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UID title description helpviewer_keywords old-location tech.root ms.assetid ms.date req.header req.include-header req.target-type req.target-min-winverclnt req.target-min-winversvr req.kmdf-ver req.umdf-ver req.ddi-compliance req.unicode-ansi req.idl req.max-support req.namespace req.assembly req.type-library req.lib req.dll req.irql targetos req.typenames req.redist ms.custom f1_keywords dev_langs topic_type api_type api_location api_name
NS:directml.DML_ROI_POOLING_OPERATOR_DESC
DML_ROI_POOLING_OPERATOR_DESC
Performs a MaxPool function across the input tensor (according to regions of interest, or ROIs).
DML_ROI_POOLING_OPERATOR_DESC
DML_ROI_POOLING_OPERATOR_DESC structure
direct3d12.dml_roi_pooling_operator_desc
directml/DML_ROI_POOLING_OPERATOR_DESC
direct3d12\dml_roi_pooling_operator_desc.htm
directml
7A165306-AC29-4C80-9586-2FB37E85147E
11/04/2020
directml.h
Windows
Windows
19H1
DML_ROI_POOLING_OPERATOR_DESC
directml/DML_ROI_POOLING_OPERATOR_DESC
c++
APIRef
kbSyntax
HeaderDef
DirectML.h
DML_ROI_POOLING_OPERATOR_DESC

-description

Performs a MaxPool function across the input tensor (according to regions of interest, or ROIs). For each output element, the coordinates of its corresponding ROI in the input are computed by the following equations.

Let Y be an index into the third dimension of InputTensor ({ BatchSize, ChannelCount, **height**, width }).

Let X be an index into the fourth dimension of InputTensor ({ BatchSize, ChannelCount, height, **width** }).

x1 = round(RoiX1 * SpatialScale)
x2 = round(RoiX2 * SpatialScale)
y1 = round(RoiY1 * SpatialScale)
y2 = round(RoiY2 * SpatialScale)

RegionHeight = y2 - y1 + 1
RegionWidth = x2 - x1 + 1

StartY = (OutputIndices.Y * RegionHeight) / PooledSize.Height + y1
StartX = (OutputIndices.X * RegionWidth) / PooledSize.Width + x1

EndY = ((OutputIndices.Y + 1) * RegionHeight + PooledSize.Height - 1) / PooledSize.Height + y1
EndX = ((OutputIndices.X + 1) * RegionWidth + PooledSize.Width - 1) / PooledSize.Width + x1

If the computed coordinates are out of bounds, then they are clamped to the input boundaries.

-struct-fields

-field InputTensor

Type: const DML_TENSOR_DESC*

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

-field ROITensor

Type: const DML_TENSOR_DESC*

A tensor containing the regions of interest (ROI) data. The expected dimensions of ROITensor are { 1, 1, NumROIs, 5 } and the data for each ROI is [BatchID, x1, y1, x2, y2]. x1, y1, x2, y2 are the inclusive coordinates of the corners of each ROI and x2 >= x1, y2 >= y1.

-field OutputTensor

Type: const DML_TENSOR_DESC*

A tensor containing the output data. The expected dimensions of OutputTensor are { NumROIs, InputChannelCount, PooledSize.Height, PooledSize.Width }.

-field SpatialScale

Type: FLOAT

Multiplicative spatial scale factor used to translate the ROI coordinates from their input scale to the scale used when pooling.

-field PooledSize

Type: DML_SIZE_2D

The ROI pool output size (height, width), which must match the last 2 dimensions of OutputTensor.

Availability

This operator was introduced in DML_FEATURE_LEVEL_1_0.

Tensor constraints

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

Tensor support

Tensor Kind Supported dimension counts Supported data types
InputTensor Input 4 FLOAT32, FLOAT16
ROITensor Input 4 FLOAT32, FLOAT16
OutputTensor Output 4 FLOAT32, FLOAT16