Resampling

Intel® oneAPI Deep Neural Network Developer Guide and Reference

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ID 768875

Date 7/13/2023

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Resampling

API reference

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:

Type	Operation	Description	Restrictions
Post-op	Sum	Adds the operation result to the destination tensor instead of overwriting it.
Post-op	Eltwise	Applies an Eltwise operation to the result.
Post-op	Binary	Applies a Binary operation to the result	General binary post-op restrictions

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.

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Intel® oneAPI Deep Neural Network Developer Guide and Reference

Resampling

General

Execution Arguments

Implementation Details

Implementation Limitations

Performance Tips

Example