Versioned name : ROIAlign-9

Category : Object detection

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

Detailed description : Reference.

ROIAlign performs the following for each Region of Interest (ROI) for each input feature map:

  1. Multiply box coordinates with spatial_scale to produce box coordinates relative to the input feature map size based on aligned_mode attribute.

  2. Divide the box into bins according to the sampling_ratio attribute.

  3. Apply bilinear interpolation with 4 points in each bin and apply maximum or average pooling based on mode attribute to produce output feature map element.


  • pooled_h

    • Description : pooled_h is the height of the ROI output feature map.

    • Range of values : a positive integer

    • Type : int

    • Required : yes

  • pooled_w

    • Description : pooled_w is the width of the ROI output feature map.

    • Range of values : a positive integer

    • Type : int

    • Required : yes

  • sampling_ratio

    • Description : sampling_ratio is the number of bins over height and width to use to calculate each output feature map element. If the value is equal to 0 then use adaptive number of elements over height and width: ceil(roi_height / pooled_h) and ceil(roi_width / pooled_w) respectively.

    • Range of values : a non-negative integer

    • Type : int

    • Required : yes

  • spatial_scale

    • Description : spatial_scale is a multiplicative spatial scale factor to translate ROI coordinates from their input spatial scale to the scale used when pooling.

    • Range of values : a positive floating-point number

    • Type : float

    • Required : yes

  • mode

    • Description : mode specifies a method to perform pooling to produce output feature map elements.

    • Range of values :

      • max - maximum pooling

      • avg - average pooling

    • Type : string

    • Required : yes

  • aligned_mode

    • Description : aligned_mode specifies how to transform the coordinate in original tensor to the resized tensor.

    • Range of values : name of the transformation mode in string format (here spatial_scale is resized_shape[x] / original_shape[x], resized_shape[x] is the shape of resized tensor in axis x, original_shape[x] is the shape of original tensor in axis x and x_original is a coordinate in axis x, for any axis x from the input axes):

      • asymmetric - the coordinate in the resized tensor axis x is calculated according to the formula x_original * spatial_scale

      • half_pixel_for_nn - the coordinate in the resized tensor axis x is x_original * spatial_scale - 0.5

      • half_pixel - the coordinate in the resized tensor axis x is calculated as ((x_original + 0.5) * spatial_scale) - 0.5

    • Type : string

    • Default value : asymmetric

    • 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, 4] describing box consisting of 4 element tuples: [x_1, y_1, x_2, y_2] in relative coordinates of type T. The box height and width are calculated the following way:

    • If aligned_mode equals asymmetric : 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.

    • else: roi_width = spatial_scale \* (x_2 - x_1), roi_height = spatial_scale \* (y_2 - y_1).

    • Required.

  • 3 : 1D input tensor of shape [NUM_ROIS] with batch indices of type IND_T. Required.

Outputs :

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


  • T : any supported floating-point type.

  • IND_T : any supported integer type.


<layer ... type="ROIAlign" ... >
    <data pooled_h="6" pooled_w="6" spatial_scale="16.0" sampling_ratio="2" mode="avg" aligned_mode="half_pixel"/>
        <port id="0">
        <port id="1">
        <port id="2">
        <port id="3" precision="FP32">