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:

ROIPooling performs the following operations for each Region of Interest (ROI) over the input feature maps:

1. Produce box coordinates relative to the input feature map size, based on method attribute.

2. Calculate box height and width.

3. Divide the box into bins according to the pooled size attributes, [pooled_h, pooled_w].

4. Apply maximum or bilinear interpolation pooling, for each bin, based on method attribute to produce output feature map element.

The box height and width have different representation based on method attribute:

• max: Expressed in relative coordinates. The box height and width are calculated the following way: roi_width = max(spatial_scale * (x_2 - x_1), 1.0), roi_height = max(spatial_scale * (y_2 - y_1), 1.0), so the malformed boxes are expressed as a box of size 1 x 1.

• bilinear: Expressed in absolute coordinates and normalized to the [0, 1] interval. The box height and width are calculated the following way: roi_width = (W - 1)  * (x_2 - x_1), roi_height = (H - 1) * (y_2 - y_1).

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

  • 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

  • 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

  • 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 [N, C, H, W] with feature maps of type T. Required.

• 2: 2D input tensor of shape [NUM_ROIS, 5] describing region of interest box consisting of 5 element tuples of type T: [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 of shape [NUM_ROIS, C, pooled_h, pooled_w] with feature maps of type T.

Types

• T: any supported floating-point type.

Example

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