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. Required.

Example

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