Visible to Intel only — GUID: GUID-857BC893-A119-42E0-A23F-6AEFA5DD4393
Convolution
Inner Product
Matrix Multiplication
RNN
Batch Normalization
Binary
Concat
Eltwise
Layer Normalization
Local Response Normalization (LRN)
Pooling
PReLU
Resampling
General
Execution Arguments
Implementation Details
Implementation Limitations
Performance Tips
Example
Shuffle
Softmax
Sum
Reorder
Reduction
Abs
AbsBackward
Add
AvgPool
AvgPoolBackward
BatchNormForwardTraining
BatchNormInference
BatchNormTrainingBackward
BiasAdd
BiasAddBackward
Clamp
ClampBackward
Concat
Convolution
ConvolutionBackwardData
ConvolutionBackwardWeights
ConvTranspose
ConvTransposeBackwardData
ConvTransposeBackwardWeights
Dequantize
Divide
DynamicDequantize
DynamicQuantize
Elu
EluBackward
End
Exp
GELU
GELUBackward
HardSwish
HardSwishBackward
Interpolate
InterpolateBackward
LayerNorm
LayerNormBackward
LeakyReLU
Log
LogSoftmax
LogSoftmaxBackward
MatMul
Maximum
MaxPool
MaxPoolBackward
Minimum
Mish
MishBackward
Multiply
PReLU
PReLUBackward
Quantize
Reciprocal
ReduceL1
ReduceL2
ReduceMax
ReduceMean
ReduceMin
ReduceProd
ReduceSum
ReLU
ReLUBackward
Reorder
Round
Sigmoid
SigmoidBackward
Softmax
SoftmaxBackward
SoftPlus
SoftPlusBackward
Sqrt
SqrtBackward
Square
SquaredDifference
StaticReshape
StaticTranspose
Subtract
Tanh
TanhBackward
TypeCast
Wildcard
enum dnnl_alg_kind_t
enum dnnl_normalization_flags_t
enum dnnl_primitive_kind_t
enum dnnl_prop_kind_t
enum dnnl_query_t
enum dnnl::normalization_flags
enum dnnl::query
struct dnnl_exec_arg_t
struct dnnl_primitive
struct dnnl_primitive_desc
struct dnnl::primitive
struct dnnl::primitive_desc
struct dnnl::primitive_desc_base
enum dnnl_rnn_direction_t
enum dnnl_rnn_flags_t
enum dnnl::rnn_direction
enum dnnl::rnn_flags
struct dnnl::augru_backward
struct dnnl::augru_forward
struct dnnl::gru_backward
struct dnnl::gru_forward
struct dnnl::lbr_augru_backward
struct dnnl::lbr_augru_forward
struct dnnl::lbr_gru_backward
struct dnnl::lbr_gru_forward
struct dnnl::lstm_backward
struct dnnl::lstm_forward
struct dnnl::rnn_primitive_desc_base
struct dnnl::vanilla_rnn_backward
struct dnnl::vanilla_rnn_forward
Visible to Intel only — GUID: GUID-857BC893-A119-42E0-A23F-6AEFA5DD4393
Resampling
General
The resampling primitive computes forward or backward resampling operation on 1D, 2D, or 3D spatial data. Resampling performs spatial scaling of original tensor using one of the supported interpolation algorithms:
Nearest Neighbor
Linear (or Bilinear for 2D spatial tensor, Trilinear for 3D spatial tensor).
Resampling operation is defined by the source tensor and scaling factors in each spatial dimension. Upsampling and downsampling are the alternative terms for resampling that are used when all scaling factors are greater (upsampling) or less (downsampling) than one.
The resampling operation is defined by the following formulas. We show formulas only for 2D spatial data which are straightforward to generalize to cases of higher and lower dimensions. Variable names follow the standard Naming Conventions.
Let 
and be 
and 
tensors respectively. Let 
and 
define scaling factors in each spatial dimension.
The following formulas show how oneDNN computes resampling for nearest neighbor and bilinear interpolation methods. To further simplify the formulas, we assume the following:
Same assumptions apply for 
. Definitions of and 
are provided below with a correspondent algorithm.
Forward
Nearest Neighbor Resampling
where
,
.
Bilinear Resampling
where
,
,
,
,
,
.
Difference Between Forward Training and Forward Inference
There is no difference between the dnnl_forward_training and dnnl_forward_inference propagation kinds.
Backward
The backward propagation computes based on .
Execution Arguments
When executed, the inputs and outputs should be mapped to an execution argument index as specified by the following table.
Primitive input/output |
Execution argument index |
|---|---|
|
DNNL_ARG_SRC |
|
DNNL_ARG_DST |
|
DNNL_ARG_DIFF_SRC |
|
DNNL_ARG_DIFF_DST |
|
DNNL_ARG_ATTR_MULTIPLE_POST_OP(binary_post_op_position) | DNNL_ARG_SRC_1 |
Implementation Details
General Notes
Resampling implementation supports data with arbitrary data tag (nchw, nhwc, nChw16c, etc.) but memory tags for src and dst are expected to be the same. Resampling primitive supports dst and diff_src memory tag dnnl::memory::format_tag::any and can define destination format based on source format.
Resampling primitive descriptor can be created by specifying the source and destination memory descriptors, only the source descriptor and floating point factors, or the source and destination memory descriptors and factors. In case when user does not provide the destination descriptor, the destination dimensions are deduced using the factors:
.
NOTE:
Implementation of resampling algorithm uses factors as defined by the relation
that do not necessarily equal to the ones passed by the user.
Data Types
Resampling primitive supports the following combination of data types for source and destination memory objects:
Propagation |
Source |
Destination |
|---|---|---|
forward / backward |
f32, bf16, f16, s32, s8, u8 |
f32, s32, bf16, s8, u8, f16 |
Post-Ops and Attributes
The following attributes are supported:
Implementation Limitations
No primitive specific limitations. Refer to Data Types for limitations related to data types support.
Performance Tips
N/A
Example
Resampling Primitive Example
This C++ API example demonstrates how to create and execute a Resampling primitive in forward training propagation mode.