DML_PADDING1_OPERATOR_DESC structure (directml.h)
Inflates the input tensor with constant or mirrored values on the edges, and writes the result to the output.
Syntax
struct DML_PADDING1_OPERATOR_DESC {
const DML_TENSOR_DESC *InputTensor;
const DML_TENSOR_DESC *OutputTensor;
DML_PADDING_MODE PaddingMode;
DML_TENSOR_DATA_TYPE PaddingValueDataType;
DML_SCALAR_UNION PaddingValue;
UINT DimensionCount;
const UINT *StartPadding;
const UINT *EndPadding;
};
Members
InputTensor
Type: const DML_TENSOR_DESC*
A tensor containing the input data.
OutputTensor
Type: const DML_TENSOR_DESC*
A tensor containing the output data. For each dimension i, OutputTensor.Sizes[i] = InputTensor.Sizes[i] + StartPadding[i] + EndPadding[i].
PaddingMode
Type: DML_PADDING_MODE*
The padding mode to use when filling the padding regions.
- DML_PADDING_MODE_CONSTANT. Uses a single constant value defined by PaddingValue for all padding values (see Example 1).
- DML_PADDING_MODE_EDGE. For each dimension, use the edge values of that dimension for all padding values (see Example 2).
- DML_PADDING_MODE_REFLECTION. Mirror the values of the tensor as if we folded it right on the edges, which means that edges are not mirrored. Note that
StartPadding[i] >= InputTensor.Sizes[i], andEndPadding[i] >= InputTensor.Sizes[i]is valid, which means that we can mirror new padding regions periodically by folding them over previous padding regions (see Example 3). - DML_PADDING_MODE_SYMMETRIC. Similar to DML_PADDING_MODE_REFLECTION, but edges are also mirrored. Note that
StartPadding[i] > InputTensor.Sizes[i], andEndPadding[i] > InputTensor.Sizes[i]is valid, which means that we can mirror new padding regions periodically by folding them over previous padding regions (see Example 4). This mode was introduced in feature levelDML_FEATURE_LEVEL_3_0.
PaddingValueDataType
Type: DML_TENSOR_DATA_TYPE
The data type of the PaddingValue member, which must match OutputTensor.DataType.
PaddingValue
Type: DML_SCALAR_UNION
The padding value to use when PaddingMode == DML_PADDING_MODE_CONSTANT, with PaddingValueDataType determining how to interpret the field. This value is ignored for other padding modes.
DimensionCount
Type: UINT
The size of the arrays pointed to by StartPadding and EndPadding. This value must be the same value as the dimension count of InputTensor and OutputTensor.
StartPadding
Type: _Field_size_(DimensionCount) const UINT*
The sizes of the padding regions to add at the beginning of each dimension. For each dimension i, StartPadding[i] = OutputTensor.Sizes[i] - InputTensor.Sizes[i] - EndPadding[i].
EndPadding
Type: _Field_size_(DimensionCount) const UINT*
The sizes of the padding regions to add at the end of each dimension. For each dimension i, EndPadding[i] = OutputTensor.Sizes[i] - InputTensor.Sizes[i] - StartPadding[i].
Remarks
Examples
Example 1
PaddingMode: DML_PADDING_MODE_CONSTANT
PaddingValueDataType: FLOAT32
PaddingValue: 9
StartPadding: {0, 0, 1, 2}
EndPadding: {0, 0, 3, 4}
InputTensor: (Sizes:{1, 1, 4, 4}, DataType:FLOAT32)
[[[[1, 2, 3, 4],
[5, 6, 7, 8],
[1, 2, 3, 4],
[5, 6, 7, 8]]]]
OutputTensor: (Sizes:{1, 1, 8, 10}, DataType:FLOAT32)
[[[[9, 9, 9, 9, 9, 9, 9, 9, 9, 9]
[9, 9, 1, 2, 3, 4, 9, 9, 9, 9],
[9, 9, 5, 6, 7, 8, 9, 9, 9, 9],
[9, 9, 1, 2, 3, 4, 9, 9, 9, 9],
[9, 9, 5, 6, 7, 8, 9, 9, 9, 9],
[9, 9, 9, 9, 9, 9, 9, 9, 9, 9],
[9, 9, 9, 9, 9, 9, 9, 9, 9, 9],
[9, 9, 9, 9, 9, 9, 9, 9, 9, 9]]]]
Example 2
PaddingMode: DML_PADDING_MODE_EDGE
PaddingValueDataType: FLOAT32
StartPadding: {0, 0, 1, 2}
EndPadding: {0, 0, 3, 4}
InputTensor: (Sizes:{1, 1, 4, 4}, DataType:FLOAT32)
[[[[1, 2, 3, 4],
[5, 6, 7, 8],
[1, 2, 3, 4],
[5, 6, 7, 8]]]]
OutputTensor: (Sizes:{1, 1, 8, 10}, DataType:FLOAT32)
[[[[1, 1, 1, 2, 3, 4, 4, 4, 4, 4]
[1, 1, 1, 2, 3, 4, 4, 4, 4, 4],
[5, 5, 5, 6, 7, 8, 8, 8, 8, 8],
[1, 1, 1, 2, 3, 4, 4, 4, 4, 4],
[5, 5, 5, 6, 7, 8, 8, 8, 8, 8],
[5, 5, 5, 6, 7, 8, 8, 8, 8, 8],
[5, 5, 5, 6, 7, 8, 8, 8, 8, 8],
[5, 5, 5, 6, 7, 8, 8, 8, 8, 8]]]]
Example 3
PaddingMode: DML_PADDING_MODE_REFLECTION
PaddingValueDataType: FLOAT32
StartPadding: {0, 0, 1, 2}
EndPadding: {0, 0, 3, 4}
InputTensor: (Sizes:{1, 1, 4, 4}, DataType:FLOAT32)
[[[[1, 2, 3, 4],
[5, 6, 7, 8],
[1, 2, 3, 4],
[5, 6, 7, 8]]]]
OutputTensor: (Sizes:{1, 1, 8, 10}, DataType:FLOAT32)
[[[[7, 6, 5, 6, 7, 8, 7, 6, 5, 6]
[3, 2, 1, 2, 3, 4, 3, 2, 1, 2],
[7, 6, 5, 6, 7, 8, 7, 6, 5, 6],
[3, 2, 1, 2, 3, 4, 3, 2, 1, 2],
[7, 6, 5, 6, 7, 8, 7, 6, 5, 6],
[3, 2, 1, 2, 3, 4, 3, 2, 1, 2],
[7, 6, 5, 6, 7, 8, 7, 6, 5, 6],
[3, 2, 1, 2, 3, 4, 3, 2, 1, 2]]]]
Example 4 (starting from DML_FEATURE_LEVEL_3_0)
PaddingMode: DML_PADDING_MODE_SYMMETRIC
PaddingValueDataType: FLOAT32
StartPadding: {0, 0, 1, 2}
EndPadding: {0, 0, 3, 4}
InputTensor: (Sizes:{1, 1, 4, 4}, DataType:FLOAT32)
[[[[1, 2, 3, 4],
[5, 6, 7, 8],
[1, 2, 3, 4],
[5, 6, 7, 8]]]]
OutputTensor: (Sizes:{1, 1, 8, 10}, DataType:FLOAT32)
[[[[2, 1, 1, 2, 3, 4, 4, 3, 2, 1]
[2, 1, 1, 2, 3, 4, 4, 3, 2, 1],
[6, 5, 5, 6, 7, 8, 8, 7, 6, 5],
[2, 1, 1, 2, 3, 4, 4, 3, 2, 1],
[6, 5, 5, 6, 7, 8, 8, 7, 6, 5],
[6, 5, 5, 6, 7, 8, 8, 7, 6, 5],
[2, 1, 1, 2, 3, 4, 4, 3, 2, 1],
[6, 5, 5, 6, 7, 8, 8, 7, 6, 5]]]]
Availability
This operator was introduced in DML_FEATURE_LEVEL_5_0.
Tensor constraints
InputTensor and OutputTensor must have the same DataType and DimensionCount.
Tensor support
| 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 |
Requirements
| Requirement | Value |
|---|---|
| Header | directml.h |
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