DML_ROI_ALIGN_OPERATOR_DESC structure (directml.h)

Performs an ROI align operation, as described in the Mask R-CNN paper. In summary, the operation extracts crops from the input image tensor and resizes them to a common output size specified by the last 2 dimensions of OutputTensor using the specified InterpolationMode.

Syntax

struct DML_ROI_ALIGN_OPERATOR_DESC {
  const DML_TENSOR_DESC  *InputTensor;
  const DML_TENSOR_DESC  *ROITensor;
  const DML_TENSOR_DESC  *BatchIndicesTensor;
  const DML_TENSOR_DESC  *OutputTensor;
  DML_REDUCE_FUNCTION    ReductionFunction;
  DML_INTERPOLATION_MODE InterpolationMode;
  FLOAT                  SpatialScaleX;
  FLOAT                  SpatialScaleY;
  FLOAT                  OutOfBoundsInputValue;
  UINT                   MinimumSamplesPerOutput;
  UINT                   MaximumSamplesPerOutput;
};

Members

InputTensor

Type: const DML_TENSOR_DESC*

A tensor containing the input data with dimensions { BatchCount, ChannelCount, InputHeight, InputWidth }.

ROITensor

Type: const DML_TENSOR_DESC*

A tensor containing the regions of interest (ROI) data. The allowed dimensions of ROITensor are { NumROIs, 4 }, { 1, NumROIs, 4 }, or { 1, 1, NumROIs, 4 }. For each ROI, the values will be the coordinates of its top-left and bottom-right corners in the order [x1, y1, x2, y2].

BatchIndicesTensor

Type: const DML_TENSOR_DESC*

A tensor containing the batch indices to extract the ROIs from. The allowed dimensions of BatchIndicesTensor are { NumROIs }, { 1, NumROIs }, { 1, 1, NumROIs }, or { 1, 1, 1, NumROIs }. Each value is the index of a batch from InputTensor. The behavior is undefined if the values are not in the range [0, BatchCount).

OutputTensor

Type: const DML_TENSOR_DESC*

A tensor containing the output data. The expected dimensions of OutputTensor are { NumROIs, ChannelCount, OutputHeight, OutputWidth }.

ReductionFunction

Type: DML_REDUCE_FUNCTION

The reduction function to use when reducing across all input samples that contribute to an output element (DML_REDUCE_FUNCTION_AVERAGE or DML_REDUCE_FUNCTION_MAX). The number of input samples to reduce across is bounded by MinimumSamplesPerOutput and MaximumSamplesPerOutput.

InterpolationMode

Type: DML_INTERPOLATION_MODE

The interpolation mode to use when resizing the regions.

• DML_INTERPOLATION_MODE_NEAREST_NEIGHBOR. Uses the Nearest Neighbor algorithm, which chooses the input element nearest to the corresponding pixel center for each output element.
• DML_INTERPOLATION_MODE_LINEAR. Uses the Bilinear algorithm, which computes the output element by doing the weighted average of the 2 nearest neighboring input elements per dimension. Since only 2 dimensions are resized, the weighted average is computed on a total of 4 input elements for each output element.

SpatialScaleX

Type: FLOAT

The X (or width) component of the scaling factor to multiply the ROITensor coordinates by in order to make them proportionate to InputHeight and InputWidth. For example, if ROITensor contains normalized coordinates (values in the range [0..1]), then SpatialScaleX would usually have the same value as InputWidth.

SpatialScaleY

Type: FLOAT

The Y (or height) component of the scaling factor to multiply the ROITensor coordinates by in order to make them proportionate to InputHeight and InputWidth. For example, if ROITensor contains normalized coordinates (values in the range [0..1]), SpatialScaleY would usually have the same value as InputHeight.

OutOfBoundsInputValue

Type: FLOAT

The value to read from InputTensor when the ROIs are outside the bounds of InputTensor. This can happen when the values obtained after scaling ROITensor by SpatialScaleX and SpatialScaleY are bigger than InputWidth and InputHeight.

MinimumSamplesPerOutput

Type: UINT

The minimum number of input samples to use for every output element. The operator will calculate the number of input samples by doing ScaledCropSize / OutputSize, and then clamp it to MinimumSamplesPerOutput and MaximumSamplesPerOutput.

MaximumSamplesPerOutput

Type: UINT

The maximum number of input samples to use for every output element. The operator will calculate the number of input samples by doing ScaledCropSize / OutputSize, and then clamp it to MinimumSamplesPerOutput and MaximumSamplesPerOutput.

Availability

This operator was introduced in DML_FEATURE_LEVEL_3_0.

Tensor constraints

InputTensor, OutputTensor, and ROITensor must have the same DataType.

Tensor support

DML_FEATURE_LEVEL_5_0 and above

DML_FEATURE_LEVEL_3_0 and above

Requirements