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Visual Neural Network Applications
/*
* Copyright (c) 2019 by Andrew Charneski.
*
* The author licenses this file to you under the
* Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance
* with the License. You may obtain a copy
* of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
* KIND, either express or implied. See the License for the
* specific language governing permissions and limitations
* under the License.
*/
package com.simiacryptus.mindseye.style_transfer;
import com.simiacryptus.aws.exe.EC2NotebookRunner;
import com.simiacryptus.aws.exe.LocalNotebookRunner;
import com.simiacryptus.mindseye.ImageScript;
import com.simiacryptus.mindseye.applications.ArtistryUtil;
import com.simiacryptus.mindseye.applications.HadoopUtil;
import com.simiacryptus.mindseye.applications.StyleTransfer;
import com.simiacryptus.mindseye.lang.Tensor;
import com.simiacryptus.mindseye.lang.cudnn.Precision;
import com.simiacryptus.mindseye.models.CVPipe_VGG19;
import com.simiacryptus.mindseye.test.TestUtil;
import com.simiacryptus.notebook.NotebookOutput;
import com.simiacryptus.notebook.TableOutput;
import javax.annotation.Nonnull;
import java.awt.image.BufferedImage;
import java.util.Arrays;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.stream.Collectors;
/**
* The type Style survey.
*/
public class StyleSurvey extends ImageScript {
/**
* The Resolution.
*/
public int resolution = 600;
/**
* The Coeff style mean.
*/
public double coeff_style_mean = 1e1;
/**
* The Content mixing coeff.
*/
public double contentMixingCoeff = 1e1;
/**
* The Dream coeff.
*/
public double dreamCoeff = 1e0;
/**
* The Coeff style bandCovariance.
*/
public double coeff_style_cov = 1e0;
/**
* The Style sources.
*/
public CharSequence[] styleSources = HadoopUtil.getFiles("git://github.com/jcjohnson/fast-neural-style.git/master/images/styles/").toArray(new CharSequence[]{});
/**
* The Content sources.
*/
public String[] contentSources = {
"https://upload.wikimedia.org/wikipedia/commons/thumb/1/1d/Taj_Mahal_%28Edited%29.jpeg/1920px-Taj_Mahal_%28Edited%29.jpeg"
};
public void accept(@Nonnull NotebookOutput log) {
StyleTransfer.VGG19 styleTransfer = new StyleTransfer.VGG19();
Precision precision = Precision.Float;
styleTransfer.parallelLossFunctions = true;
styleTransfer.setTiled(false);
TableOutput experimentTable = new TableOutput();
Arrays.stream(contentSources).forEach(contentSource ->
{
log.p("Content Source:");
log.p(log.png(ArtistryUtil.load(contentSource, resolution), "Content Image"));
BufferedImage[] imgs = Arrays.stream(styleSources).map(x -> Arrays.asList((CharSequence) x)).map(sources ->
{
try {
log.p("Style Source:");
for (final CharSequence styleSource : sources) {
log.p(log.png(ArtistryUtil.load(styleSource, resolution), "Style Image"));
}
final Map, StyleTransfer.StyleCoefficients> styles = TestUtil.buildMap(x ->
x.put(
sources,
new StyleTransfer.StyleCoefficients(
StyleTransfer.CenteringMode.Origin)
.set(CVPipe_VGG19.Layer.Layer_0, coeff_style_mean, coeff_style_cov, dreamCoeff)
.set(CVPipe_VGG19.Layer.Layer_1a, coeff_style_mean, coeff_style_cov, dreamCoeff)
.set(CVPipe_VGG19.Layer.Layer_1b, coeff_style_mean, coeff_style_cov, dreamCoeff)
.set(CVPipe_VGG19.Layer.Layer_1c, coeff_style_mean, coeff_style_cov, dreamCoeff)
));
Tensor canvasImage = ArtistryUtil.loadTensor(contentSource, resolution);
canvasImage = Tensor.fromRGB(TestUtil.resize(canvasImage.toImage(), resolution, true));
canvasImage = ArtistryUtil.expandPlasma((
Tensor.fromRGB(TestUtil.resize(canvasImage.toImage(), 16, true))),
1000.0, 1.1, resolution
).scale(0.9);
StyleTransfer.StyleSetup styleSetup = new StyleTransfer.StyleSetup<>(
precision,
ArtistryUtil.loadTensor(
contentSource,
canvasImage.getDimensions()[0],
canvasImage.getDimensions()[1]
),
new StyleTransfer.ContentCoefficients()
.set(CVPipe_VGG19.Layer.Layer_1a, contentMixingCoeff * 1e-1)
.set(CVPipe_VGG19.Layer.Layer_1c, contentMixingCoeff)
.set(CVPipe_VGG19.Layer.Layer_1d, contentMixingCoeff),
TestUtil.buildMap(y -> y.putAll(
styles.keySet().stream().flatMap(x -> x.stream())
.collect(Collectors.toMap(
x -> x,
file -> ArtistryUtil.load(file, resolution)
)))), styles
);
Tensor image = styleTransfer.transfer(
log,
canvasImage,
styleSetup,
getTrainingMinutes(),
styleTransfer.measureStyle(styleSetup),
getMaxIterations(),
isVerbose()
);
HashMap row = new HashMap<>();
row.put("Style", sources.stream().reduce((a, b) -> a + "\n" + b).get());
row.put("Image", log.png(image.toImage(), "image"));
experimentTable.putRow(row);
return image.toImage();
} catch (Throwable throwable) {
log.eval(() -> {
return throwable;
});
return null;
}
}).filter(x -> x != null).toArray(i -> new BufferedImage[i]);
log.p("Summary Table:");
log.p(experimentTable.toMarkdownTable());
log.p("Animated Sequence:");
log.p(TestUtil.animatedGif(log, imgs));
});
}
/**
* The type Local.
*/
public static class Local {
/**
* The entry point of application.
*
* @param args the input arguments
* @throws Exception the exception
*/
public static void main(String... args) throws Exception {
LocalNotebookRunner.run(LocalNotebookRunner.getTask(StyleSurvey.class));
}
}
/**
* The type Ec 2.
*/
public static class EC2 {
/**
* The entry point of application.
*
* @param args the input arguments
* @throws Exception the exception
*/
public static void main(String... args) throws Exception {
EC2NotebookRunner.run(LocalNotebookRunner.getTask(StyleSurvey.class));
}
}
}