openvino/docs/ops/detection/PSROIPooling_1.md

PSROIPooling

Versioned name: PSROIPooling-1

Category: Object detection

Short description: PSROIPooling computes position-sensitive pooling on regions of interest specified by input.

Detailed description: Reference.

PSROIPooling operation takes two input blobs: with feature maps and with regions of interests (box coordinates). The latter is specified as five element tuples: [batch_id, x_1, y_1, x_2, y_2]. ROIs coordinates are specified in absolute values for the average mode and in normalized values (to [0,1] interval) for bilinear interpolation.

Attributes

• output_dim

  • Description: output_dim is a pooled output channel number.
  • Range of values: a positive integer
  • Type: int
  • Required: yes

• group_size

  • Description: group_size is the number of groups to encode position-sensitive score maps.
  • Range of values: a positive integer
  • Type: int
  • Default value: 1
  • Required: no

• spatial_scale

  • Description: spatial_scale is a multiplicative spatial scale factor to translate ROI coordinates from their input scale to the scale used when pooling.
  • Range of values: a positive floating-point number
  • Type: float
  • Required: yes

• mode

  • Description: mode specifies mode for pooling.
  • Range of values:
    • average - perform average pooling
    • bilinear - perform pooling with bilinear interpolation
  • Type: string
  • Default value: average
  • Required: no

• spatial_bins_x

  • Description: spatial_bins_x specifies numbers of bins to divide the input feature maps over width. Used for "bilinear" mode only.
  • Range of values: a positive integer
  • Type: int
  • Default value: 1
  • Required: no

• spatial_bins_y

  • Description: spatial_bins_y specifies numbers of bins to divide the input feature maps over height. Used for "bilinear" mode only.
  • Range of values: a positive integer
  • Type: int
  • Default value: 1
  • Required: no

Inputs:

• 1: 4D input tensor with shape [N, C, H, W] and type T with feature maps. Required.

• 2: 2D input tensor with shape [num_boxes, 5]. It contains a list of five element tuples that describe a region of interest: [batch_id, x_1, y_1, x_2, y_2]. Required. Batch indices must be in the range of [0, N-1].

Outputs:

• 1: 4D output tensor with areas copied and interpolated from the 1st input tensor by coordinates of boxes from the 2nd input.

Types

• T: any supported floating-point type.

Example

<layer ... type="PSROIPooling" ... >
    <data group_size="6" mode="bilinear" output_dim="360" spatial_bins_x="3" spatial_bins_y="3" spatial_scale="1"/>
    <input>
        <port id="0">
            <dim>1</dim>
            <dim>3240</dim>
            <dim>38</dim>
            <dim>38</dim>
        </port>
        <port id="1">
            <dim>100</dim>
            <dim>5</dim>
        </port>
    </input>
    <output>
        <port id="2">
            <dim>100</dim>
            <dim>360</dim>
            <dim>6</dim>
            <dim>6</dim>
        </port>
    </output>
</layer>