Wolfram Research

StarGAN Trained on CelebA Data

Translate an image from one domain to another

Released in 2017, StarGAN performs multi-domain image-to-image translation. Adapted from CycleGANs, StarGAN uses the same architecture for the generator network and has a similiar objective loss but instead of having one generator for every particular image translation task, StarGAN has a single generator for all translations.

Trained size: 34 MB | Number of models: 2

Training Set Information

Model Information

Examples

Resource retrieval

Get the pre-trained net:

In[1]:=
NetModel["StarGAN Trained on CelebA Data"]
Out[1]=

NetModel parameters

This model consists of a family of individual nets, each identified by a specific parameter combination. Inspect the available parameters:

In[2]:=
NetModel["StarGAN Trained on CelebA Data", "ParametersInformation"]
Out[2]=

Pick a non-default net by specifying the parameters:

In[3]:=
NetModel[{"StarGAN Trained on CelebA Data", "ImageSize" -> 256}]
Out[3]=

Pick a non-default uninitialized net:

In[4]:=
NetModel[{"StarGAN Trained on CelebA Data", "ImageSize" -> 256}, "UninitializedEvaluationNet"]
Out[4]=

Basic usage

Evaluate a net on a photo:

In[5]:=
NetModel["StarGAN Trained on CelebA Data"][<|"Image" -> \!\(\*
GraphicsBox[
TagBox[RasterBox[CompressedData["
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"], {{0, 48.375352736947036`}, {48.000350002552096`, 0}}, {0, 255},
ColorFunction->RGBColor,
ImageResolution->{191.9986, 191.9986}],
BoxForm`ImageTag["Byte", ColorSpace -> "RGB", Interleaving -> True],
Selectable->False],
DefaultBaseStyle->"ImageGraphics",
ImageSizeRaw->{48.000350002552096`, 48.375352736947036`},
PlotRange->{{0, 48.000350002552096`}, {0, 48.375352736947036`}}]\), "Attributes" -> {"black", "young"}|>]
Out[5]=

Attributes interpolation

Remove the class encoder from the net:

In[6]:=
net = NetModel[{"StarGAN Trained on CelebA Data", "ImageSize" -> 128}];
In[7]:=
generator = NetReplacePart[
  NetExtract[net, "Generator"], {"Image" -> NetExtract[net, "Image"], "Output" -> NetExtract[net, "Output"]}]
Out[7]=

Instead of using one-hot representation for the class attributes, allow continuous inputs:

In[8]:=
Manipulate[
 generator[<|"Attributes" -> {black, blonde, brunette, male, young}, "Image" -> \!\(\*
GraphicsBox[
TagBox[RasterBox[CompressedData["
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"], {{0, 48.375352736947036`}, {48.000350002552096`, 0}}, {0, 255},
ColorFunction->RGBColor,
ImageResolution->{191.9986, 191.9986}],
BoxForm`ImageTag["Byte", ColorSpace -> "RGB", Interleaving -> True],
Selectable->False],
DefaultBaseStyle->"ImageGraphics",
ImageSize->{97.8753500025521, Automatic},
ImageSizeRaw->{48.000350002552096`, 48.375352736947036`},
PlotRange->{{0, 48.000350002552096`}, {0, 48.375352736947036`}}]\)|>],
 {black, 0, 1}, {blonde, 0, 1}, {brunette, 0, 1}, {male, 0, 1}, {young, 0.5, 1}
 ]
Out[8]=

Net information

Inspect the sizes of all arrays in the net:

In[9]:=
Information[NetModel["StarGAN Trained on CelebA Data"], "ArraysSizes"]
Out[9]=

Obtain the total number of parameters:

In[10]:=
Information[
 NetModel["StarGAN Trained on CelebA Data"], "ArraysTotalElementCount"]
Out[10]=

Obtain the layer type counts:

In[11]:=
Information[
 NetModel["StarGAN Trained on CelebA Data"], "LayerTypeCounts"]
Out[11]=

Display the summary graphic:

In[12]:=
Information[
 NetModel["StarGAN Trained on CelebA Data"], "SummaryGraphic"]
Out[12]=

Export to ONNX

Export the net to the ONNX format:

In[13]:=
onnxFile = Export[FileNameJoin[{$TemporaryDirectory, "net.onnx"}], NetExtract[NetModel["StarGAN Trained on CelebA Data"], "Generator"]]
Out[13]=

Get the size of the ONNX file:

In[14]:=
FileByteCount[onnxFile]
Out[14]=

The size is similar to the byte count of the resource object :

In[15]:=
NetModel["StarGAN Trained on CelebA Data", "ByteCount"]
Out[15]=

Check some metadata of the ONNX model:

In[16]:=
{OpsetVersion, IRVersion} = {Import[onnxFile, "OperatorSetVersion"], Import[onnxFile, "IRVersion"]}
Out[16]=

Import the model back into the Wolfram Language. However, the NetEncoder and NetDecoder will be absent because they are not supported by ONNX:

In[17]:=
Import[onnxFile]
Out[17]=

Resource History

Reference

  • Y. Choi, M. Choi, M. Kim, J.-W. Ha, S. Kim, J. Choo, "StarGAN: Unified Generative Adversarial Networks for Multi-domain Image-to-Image Translation," arXiv:1711.09020 (2017)
  • Available from: https://github.com/yunjey/stargan
  • Rights: MIT License