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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_SLICE_OPERATOR_DESC
DML_SLICE_OPERATOR_DESC
Extracts a single subregion (a "slice") of an input tensor. (DML_SLICE_OPERATOR_DESC)
DML_SLICE_OPERATOR_DESC
DML_SLICE_OPERATOR_DESC structure
direct3d12.dml_slice_operator_desc
directml/DML_SLICE_OPERATOR_DESC
direct3d12\dml_slice_operator_desc.htm
directml
6CB9CF44-B980-42B3-A967-4095AAA088B3
11/04/2020
directml.h
Windows
Windows
19H1
DML_SLICE_OPERATOR_DESC
directml/DML_SLICE_OPERATOR_DESC
c++
APIRef
kbSyntax
HeaderDef
DirectML.h
DML_SLICE_OPERATOR_DESC

-description

Extracts a single subregion (a "slice") of an input tensor.

The elements copied in the slice are determined using three values for each dimension.

  • The offset marks the first element to copy in a dimension.
  • The size marks the number of elements to copy in a dimension.
  • The stride indicates the element increment or step in a dimension.

The provided Offsets, Sizes, and Strides must only copy elements that are within the bounds of the input tensor (out-of-bounds reads are not permitted). The Sizes of the slice must exactly match the output tensor sizes. In general, the elements copied are calculated as follows.

OutputTensor[OutputCoordinates] = InputTensor[Offsets + Strides * OutputCoordinates]

-struct-fields

-field InputTensor

Type: const DML_TENSOR_DESC*

The tensor to extract slices from.

-field OutputTensor

Type: const DML_TENSOR_DESC*

The tensor to write the sliced data results to.

-field DimensionCount

Type: UINT

The number of dimensions. This field determines the size of the Offsets, Sizes, and Strides arrays. This value must match the DimensionCount of the input and output tensors. This value must be between 1 and 8, inclusively, starting from DML_FEATURE_LEVEL_3_0; earlier feature levels require a value of either 4 or 5.

-field Offsets

Type: _Field_size_(DimensionCount) const UINT*

An array containing the slice's start along each dimension of the input tensor, in elements.

-field Sizes

Type: _Field_size_(DimensionCount) const UINT*

An array containing the slice's size along each dimension, in elements. The values in this array must match the sizes specified in the output tensor.

-field Strides

Type: _Field_size_(DimensionCount) const UINT*

An array containing the slice's stride along each dimension of the input tensor, in elements. A stride larger than 1 indicates that elements of the input tensor may be skipped (for example, a stride of 2 will select every second element along the dimension).

Examples

The following examples use the same input tensor:

InputTensor: (Sizes:{1, 1, 4, 4}, DataType:FLOAT32)
[[[[ 1,  2,  3,  4],
   [ 5,  6,  7,  8],
   [ 9, 10, 11, 12],
   [13, 14, 15, 16]]]]

Example 1. Contiguous slice

Offsets = {0, 0, 1, 2}
Sizes   = {1, 1, 3, 2}
Strides = {1, 1, 1, 1}

OutputTensor: (Sizes:{1, 1, 3, 2}, DataType:FLOAT32)
[[[[ 7,  8],
   [11, 12],
   [15, 16]]]]

Example 2. Strided slice

Offsets = {0, 0, 1, 0}
Sizes   = {1, 1, 2, 2}
Strides = {1, 1, 2, 3}

OutputTensor: (Sizes:{1, 1, 2, 2}, DataType:FLOAT32)
[[[[ 5,  8],
   [13, 16]]]]

-remarks

A newer version of this operator, DML_SLICE1_OPERATOR_DESC, was introduced in DML_FEATURE_LEVEL_2_1.

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

DML_FEATURE_LEVEL_4_1 and above

Tensor Kind Supported dimension counts Supported data types
InputTensor Input 1 to 8 FLOAT64, FLOAT32, FLOAT16, INT64, INT32, INT16, INT8, UINT64, UINT32, UINT16, UINT8
OutputTensor Output 1 to 8 FLOAT64, FLOAT32, FLOAT16, INT64, INT32, INT16, INT8, UINT64, UINT32, UINT16, UINT8

DML_FEATURE_LEVEL_3_0 and above

Tensor Kind Supported dimension counts Supported data types
InputTensor Input 1 to 8 FLOAT32, FLOAT16, INT32, INT16, INT8, UINT32, UINT16, UINT8
OutputTensor Output 1 to 8 FLOAT32, FLOAT16, INT32, INT16, INT8, UINT32, UINT16, UINT8

DML_FEATURE_LEVEL_2_1 and above

Tensor Kind Supported dimension counts Supported data types
InputTensor Input 4 to 5 FLOAT32, FLOAT16, INT32, INT16, INT8, UINT32, UINT16, UINT8
OutputTensor Output 4 to 5 FLOAT32, FLOAT16, INT32, INT16, INT8, UINT32, UINT16, UINT8

DML_FEATURE_LEVEL_1_0 and above

Tensor Kind Supported dimension counts Supported data types
InputTensor Input 4 to 5 FLOAT32, FLOAT16, INT32, INT16, UINT32, UINT16
OutputTensor Output 4 to 5 FLOAT32, FLOAT16, INT32, INT16, UINT32, UINT16