cycleGANGenerator
Description
creates a CycleGAN generator network for input of size net
= cycleGANGenerator(inputSize
)inputSize
. For
more information about the network architecture, see CycleGAN Generator Network.
This function requires Deep Learning Toolbox™.
modifies aspects of the CycleGAN network using name-value arguments.net
= cycleGANGenerator(inputSize
,Name=Value
)
Examples
Create CycleGAN Generator
Specify the network input size for RGB images of size 256-by-256.
inputSize = [256 256 3];
Create a CycleGAN generator that generates RGB images of the input size.
net = cycleGANGenerator(inputSize)
net = dlnetwork with properties: Layers: [72x1 nnet.cnn.layer.Layer] Connections: [80x2 table] Learnables: [94x3 table] State: [0x3 table] InputNames: {'inputLayer'} OutputNames: {'fActivation'} Initialized: 1 View summary with summary.
Display the network.
analyzeNetwork(net)
Create CycleGAN Generator with Six Residual Blocks
Specify the network input size for RGB images of size 128-by-128 pixels.
inputSize = [128 128 3];
Create a CycleGAN generator with six residual blocks. Add the prefix "cycleGAN6_" to all layer names.
net = cycleGANGenerator(inputSize,"NumResidualBlocks",6, ... "NamePrefix","cycleGAN6_")
net = dlnetwork with properties: Layers: [54x1 nnet.cnn.layer.Layer] Connections: [59x2 table] Learnables: [70x3 table] State: [0x3 table] InputNames: {'cycleGAN6_inputLayer'} OutputNames: {'cycleGAN6_fActivation'} Initialized: 1 View summary with summary.
Display the network.
analyzeNetwork(net)
Input Arguments
inputSize
— Network input size
3-element vector of positive integers
Network input size, specified as a 3-element vector of positive integers.
inputSize
has the form [H
W
C], where H is the height,
W is the width, and C is the number of
channels.
Example: [28 28 3]
specifies an input size of 28-by-28 pixels for a
3-channel image.
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.
Example: net = cycleGANGenerator(inputSize,NumFiltersInFirstBlock=32)
creates a network with 32 filters in the first convolution layer.
Before R2021a, use commas to separate each name and value, and enclose
Name
in quotes.
Example: net =
cycleGANGenerator(inputSize,"NumFiltersInFirstBlock",32)
creates a network with
32 filters in the first convolution layer.
NumDownsamplingBlocks
— Number of downsampling blocks
2
(default) | positive integer
Number of downsampling blocks in the network encoder module, specified as a
positive integer. In total, the network downsamples the input by a factor of
2^NumDownsamplingBlocks
. The decoder module consists of the
same number of upsampling blocks.
NumFiltersInFirstBlock
— Number of filters in first convolution layer
64
(default) | positive even integer
Number of filters in the first convolution layer, specified as a positive even integer.
NumOutputChannels
— Number of output channels
"auto"
(default) | positive integer
Number of output channels, specified as "auto"
or a positive
integer. When you specify "auto"
, the number of output channels is
the same as the number of input channels.
FilterSizeInFirstAndLastBlocks
— Filter size in first and last convolution layers
7
(default) | positive odd integer | 2-element vector of positive odd integers
Filter size in the first and last convolution layers, specified as a positive odd integer or 2-element vector of positive odd integers of the form [height width]. When you specify the filter size as a scalar, the filter has identical height and width.
FilterSizeInIntermediateBlocks
— Filter size in intermediate convolution layers
3
(default) | 2-element vector of positive odd integers | positive odd integer
Filter size in intermediate convolution layers, specified as a positive odd integer or 2-element vector of positive odd integers of the form [height width]. The intermediate convolution layers are the convolution layers excluding the first and last convolution layer. When you specify the filter size as a scalar, the filter has identical height and width. Typical values are between 3 and 7.
NumResidualBlocks
— Number of residual blocks
9
(default) | positive integer
Number of residual blocks, specified as a positive integer. Typically, this value
is set to 6
for images of size 128-by-128 and 9
for images of size 256-by-256 or larger.
ConvolutionPaddingValue
— Style of padding
"symmetric-exclude-edge"
(default) | "replicate"
| "symmetric-include-edge"
| numeric scalar
Style of padding used in the network, specified as one of these values.
PaddingValue | Description | Example |
---|---|---|
Numeric scalar | Pad with the specified numeric value |
|
"symmetric-include-edge" | Pad using mirrored values of the input, including the edge values |
|
"symmetric-exclude-edge" | Pad using mirrored values of the input, excluding the edge values |
|
"replicate" | Pad using repeated border elements of the input |
|
UpsampleMethod
— Method used to upsample activations
"transposedConv"
(default) | "bilinearResize"
| "pixelShuffle"
Method used to upsample activations, specified as one of these values:
"transposedConv"
— Use atransposedConv2dLayer
(Deep Learning Toolbox) with a stride of [2 2]"bilinearResize"
— Use aconvolution2dLayer
(Deep Learning Toolbox) with a stride of [1 1] followed by aresize2dLayer
with a scale of [2 2]"pixelShuffle"
— Use aconvolution2dLayer
(Deep Learning Toolbox) with a stride of [1 1] followed by adepthToSpace2dLayer
with a block size of [2 2]
Data Types: char
| string
ConvolutionWeightsInitializer
— Weight initialization used in convolution layers
"narrow-normal"
(default) | "glorot"
| "he"
| function
Weight initialization used in convolution layers, specified as
"glorot"
, "he"
,
"narrow-normal"
, or a function handle. For more information, see
Specify Custom Weight Initialization Function (Deep Learning Toolbox).
ActivationLayer
— Activation function
"relu"
(default) | "leakyRelu"
| "elu"
| layer object
Activation function to use in the network, specified as one of these values. For more information and a list of available layers, see Activation Layers (Deep Learning Toolbox).
"relu"
— Use areluLayer
(Deep Learning Toolbox)"leakyRelu"
— Use aleakyReluLayer
(Deep Learning Toolbox) with a scale factor of 0.2"elu"
— Use aneluLayer
(Deep Learning Toolbox)A layer object
FinalActivationLayer
— Activation function after final convolution
"tanh"
(default) | "none"
| "sigmoid"
| "softmax"
| layer object
Activation function after the final convolution layer, specified as one of these values. For more information and a list of available layers, see Activation Layers (Deep Learning Toolbox).
"tanh"
— Use atanhLayer
(Deep Learning Toolbox)"sigmoid"
— Use asigmoidLayer
(Deep Learning Toolbox)"softmax"
— Use asoftmaxLayer
(Deep Learning Toolbox)"none"
— Do not use a final activation layerA layer object
NormalizationLayer
— Normalization operation
"instance"
(default) | "none"
| "batch"
| layer object
Normalization operation to use after each convolution, specified as one of these values. For more information and a list of available layers, see Normalization Layers (Deep Learning Toolbox).
"instance"
— Use aninstanceNormalizationLayer
(Deep Learning Toolbox)"batch"
— Use abatchNormalizationLayer
(Deep Learning Toolbox)"none"
— Do not use a normalization layerA layer object
Dropout
— Probability of dropout
0
(default) | number in the range [0, 1]
Probability of dropout, specified as a number in the range [0, 1]. If you specify
a value of 0
, then the network does not include dropout layers. If
you specify a value greater than 0
, then the network includes a
dropoutLayer
(Deep Learning Toolbox)
in each residual block.
NamePrefix
— Prefix to all layer names
""
(default) | string | character vector
Prefix to all layer names in the network, specified as a string or character vector.
Data Types: char
| string
Output Arguments
net
— CycleGAN generator network
dlnetwork
object
CycleGAN generator network, returned as a dlnetwork
(Deep Learning Toolbox) object.
More About
CycleGAN Generator Network
A cycleGAN generator network consists of an encoder module followed by a decoder module. The default network follows the architecture proposed by Zhu et. al. [1].
The encoder module downsamples the input by a factor of
2^NumDownsamplingBlocks
. The encoder module consists of an initial
block of layers, NumDownsamplingBlocks
downsampling blocks, and
NumResidualBlocks
residual blocks. The decoder module upsamples the
input by a factor of 2^NumDownsamplingBlocks
. The decoder module
consists of NumDownsamplingBlocks
upsampling blocks and a final block.
The table describes the blocks of layers that comprise the encoder and decoder modules.
Block Type | Layers | Diagram of Default Block |
---|---|---|
Initial block |
|
|
Downsampling block |
|
|
Residual block |
|
|
Upsampling block |
|
|
Final block |
|
|
References
[1] Zhu, Jun-Yan, Taesung Park, Phillip Isola, and Alexei A. Efros. "Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks." In 2017 IEEE International Conference on Computer Vision (ICCV), 2242–2251. Venice: IEEE, 2017. https://ieeexplore.ieee.org/document/8237506.
[2] Zhu, Jun-Yan, Taesung Park, and Tongzhou Wang. "CycleGAN and pix2pix in PyTorch." https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix.
Version History
Introduced in R2021a
See Also
Topics
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