# 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>