resize3dLayer

3-D resize layer

Description

A 3-D resize layer resizes 3-D input by a scale factor, to a specified height, width, and depth, or to the size of a reference input feature map. Use of this layer requires Deep Learning Toolbox™.

Creation

Syntax

layer = resize3dLayer('Scale',scale)

layer = resize3dLayer('OutputSize',outputSize)

layer = resize3dLayer('EnableReferenceInput',tf)

layer = resize3dLayer(___,Name,Value)

Description

layer = resize3dLayer('Scale',scale) creates a 3-D resize layer and sets the Scale property as the scale factor specified by scale.

layer = resize3dLayer('OutputSize',outputSize) creates a 3-D resize layer and sets the OutputSize property with the height, width, and depth specified by outputSize.

layer = resize3dLayer('EnableReferenceInput',tf) creates a 3-D resize layer and sets the EnableReferenceInput property with the boolean specified by tf. When you specify the value as true, the layer adds an additional input that accepts a reference feature map and resizes the input to the size of the reference feature map.

layer = resize3dLayer(___,Name,Value) also sets the optional Method, GeometricTransformMode, NearestRoundingMode, and Name properties using name-value pair arguments. You can specify multiple name-value pair arguments. Enclose each property name in single quotes.

Example: layer = resize3dLayer('OutputSize',[128 128 36],'Method','trilinear') creates a 3-D resize layer that resizes input to 128-by-128-by-36 pixels using trilinear interpolation

Properties

Resize

Scale — Scale factor to resize input

3-element row vector of positive numbers

Scale factor to resize input, specified as 3-element row vector of positive numbers. The scale factors are for the row, column, and plane dimensions, respectively. When creating the layer, you can specify Scale as a scalar to use the same value for all dimensions.

OutputSize — Output size of resized input

3-element row vector of positive integers

Output size of resized input, specified as a 3-element row vector of positive integers of the form [nrows ncols nplanes]. You can specify two elements as NaN, in which case the layer computes the values automatically to preserve the aspect ratio of the input.

EnableReferenceInput — Add reference feature map as input

false or 0 | true or 1

Add reference feature map as input to the layer, specified as a numeric or logical 0 (false) or 1 (true). When you specify the value as true, the layer resizes the height, width, and depth of the input to match the height, width, and depth of the reference feature map. The resizing operation does not change the number of channels of the input.

When you enable a reference feature map, the inputs to the layer have the names 'in1' and 'ref', where 'ref' is the name of the reference feature map. Use the input names when connecting or disconnecting the layer by using connectLayers (Deep Learning Toolbox) or disconnectLayers (Deep Learning Toolbox).

Method — Interpolation method

'nearest' (default) | 'trilinear'

Interpolation method, specified as 'nearest' for nearest neighbor interpolation or 'trilinear' for trilinear interpolation.

GeometricTransformMode — Geometric transformation mode

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

Geometric transformation mode to map points from input space to output space, specified as'half-pixel' or 'asymmetric'.

NearestRoundingMode — Rounding mode for nearest neighbor interpolation

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

Rounding mode for nearest neighbor interpolation, specified as 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 property is valid when the Method property is 'nearest'.

Layer

Name — Layer name

'' (default) | character vector | string scalar

Layer name, specified as a character vector or a string scalar. For Layer array input, the trainNetwork, assembleNetwork, layerGraph, and dlnetwork functions automatically assign names to layers with Name set to ''.

Data Types: char | string

NumInputs — Number of inputs

1 (default) | 2

Number of inputs of the layer, specified as 1 when the EnableReferenceInput property is false or 2 when the EnableReferenceInput property is true.

Data Types: double

InputNames — Input names

{'in'} (default) | {'in','ref'}

Input names of the layer, specified as {'in'} when the EnableReferenceInput property is false or {'in','ref'} when the EnableReferenceInput property is true.

Data Types: cell

NumOutputs — Number of outputs

1 (default)

This property is read-only.

Number of outputs of the layer. This layer has a single output only.

Data Types: double

OutputNames — Output names

{'out'} (default)

This property is read-only.

Output names of the layer. This layer has a single output only.

Data Types: cell

Examples

Create 3-D Resize Layer Specifying Scale Factor

Create a 3-D resize layer. Specify a horizontal and vertical scale factor of 2 and a depthwise scale factor of 4.

layer = resize3dLayer('Scale',[2 2 4])

layer = 
  Resize3DLayer with properties:

                      Name: ''
                     Scale: [2 2 4]
                OutputSize: []
      EnableReferenceInput: 0
                    Method: 'nearest'
    GeometricTransformMode: 'half-pixel'
       NearestRoundingMode: 'round'

   Learnable Parameters
    No properties.

   State Parameters
    No properties.

  Show all properties

Create 3-D Resize Layer Specifying Output Size

Create a 3-D resize layer named 'resize224' with an output size of [224 224 224].

layer = resize3dLayer('OutputSize',[224 224 224],'Name','resize224')

layer = 
  Resize3DLayer with properties:

                      Name: 'resize224'
                     Scale: []
                OutputSize: [224 224 224]
      EnableReferenceInput: 0
                    Method: 'nearest'
    GeometricTransformMode: 'half-pixel'
       NearestRoundingMode: 'round'

   Learnable Parameters
    No properties.

   State Parameters
    No properties.

  Show all properties

Create 3-D Resize Layer with Reference Port

Create an array of layers that includes a 3-D resize layer that accepts a reference input feature map.

layers = [
    image3dInputLayer([32 32 32 3],'Name','image')
    resize3dLayer('EnableReferenceInput',true,'Name','resize')]

layers = 
  2x1 Layer array with layers:

     1   'image'    3-D Image Input   32x32x32x3 images with 'zerocenter' normalization
     2   'resize'   Resize            nnet.cnn.layer.Resize3DLayer

Create a layerGraph. The first input of the 3-D resize layer is automatically connected to the output of the 3-D image input layer.

lgraph = layerGraph(layers);

Connect the 'ref' input of the 3-D resize layer to the output of a layer that provides a reference feature map by using the connectLayers function. This example shows a trivial connection in which the 'ref' input is also connected to the output of the 3-D image input layer.

lgraph = connectLayers(lgraph,'image','resize/ref');

Create 3-D Resize Layer with Trilinear Interpolation

Create a 3-D resize layer named 'rescale0.5' with a uniform scale factor of 0.5. Specify the interpolation method as trilinear interpolation.

layer = resize3dLayer('Scale',0.5,'Method','trilinear','Name','rescale0.5')

layer = 
  Resize3DLayer with properties:

                      Name: 'rescale0.5'
                     Scale: [0.5000 0.5000 0.5000]
                OutputSize: []
      EnableReferenceInput: 0
                    Method: 'trilinear'
    GeometricTransformMode: 'half-pixel'
       NearestRoundingMode: 'round'

   Learnable Parameters
    No properties.

   State Parameters
    No properties.

  Show all properties

References

[1] Open Neural Network Exchange. https://github.com/onnx/.

[2] ONNX. https://onnx.ai/.