crop3dLayer

3-D crop layer

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Description

A 3-D crop layer crops a 3-D volume to the size of the input feature map.

Specify the number of inputs to the layer when you create it. The inputs to the layer have the names 'in' and 'ref'. Use the input names when connecting or disconnecting the layer by using connectLayers or disconnectLayers. All inputs to a 3-D crop layer must have the same number of dimensions.

Creation

Syntax

layer = crop3dLayer
layer = crop3dLayer([X Y Z])
layer = crop3dLayer(___,'Name',Name)

Description

layer = crop3dLayer creates a 3-D crop layer that crops an input feature map from the center of the feature map. The size of the cropped region is equal to the size of a second reference input feature map.

layer = crop3dLayer([X Y Z]) also sets the cropLocation property with the (X,Y,Z) coordinate of the crop window. X is the coordinate in the horizontal direction, Y is the coordinate in the vertical direction, and Z is the coordinate in the depth direction.

example

layer = crop3dLayer(___,'Name',Name) also sets the Name property. To create a network containing a 3-D crop layer, you must specify a layer name.

Properties

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Crop

Crop location, specified as 'centercrop' or a three-element numeric vector representing the (x,y,z) coordinate of the crop window.

Layer

Layer name, specified as a character vector or a string scalar. To include a layer in a layer graph, you must specify a nonempty unique layer name. If you train a series network with the layer and Name is set to '', then the software automatically assigns a name to the layer at training time.

Data Types: char | string

Number of inputs of the layer. This layer accepts two inputs.

Data Types: double

Input names of the layer, specified as {'in','ref'}. This layer accepts two inputs.

Data Types: cell

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

Data Types: double

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

Data Types: cell

Examples

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Open Live Script

Create a 3-D crop layer and connect both of its inputs using a layerGraph object.

layers = [
    image3dInputLayer([32 32 32 3],'Name','image')
    convolution3dLayer(3,16,'Padding','same','Name','conv')
    crop3dLayer('Name','crop')
    concatenationLayer(4,2,'Name','concat')
    ]
layers = 
  4x1 Layer array with layers:

     1   'image'    3-D Image Input   32x32x32x3 images with 'zerocenter' normalization
     2   'conv'     Convolution       16 3x3x3 convolutions with stride [1  1  1] and padding 'same'
     3   'crop'     Crop 3D           center crop
     4   'concat'   Concatenation     Concatenation of 2 inputs along dimension 4

Create a layer graph. The first input of the 3-D crop layer is automatically connected to the output of the 3-D convolutional layer.

lgraph = layerGraph(layers);

Add a max pooling layer to the layer graph.

maxPool = maxPooling3dLayer(2,'stride',2,'Name','pool');
lgraph = addLayers(lgraph,maxPool);
lgraph = connectLayers(lgraph,'image','pool');

Connect the second input of the crop layer to the output of the max pooling layer.

lgraph = connectLayers(lgraph,'pool','crop/ref');

Concatenate the crop layer output and the max pooling layer output.

lgraph = connectLayers(lgraph,'pool','concat/in2');

Display the layer graph.

plot(lgraph)

See Also

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Topics

Introduced in R2019b

Deep Learning Toolbox Documentation

Support