openvino/docs/OV_Runtime_UG/layout_overview.md

Layout API Overview

The concept of layout helps you (and your application) to understand what each particular dimension of input/output tensor means. For example, if your input has the {1, 3, 720, 1280} shape and the NCHW layout, it is clear that N(batch) = 1, C(channels) = 3, H(height) = 720, and W(width) = 1280. Without the layout information, the {1, 3, 720, 1280} tuple does not give any idea to your application on what these numbers mean and how to resize the input image to fit the expectations of the model.

With the NCHW layout, it is easier to understand what the {8, 3, 224, 224} model shape means. Without the layout, it is just a 4-dimensional tensor.

Below is a list of cases where input/output layout is important:

• Performing model modification:
  • Applying the preprocessing steps, such as subtracting means, dividing by scales, resizing an image, and converting RGB<->BGR.
  • Setting/getting a batch for a model.
• Doing the same operations as used during the model conversion phase. For more information, refer to the Model Optimizer Embedding Preprocessing Computation guide.
• Improving the readability of a model input and output.

Syntax of Layout

Short Syntax

The easiest way is to fully specify each dimension with one alphabet 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 the first dimension, C to the second, H to the third, and W to the fourth.

Advanced Syntax

The advanced syntax allows assigning a word to a dimension. To do this, wrap a layout with square brackets [] and specify each name separated by a 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 a certain dimension is not important, its 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 several dimensions are not important, an ellipsis ... can be used to specify those 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

A layout has some pre-defined dimension names, widely used in computer vision:

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

These names are used in PreProcessing API. There is a set of helper functions to get appropriate dimension index from a 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

A layout can be converted to a string in the advanced syntax format. It 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

• API Reference: ov::Layout C++ class