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>