depthToSpace2dLayer

Depth to space layer

Since R2021a

Description

A 2-D depth to space layer permutes data from the depth dimension into blocks of 2-D spatial data.

Given an input feature map of size [H W C*height*width] and blocks of size [height width], the output feature map size is [H*height W*width C].

This object requires Deep Learning Toolbox™.

Creation

Syntax

layer = depthToSpace2dLayer(blockSize)

layer = depthToSpace2dLayer(blockSize,Name=Value)

Description

layer = depthToSpace2dLayer(blockSize) creates a 2-D depth to space layer, specifying the block size to rearrange the input activation. The blockSize input sets the BlockSize property.

layer = depthToSpace2dLayer(blockSize,Name=Value) uses one or more name-value arguments to set the Mode and Name properties.

Example: depthToSpace2dLayer(blockSize,Mode="crd") creates a 2-D depth to space layer that orders data by column, row, and then depth.

Properties

BlockSize — Block size to reorder input activation

vector of two positive integers

Block size to reorder the input activation, specified as a vector of two positive integers [h w], where h is the height and w is the width. When creating the layer, you can specify BlockSize as a scalar to use the same value for both dimensions.

Example: [2 1] specifies blocks of height 2 and width 1.

Mode — Order of rearranged dimensions

"dcr" (default) | "crd"

Order of rearranged dimensions from the input data, specified as "dcr" or "crd". When you specify "dcr", the layer orders data by depth, column, and then row. When you specify "crd", the layer orders data by column, row, and then depth.

Data Types: char | string

Name — Layer name

"" (default) | character vector | string scalar

Layer name, specified as a character vector or string scalar. For Layer array input, the trainnet (Deep Learning Toolbox) and dlnetwork (Deep Learning Toolbox) functions automatically assign names to layers with the name "".

The DepthToSpace2DLayer object stores this property as a character vector.

Data Types: char | string

NumInputs — Number of inputs

1 (default)

This property is read-only.

Number of inputs to the layer, returned as 1. This layer accepts a single input only.

Data Types: double

InputNames — Input names

{'in'} (default)

This property is read-only.

Input names, returned as {'in'}. This layer accepts a single input only.

Data Types: cell

NumOutputs — Number of outputs

1 (default)

This property is read-only.

Number of outputs from the layer, returned as 1. This layer has a single output only.

Data Types: double

OutputNames — Output names

{'out'} (default)

This property is read-only.

Output names, returned as {'out'}. This layer has a single output only.

Data Types: cell

Examples

Create 2-D Depth to Space Layer

Specify the block size for reordering input activations.

blockSize = [2 2];

Create a 2-D depth to space layer that orders data by column, row, and then depth.

layer = depthToSpace2dLayer(blockSize,"Mode","crd","Name","depthToSpaceLayer")

layer = 
  DepthToSpace2DLayer with properties:

         Name: 'depthToSpaceLayer'
    BlockSize: [2 2]
         Mode: "crd"

   Learnable Parameters
    No properties.

   State Parameters
    No properties.

Use properties method to see a list of all properties.

Extended Capabilities

C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.

GPU Code Generation

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

To generate CUDA^® or C++ code by using GPU Coder™, you must first construct and train a deep neural network. Once the network is trained and evaluated, you can configure the code generator to generate code and deploy the convolutional neural network on platforms that use NVIDIA^® or ARM^® GPU processors. For more information, see Deep Learning with GPU Coder (GPU Coder).

For this layer, you can generate code that takes advantage of the NVIDIA CUDA deep neural network library (cuDNN), NVIDIA TensorRT™ high performance inference library, or the ARM Compute Library for Mali GPU.

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

Version History

Introduced in R2021a