openvino/docs/OV_Runtime_UG/layout_overview.md

Layout API overview

Introduction

In few words, with layout NCHW it is easier to understand what model's shape {8, 3, 224, 224} means. Without layout it is just a 4-dimensional tensor.

Concept of layout helps you (and your application) to understand what does each particular dimension of input/output tensor mean. For example, if your input has shape {1, 3, 720, 1280} and layout "NCHW" - it is clear that N(batch) = 1, C(channels) = 3, H(height) = 720 and W(width) = 1280. Without layout information {1, 3, 720, 1280} doesn't give any idea to your application what these number mean and how to resize input image to fit model's expectations.

Reasons when you may want to care about input/output layout:

• Perform model modification:
  • Apply preprocessing steps, like subtract means, divide by scales, resize image, convert RGB<->BGR
  • Set/get batch for a model
• Same operations, used during model conversion phase, see Model Optimizer Embedding Preprocessing Computation
• Improve readability of a model's input and output

Layout syntax

Short

The easiest way is to fully specify each dimension with one alphabetical letter

@sphinxtabset

@sphinxtab{C++}

@snippet docs/snippets/ov_layout.cpp ov:layout:simple

@endsphinxtab

@sphinxtab{Python}

@snippet docs/snippets/ov_layout.py ov:layout:simple

@endsphinxtab

@endsphinxtabset

This assigns 'N' to first dimension, 'C' to second, 'H' to 3rd and 'W' to 4th

Advanced

Advanced syntax allows assigning a word to a dimension. To do this, wrap layout with square brackets [] and specify each name separated by comma ,

@sphinxtabset

@sphinxtab{C++}

@snippet docs/snippets/ov_layout.cpp ov:layout:complex

@endsphinxtab

@sphinxtab{Python}

@snippet docs/snippets/ov_layout.py ov:layout:complex

@endsphinxtab

@endsphinxtabset

Partially defined layout

If some dimension is not important, it's name can be set to ?

@sphinxtabset

@sphinxtab{C++}

@snippet docs/snippets/ov_layout.cpp ov:layout:partially_defined

@endsphinxtab

@sphinxtab{Python}

@snippet docs/snippets/ov_layout.py ov:layout:partially_defined

@endsphinxtab

@endsphinxtabset

Dynamic layout

If number of dimensions is not important, ellipsis ... can be used to specify variadic number of dimensions.

@sphinxtabset

@sphinxtab{C++}

@snippet docs/snippets/ov_layout.cpp ov:layout:dynamic

@endsphinxtab

@sphinxtab{Python}

@snippet docs/snippets/ov_layout.py ov:layout:dynamic

@endsphinxtab

@endsphinxtabset

Predefined names

Layout has pre-defined some widely used in computer vision dimension names:

• N/Batch - batch size
• C/Channels - channels dimension
• D/Depth - depth
• H/Height - height
• W/Width - width

These names are used in PreProcessing API and there is a set of helper functions to get appropriate dimension index from layout

@sphinxtabset

@sphinxtab{C++}

@snippet docs/snippets/ov_layout.cpp ov:layout:predefined

@endsphinxtab

@sphinxtab{Python}

@snippet docs/snippets/ov_layout.py ov:layout:predefined

@endsphinxtab

@endsphinxtabset

Equality

Layout names are case-insensitive, which means that Layout("NCHW") == Layout("nChW") == Layout("[N,c,H,w]")

Dump layout

Layout can be converted to string in advanced syntax format. Can be useful for debugging and serialization purposes

@sphinxtabset

@sphinxtab{C++}

@snippet docs/snippets/ov_layout.cpp ov:layout:dump

@endsphinxtab

@sphinxtab{Python}

@snippet docs/snippets/ov_layout.py ov:layout:dump

@endsphinxtab

@endsphinxtabset

See also

• ov::Layout C++ class documentation