Range of values :

      • “” (empty string) - calculate box coordinates like in Caffe*

      • tensorflow - calculate box coordinates like in the TensorFlow* Object Detection API models

    • Type : string

    • Default value : “” (empty string)

    • Required : no

Inputs :

  • 1 : 4D tensor of type T and shape [batch_size, 2\*K, H, W] with class prediction scores. Required.

  • 2 : 4D tensor of type T and shape [batch_size, 4\*K, H, W] with deltas for each bounding box. Required.

  • 3 : 1D tensor of type T with 3 or 4 elements: [image_height, image_width, scale_height_and_width] or [image_height, image_width, scale_height, scale_width]. Required.

Outputs

  • 1 : tensor of type T and shape [batch_size \* post_nms_topn, 5].

  • 2 : tensor of type T and shape [batch_size \* post_nms_topn] with probabilities.

Types

  • T : floating-point type.

Example

<layer ... type="Proposal" ... >
    <data base_size="16" feat_stride="8" min_size="16" nms_thresh="1.0" normalize="0" post_nms_topn="1000" pre_nms_topn="1000" ratio="1" scale="1,2"/>
    <input>
        <port id="0">
            <dim>7</dim>
            <dim>4</dim>
            <dim>28</dim>
            <dim>28</dim>
        </port>
        <port id="1">
            <dim>7</dim>
            <dim>8</dim>
            <dim>28</dim>
            <dim>28</dim>
        </port>
        <port id="2">
            <dim>3</dim>
        </port>
    </input>
    <output>
        <port id="3" precision="FP32">
            <dim>7000</dim>
            <dim>5</dim>
        </port>
        <port id="4" precision="FP32">
            <dim>7000</dim>
        </port>
    </output>
</layer>
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