openvino/docs/ops/detection/Proposal_4.md

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 \f$intersection/union > nms_thresh\f$
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>