openvino/docs/ops/detection/ROIPooling_1.md

ROIPooling

Versioned name: ROIPooling-1

Category: Object detection

Short description: ROIPooling is a pooling layer used over feature maps of non-uniform input sizes and outputs a feature map of a fixed size.

Detailed description: deepsense.io reference

Attributes

• pooled_h

  • Description: pooled_h is the height of the ROI output feature map. For example, pooled_h equal to 6 means that the height of the output of ROIPooling is 6.
  • Range of values: a non-negative integer
  • Type: int
  • Default value: None
  • Required: yes

• pooled_w

  • Description: pooled_w is the width of the ROI output feature map. For example, pooled_w equal to 6 means that the width of the output of ROIPooling is 6.
  • Range of values: a non-negative integer
  • Type: int
  • Default value: None
  • Required: yes

• spatial_scale

  • Description: spatial_scale is the ratio of the input feature map over the input image size.
  • Range of values: a positive floating-point number
  • Type: float
  • Default value: None
  • Required: yes

• method

  • Description: method specifies a method to perform pooling. If the method is bilinear, the input box coordinates are normalized to the [0, 1] interval.
  • Range of values: max or bilinear
  • Type: string
  • Default value: max
  • Required: no

Inputs:

• 1: 4D input tensor of shape [1, C, H, W] with feature maps. Required.

• 2: 2D input tensor of shape [NUM_ROIS, 5] describing box consisting of 5 element tuples: [batch_id, x_1, y_1, x_2, y_2]. Required.

Outputs:

• 1: 4D output tensor of shape [NUM_ROIS, C, pooled_h, pooled_w] with feature maps.

Example

<layer ... type="ROIPooling" ... >
        <data pooled_h="6" pooled_w="6" spatial_scale="0.062500"/>
        <input> ... </input>
        <output> ... </output>
    </layer>