DeformablePSROIPooling#

Versioned name: DeformablePSROIPooling-1

Category: Object detection

Short description: DeformablePSROIPooling computes deformable position-sensitive pooling of regions of interest specified by input.

Detailed description: Reference.

DeformablePSROIPooling operation takes two or three input tensors: with position score maps, with regions of interests (ROI, box coordinates) and an optional tensor with transformation values (normalized offsets for ROI bins coordinates). If only two inputs are provided, position sensitive pooling with regular ROI bins position is calculated (non-deformable). If third input is provided, each bin position is transformed by adding corresponding offset to the bin left top corner coordinates. Third input values are usually calculated by regular position sensitive pooling layer, so non-deformable mode (DeformablePSROIPooling with two inputs). The ROI coordinates are specified as five element tuples: [batch_id, x_1, y_1, x_2, y_2] in absolute values.

This operation is compatible with Apache MXNet DeformablePSROIPooling cases where group_size is equal to pooled_size.

Attributes

  • output_dim

    • Description: output_dim is the number of the output channels, size of output C dimension.

    • Range of values: a positive integer

    • Type: int

    • Required: yes

  • spatial_scale

    • Description: spatial_scale is a multiplicative spatial scale factor to translate ROI coordinates from their input original size to the pooling input. Ratio of the input score map size to the original image size.

    • Range of values: a positive floating-point number

    • Type: float

    • Required: yes

  • group_size

    • Description: group_size is the number of horizontal bins per row to divide single ROI area. Total number of bins can be calculated as group_size*group_size. It defines pooled width and height, so output H_out and W_out dimensions (always equal). Square of the group_size is also the number to divide input channels C_in dimension and split it into C_in \\ group_size*group_size groups. Each group corresponds to the exactly one output channel and ROI’s bins are spread over input channel group members.

    • Range of values: a positive integer

    • Type: int

    • Default value: 1

    • Required: no

  • mode

    • Description: mode specifies mode for pooling.

    • Range of values:

      • bilinear_deformable - perform pooling with bilinear interpolation over single ROI bin. For each ROI bin average of his interpolated spatial_bins_x*spatial_bins_y sub-bins values is calculated.

    • Type: string

    • Default value: bilinear_deformable

    • Required: no

  • spatial_bins_x

    • Description: spatial_bins_x specifies number of horizontal sub-bins (bilinear interpolation samples) to divide ROI single bin.

    • Range of values: a positive integer

    • Type: int

    • Default value: 1

    • Required: no

  • spatial_bins_y

    • Description: spatial_bins_y specifies number of vertical sub-bins (bilinear interpolation samples) to divide ROI single bin.

    • Range of values: a positive integer

    • Type: int

    • Default value: 1

    • Required: no

  • trans_std

    • Description: trans_std is the value that all third input values (offests) are multiplied with to modulate the magnitude of the offsets.

    • Range of values: floating-point number

    • Type: float

    • Default value: 1

    • Required: no

  • part_size

    • Description: part_size is the size of H and W dimensions of the third input (offsets). Basically it is the height and width of the third input with transformation values.

    • Range of values: positive integer number

    • Type: int

    • Default value: 1

    • Required: no

Inputs:

  • 1: 4D input tensor of type T and shape [N_in, C_in, H_in, W_in] with position sensitive score maps. Required.

  • 2: 2D input tensor of type T and shape [NUM_ROIS, 5]. It contains a list of five element tuples describing a single ROI (region of interest): [batch_id, x_1, y_1, x_2, y_2]. Required. Batch indices must be in the range of [0, N_in-1].

  • 3: 4D input tensor of type T and shape [NUM_ROIS, 2*NUM_CLASSES, group_size, group_size] with transformation values. It contains normalized [0, 1] offsets for each ROI bin left top corner coordinates. Channel dimension is multiplied by 2 because of encoding two (x, y) coordinates. Optional.

Outputs:

  • 1: 4D output tensor of type T shape [NUM_ROIS, output_dim, group_size, group_size] with ROIs score maps.

Types:

  • T: Any floating-point type.

Example

  • Two inputs (without offsets)

<layer ... type="DeformablePSROIPooling" ... >
    <data spatial_scale="0.0625" output_dim="882" group_size="3" mode="bilinear_deformable" spatial_bins_x="4" spatial_bins_y="4" trans_std="0.0" part_size="3"/>
    <input>
        <port id="0">
            <dim>1</dim>
            <dim>7938</dim>
            <dim>63</dim>
            <dim>38</dim>
        </port>
        <port id="1">
            <dim>300</dim>
            <dim>5</dim>
        </port>
    </input>
    <output>
        <port id="2" precision="FP32">
            <dim>300</dim>
            <dim>882</dim>
            <dim>3</dim>
            <dim>3</dim>
        </port>
    </output>
</layer>

  • Three inputs (with offsets)

<layer ... type="DeformablePSROIPooling" ... >
    <data group_size="7" mode="bilinear_deformable" output_dim="8" part_size="7" spatial_bins_x="4" spatial_bins_y="4" spatial_scale="0.0625" trans_std="0.1"/>
    <input>
        <port id="0">
            <dim>1</dim>
            <dim>392</dim>
            <dim>38</dim>
            <dim>63</dim>
        </port>
        <port id="1">
            <dim>300</dim>
            <dim>5</dim>
        </port>
        <port id="2">
            <dim>300</dim>
            <dim>2</dim>
            <dim>7</dim>
            <dim>7</dim>
        </port>
    </input>
    <output>
        <port id="3" precision="FP32">
            <dim>300</dim>
            <dim>8</dim>
            <dim>7</dim>
            <dim>7</dim>
        </port>
    </output>
</layer>