DML_RESAMPLE1_OPERATOR_DESC structure (directml.h)

Resamples elements from the source to the destination tensor, using the scale factors to compute the destination tensor size. You can use a linear or nearest-neighbor interpolation mode. The operator supports interpolation across multiple dimensions, not just 2D. So you can keep the same spatial size, but interpolate across channels or across batches. The relationship between the input and output coordinates is the following.

OutputTensorX = (InputTensorX + InputPixelOffset) * Scale + OutputPixelOffset

Syntax

struct DML_RESAMPLE1_OPERATOR_DESC {
  const DML_TENSOR_DESC  *InputTensor;
  const DML_TENSOR_DESC  *OutputTensor;
  DML_INTERPOLATION_MODE InterpolationMode;
  UINT                   DimensionCount;
  const FLOAT            *Scales;
  const FLOAT            *InputPixelOffsets;
  const FLOAT            *OutputPixelOffsets;
};

Members

InputTensor

Type: const DML_TENSOR_DESC*

The tensor containing the input data.

OutputTensor

Type: const DML_TENSOR_DESC*

The tensor to write the output data to.

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 Linear Interpolation algorithm, which computes the output element by computing the weighted average of the 2 nearest neighboring input elements per dimension. Resampling is supported up to 4 dimensions (quadrilinear), where the weighted average is computed on a total of 16 input elements for each output element.

DimensionCount

Type: UINT

The number of values in the arrays that Scales, InputPixelOffsets, and OutputPixelOffsets point to. This value must match the dimension count of InputTensor and OutputTensor.

Scales

Type: _Field_size_(DimensionCount) const FLOAT*

The scales to apply when resampling the input, where scales > 1 scale up the image and scales < 1 scale down the image for that dimension. Note that the scales don't need to be exactly OutputSize / InputSize. If the input after scaling is larger than the output bound, then we crop it to the output size. On the other hand, if the input after scaling is smaller than the output bound, the output edges are clamped.

InputPixelOffsets

Type: _Field_size_(DimensionCount) const FLOAT*

The offsets to apply to the input pixels before resampling. When this value is 0, the top left corner of the pixel is used instead of its center, which usually won't give the expected result. To resample the image by using the center of the pixels and to get the same behavior as DML_RESAMPLE_OPERATOR_DESC, this value must be 0.5.

OutputPixelOffsets

Type: _Field_size_(DimensionCount) const FLOAT*

The offsets to apply to the output pixels after resampling. When this value is 0, the top left corner of the pixel is used instead of its center, which usually won't give the expected result. To resample the image by using the center of the pixels and to get the same behavior as DML_RESAMPLE_OPERATOR_DESC, this value must be -0.5.

Remarks

When the InputPixelOffsets are set to 0.5, and the OutputPixelOffsets are set to -0.5, this operator is equivalent to DML_RESAMPLE_OPERATOR_DESC.

Availability

This operator was introduced in DML_FEATURE_LEVEL_2_1.

Tensor constraints

InputTensor and OutputTensor must have the same DataType and DimensionCount.

Tensor support

DML_FEATURE_LEVEL_6_2 and above

DML_FEATURE_LEVEL_5_1 and above

DML_FEATURE_LEVEL_2_1 and above

Requirements