DML_SLICE1_OPERATOR_DESC structure (directml.h)
Extracts a single subregion (a "slice") of an input tensor.
The input window describes the bounds of the input tensor to consider in the slice. The window is defined using three values for each dimension.
- The offset marks the beginning of the window in a dimension.
- The size marks the extent of the window in a dimension. The end of the window in a dimension is
offset + size - 1. - The stride indicates how to traverse the elements in a dimension.
- The magnitude of the stride indicates how many elements to advance when copying within the window.
- If a stride is positive, elements are copied starting at the beginning of the window in the dimension.
- If a stride is negative, elements are copied starting at the end of the window in the dimension.
The following pseudo-code illustrates how elements are copied using the input window. Note how elements in a dimension are copied from start to end with a positive stride, and copied from end to start with a negative stride.
CopyStart = InputWindowOffsets
for dimension i in [0, DimensionCount - 1]:
if InputWindowStrides[i] < 0:
CopyStart[i] += InputWindowSizes[i] - 1 // start at the end of the window in this dimension
OutputTensor[OutputCoordinates] = InputTensor[CopyStart + InputWindowStrides * OutputCoordinates]
The input window mustn't be empty in any dimension, and the window mustn't extend beyond the dimensions of the input tensor (out-of-bounds reads aren't permitted). The window's size and strides effectively limit the number of elements that may be copied from each dimension i of the input tensor.
MaxCopiedElements[i] = 1 + (InputWindowSize[i] - 1) / InputWindowStrides[i]
The output tensor isn't required to copy all reachable elements within the window. The slice is valid so long as 1 <= OutputSizes[i] <= MaxCopiedElements[i].
Syntax
struct DML_SLICE1_OPERATOR_DESC {
const DML_TENSOR_DESC *InputTensor;
const DML_TENSOR_DESC *OutputTensor;
UINT DimensionCount;
const UINT *InputWindowOffsets;
const UINT *InputWindowSizes;
const INT *InputWindowStrides;
};
Members
InputTensor
Type: const DML_TENSOR_DESC*
The tensor to extract slices from.
OutputTensor
Type: const DML_TENSOR_DESC*
The tensor to write the sliced data results to.
DimensionCount
Type: UINT
The number of dimensions. This field determines the size of the InputWindowOffsets, InputWindowSizes, and InputWindowStrides 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.
InputWindowOffsets
Type: _Field_size_(DimensionCount) const UINT*
An array containing the beginning (in elements) of the input window in each dimension. Values in the array must satisfy the constraint InputWindowOffsets[i] + InputWindowSizes[i] <= InputTensor.Sizes[i]
InputWindowSizes
Type: _Field_size_(DimensionCount) const UINT*
An array containing the extent (in elements) of the input window in each dimension. Values in the array must satisfy the constraint InputWindowOffsets[i] + InputWindowSizes[i] <= InputTensor.Sizes[i]
InputWindowStrides
Type: _Field_size_(DimensionCount) const UINT*
An array containing the slice's stride along each dimension of the input tensor, in elements. The magnitude of the stride indicates how many elements to advance when copying within the input window. The sign of the stride determines if elements are copied starting at the beginning of the window (positive stride) or end of the window (negative stride). Strides may not be 0.
Examples
The following examples use this 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. Strided slice with positive strides
InputWindowOffsets = {0, 0, 0, 1}
InputWindowSizes = {1, 1, 4, 3}
InputWindowStrides = {1, 1, 2, 2}
OutputTensor: (Sizes:{1, 1, 2, 2}, DataType:FLOAT32)
[[[[ 2, 4],
[10, 12]]]]
The copied elements are calculated as follows.
Output[0,0,0,0] = {0,0,0,1} + {1,1,2,2} * {0,0,0,0} = Input[{0,0,0,1}] = 2
Output[0,0,0,1] = {0,0,0,1} + {1,1,2,2} * {0,0,0,1} = Input[{0,0,0,3}] = 4
Output[0,0,1,0] = {0,0,0,1} + {1,1,2,2} * {0,0,1,0} = Input[{0,0,2,1}] = 10
Output[0,0,1,1] = {0,0,0,1} + {1,1,2,2} * {0,0,1,1} = Input[{0,0,2,3}] = 12
Example 2. Strided slice with negative strides
InputWindowOffsets = {0, 0, 0, 1}
InputWindowSizes = {1, 1, 4, 3}
InputWindowStrides = {1, 1, -2, 2}
OutputTensor: (Sizes:{1, 1, 2, 2}, DataType:FLOAT32)
[[[[14, 16],
[ 6, 8]]]]
Recall that dimensions with negative window strides start copying at the end of the input window for that dimension; this is done by adding InputWindowSize[i] - 1 to the window offset. Dimensions with a positive stride simply start at InputWindowOffset[i].
- Axis 0 (
+1window stride) starts copying atInputWindowOffsets[0] = 0. - Axis 1 (
+1window stride) starts copying atInputWindowOffsets[1] = 0. - Axis 2 (
-2window stride) starts copying atInputWindowOffsets[2] + InputWindowSizes[0] - 1 = 0 + 4 - 1 = 3. - Axis 3 (
+2window stride) starts copying atInputWindowOffsets[3] = 1.
The copied elements are calculated as follows.
Output[0,0,0,0] = {0,0,3,1} + {1,1,-2,2} * {0,0,0,0} = Input[{0,0,3,1}] = 14
Output[0,0,0,1] = {0,0,3,1} + {1,1,-2,2} * {0,0,0,1} = Input[{0,0,3,3}] = 16
Output[0,0,1,0] = {0,0,3,1} + {1,1,-2,2} * {0,0,1,0} = Input[{0,0,1,1}] = 6
Output[0,0,1,1] = {0,0,3,1} + {1,1,-2,2} * {0,0,1,1} = Input[{0,0,1,3}] = 8
Remarks
This operator is similar to DML_SLICE_OPERATOR_DESC, but it differs in two important ways.
- Slice strides may be negative, which allows reversing values along dimensions.
- The input window sizes are not necessarily the same as the output tensor sizes.
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_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 |
Requirements
| Requirement | Value |
|---|---|
| Minimum supported client | Windows 10 Build 20348 |
| Minimum supported server | Windows 10 Build 20348 |
| Header | directml.h |
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