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
    • Default value: None
    • 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
    • Default value: None
    • 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
    • Default value: None
    • 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
    • Default value: None
    • 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
    • Default value: None
    • 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
    • Default value: None
    • Required: yes
  • ratio
    • Description: ratio is the ratios for anchor generation.
    • Range of values: a list of floating-point numbers
    • Type: float[]
    • Default value: None
    • Required: yes
  • scale
    • Description: scale is the scales for anchor generation.
    • Range of values: a list of floating-point numbers
    • Type: float[]
    • Default value: None
    • 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>