dlresize

Resize spatial dimensions of dlarray object

Syntax

Y = dlresize(X,'Scale',scale)

Y = dlresize(X,'OutputSize',outputSize)

Y = dlresize(___,Name,Value)

Description

Y = dlresize(X,'Scale',scale) resizes the spatial dimensions of the dlarray object X by a scale factor scale.

This function requires Deep Learning Toolbox™.

Y = dlresize(X,'OutputSize',outputSize) resizes the spatial dimensions of the dlarray object X so that the spatial dimension sizes are equal to outputSize.

Y = dlresize(___,Name,Value) adjusts the resizing operation using name-value pair arguments. If X is not a formatted dlarray, then you must specify the DataFormat name-value pair argument.

Examples

Resize dlarray by Scale Factor

Read an RGB image.

A = imread('peppers.png');

Convert the image to data type single for use in a dlarray. Then, create a dlarray containing the input image.

A = im2single(A);
dlarrayA = dlarray(A,'SSC');

Rescale the dlarray by a factor of 1.5 vertically.

dlarrayB = dlresize(dlarrayA,'Scale',[1.5 1]);

Extract the image data from the resized dlarray B by using the extractdata (Deep Learning Toolbox) function.

B = extractdata(dlarrayB);

Display the original and resized images as a montage.

montage({A,B},"ThumbnailSize",size(B,[1 2]), ...
    "BorderSize",10,"BackgroundColor","white")

Input Arguments

X — Deep learning array to resize

dlarray object

Deep learning array to resize, specified as a dlarray (Deep Learning Toolbox) object.

scale — Scale factor to resize input

positive number | vector of positive numbers

Scale factor to resize input, specified as a positive number or a vector of positive numbers of length equal to the number of spatial dimensions in X. If scale is a scalar, then dlresize applies the same scale factor to all spatial dimensions.

outputSize — Output size of resized input

vector of positive integers

Output size of resized input, specified as a vector of positive integers of length equal to the number of spatial dimensions in X. You can specify one element as a positive integer and specify the other elements as NaN, in which case the layer computes the other elements automatically to preserve the aspect ratio of the input.

Name-Value Arguments

Specify optional pairs of arguments as Name1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Before R2021a, use commas to separate each name and value, and enclose Name in quotes.

Example: 'Method',linear

DataFormat — Dimension labels

string scalar | character vector

Dimension labels of the input dlarray object X, specified as the comma-separated pair consisting of 'DataFormat' and a string scalar or character vector. Each character must be one of these labels:

• S — Spatial

• C — Channel

• B — Batch observations

• T — Time or sequence

• U — Unspecified

If X is not a formatted dlarray, then you must specify the DataFormat name-value pair argument. For more information, see dlarray (Deep Learning Toolbox).

Example: 'SSC' indicates the array has two spatial dimensions and one channel dimension, appropriate for 2-D RGB image data.

Method — Interpolation method

"nearest" (default) | "linear"

Interpolation method, specified as the comma-separated pair consisting of 'Method' and "nearest" for nearest neighbor interpolation or "linear" for bilinear interpolation.

GeometricTransformMode — Geometric transformation mode

"half-pixel" (default) | "asymmetric"

Geometric transformation mode to map points from input space to output space, specified as the comma-separated pair consisting of 'GeometricTransformMode' and "half-pixel" or "asymmetric".

NearestRoundingMode — Rounding mode for nearest neighbor interpolation

"round" (default) | "floor" | "onnx-10"

Rounding mode for nearest neighbor interpolation, specified as the comma-separated pair consisting of 'NearestRoundingMode' and one of the following.

• "round" — use the same rounding behavior as the MATLAB^® round function.

• "floor" — use the same rounding behavior as the MATLAB floor function.

• "onnx-10" — reproduce the resizing behavior of the ONNX™ (Open Neural Network Exchange) opset 10 Resize operator.

This argument is used when you specify the Method as 'nearest'.

Output Arguments

Y — Resized deep learning array

dlarray object

Resized deep learning array, returned as a dlarray (Deep Learning Toolbox) object.

Extended Capabilities

C/C++ Code Generation

Generate C and C++ code using MATLAB® Coder™.

Usage notes and limitations:

• dlarray inputs with more than 3 spatial (S) dimensions are not supported.

• Code generation supports variable-size data for only the spatial (S) dimension of the dlarray input.

• For a dlarray input with more than one spatial dimension, all spatial dimensions must be fixed-size or all variable-size.

• DataFormat must be compile-time constant.

GPU Code Generation

Generate CUDA® code for NVIDIA® GPUs using GPU Coder™.

Usage notes and limitations:

• dlarray inputs with more than 3 spatial (S) dimensions are not supported.

• Code generation supports variable-size data for only the spatial (S) dimension of the dlarray input.

• For a dlarray input with more than one spatial dimension, all spatial dimensions must be fixed-size or all variable-size.

• DataFormat must be compile-time constant.

GPU Arrays

Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

This function fully supports GPU arrays. For more information, see Image Processing on a GPU.

Version History

Introduced in R2020b

R2024a: Generate CUDA code using GPU Coder

dlresize now supports the generation of optimized CUDA^® code (requires GPU Coder™).

R2024a: Generate C code using MATLAB Coder

dlresize now supports the generation of C code (requires MATLAB Coder™).