DML_UPSAMPLE_2D_OPERATOR_DESC structure (directml.h)

Upsamples the input image, writing the result into the output tensor. The order of the dimensions should be NCHW (BatchSize, ChannelCount, Height, Width) or NCDHW (BatchSize, ChannelCount, Depth, Height, Width), but strides can be used if the data is stored in a different format. Unlike DML_RESAMPLE_OPERATOR_DESC, only the last 2 dimensions (height and width) can be upsampled.

If available, you should prefer DML_RESAMPLE_OPERATOR_DESC since it is a more flexible version of DML_UPSAMPLE_2D_OPERATOR_DESC.

Syntax

struct DML_UPSAMPLE_2D_OPERATOR_DESC {
  const DML_TENSOR_DESC  *InputTensor;
  const DML_TENSOR_DESC  *OutputTensor;
  DML_SIZE_2D            ScaleSize;
  DML_INTERPOLATION_MODE InterpolationMode;
};

Members

InputTensor

Type: const DML_TENSOR_DESC*

A tensor containing the input data. The expected dimensions of the InputTensor are { InputBatchCount, InputChannelCount, InputHeight, InputWidth } for 4D, and { InputBatchCount, InputChannelCount, InputDepth, InputHeight, InputWidth } for 5D.

OutputTensor

Type: const DML_TENSOR_DESC*

A tensor containing the input data. The expected dimensions of the OutputTensor are { InputBatchCount, InputChannelCount, InputHeight * HeightScale, InputWidth * WidthScale } for 4D, and { InputBatchCount, InputChannelCount, InputDepth, InputHeight * HeightScale, InputWidth * WidthScale } for 5D.

ScaleSize

Type: DML_SIZE_2D

The width and height scales of type UINT to apply when upsampling the input. 0 < ScaleSize.Height <= UINT_MAX / InputHeight and 0 < ScaleSize.Width <= UINT_MAX / InputWidth.

InterpolationMode

Type: DML_INTERPOLATION_MODE

This field determines the kind of interpolation used to choose output pixels.

• 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 in the height dimension, and the 2 nearest neighboring input elements in the width dimension, for a total of 4 elements. This is true even if the input/output DimensionCount is 5. That is, samples are only ever averaged along the width and height dimensions, and never along the batch, channel, or depth.

Availability

This operator was introduced in DML_FEATURE_LEVEL_1_0.

Tensor constraints

InputTensor and OutputTensor must have the same DataType and DimensionCount.

Tensor support

Requirements