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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.texture_generation;
import com.simiacryptus.mindseye.ImageScript;
import com.simiacryptus.mindseye.applications.ArtistryUtil;
import com.simiacryptus.mindseye.applications.ColorTransfer;
import com.simiacryptus.mindseye.applications.ImageArtUtil;
import com.simiacryptus.mindseye.applications.TextureGeneration;
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 javax.annotation.Nonnull;
import java.util.Arrays;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.concurrent.atomic.AtomicReference;
import java.util.stream.Collectors;
/**
* The type Hi def.
*/
public abstract class TextureLayerSurvey extends ImageScript {
public final double coeff_style_mean;
/**
* The Coeff style bandCovariance.
*/
public final double coeff_style_cov;
/**
* The Style sources.
*/
public final String[] styleSources;
/**
* The Resolution.
*/
public final int[] resolutionSchedule;
/**
* The Dream coeff.
*/
public final double dreamCoeff;
private final int style_resolution;
private final double aspect_ratio;
private final double plasma_magnitude;
public TextureLayerSurvey(
final double coeff_style_mean,
final double coeff_style_cov,
final double dreamCoeff,
final int[] resolutionSchedule,
final int style_resolution,
final double aspect_ratio,
final double plasma_magnitude,
final String... styleSources
) {
this.coeff_style_mean = coeff_style_mean;
this.coeff_style_cov = coeff_style_cov;
this.dreamCoeff = dreamCoeff;
this.resolutionSchedule = resolutionSchedule;
this.style_resolution = style_resolution;
this.styleSources = styleSources;
this.aspect_ratio = aspect_ratio;
this.plasma_magnitude = plasma_magnitude;
}
public void accept(@Nonnull NotebookOutput log) {
Precision precision = Precision.Float;
log.p("Style Source:");
for (final CharSequence styleSource : styleSources) {
log.p(log.png(ArtistryUtil.load(styleSource, style_resolution), "Style Image"));
}
final AtomicReference canvas = new AtomicReference<>(ArtistryUtil.paint_Plasma(
3,
1000.0,
1.1,
resolutionSchedule[0],
(int) (aspect_ratio * resolutionSchedule[0])
).scale(plasma_magnitude));
canvas.set(log.subreport("Color_Space_Analog", sublog -> {
ColorTransfer contentColorTransform = new ColorTransfer.VGG19() {
}.setOrtho(false).setUnit(true);
//colorSyncContentCoeffMap.set(CVPipe_VGG19.Layer.Layer_1a, 1e-1);
int colorSyncResolution = 600;
Tensor resizedCanvas = Tensor.fromRGB(TestUtil.resize(canvas.get().toImage(), colorSyncResolution, (int) (aspect_ratio * colorSyncResolution)));
final ColorTransfer.StyleSetup styleSetup = ImageArtUtil.getColorAnalogSetup(
Arrays.asList(styleSources),
precision,
resizedCanvas,
ImageArtUtil.getStyleImages(
styleSources,
new HashMap<>(),
colorSyncResolution, (int) (aspect_ratio * colorSyncResolution)
),
CVPipe_VGG19.Layer.Layer_0
);
contentColorTransform.transfer(
sublog,
resizedCanvas,
styleSetup,
getTrainingMinutes(),
contentColorTransform.measureStyle(styleSetup),
getMaxIterations(),
isVerbose()
);
return contentColorTransform.forwardTransform(canvas.get());
}));
for (final List layers : getLayers()) {
String reportName = layers.stream().map(x -> x.name()).reduce((a, b) -> a + "_" + b).get();
log.h1(reportName);
Tensor subresult = log.subreport(reportName, subreport -> {
final Map, TextureGeneration.StyleCoefficients> styles = TestUtil.buildMap(x -> {
TextureGeneration.StyleCoefficients styleCoefficients = new TextureGeneration.StyleCoefficients<>(
TextureGeneration.CenteringMode.Origin);
for (final CVPipe_VGG19.Layer layer : layers) {
styleCoefficients.set(
layer,
coeff_style_mean,
coeff_style_cov,
dreamCoeff
);
}
x.put(
Arrays.asList(styleSources),
styleCoefficients
);
});
TextureGeneration.StyleSetup styleSetup = new TextureGeneration.StyleSetup<>(
precision,
TestUtil.buildMap(y -> y.putAll(
styles.keySet().stream().flatMap(
x -> x.stream())
.collect(Collectors.toMap(
x -> x,
file -> ArtistryUtil.load(
file,
style_resolution
)
)))),
styles
);
AtomicReference canvasCopy = new AtomicReference<>(canvas.get().copy());
for (final Integer width : resolutionSchedule) {
TextureGeneration.VGG19 textureGeneration = new TextureGeneration.VGG19();
textureGeneration.parallelLossFunctions = true;
int height = (int) (aspect_ratio * width);
textureGeneration.setTiling((int) Math.max(Math.min((2.0 * Math.pow(600, 2)) / (width * height), 9), 2));
canvasCopy.set(Tensor.fromRGB(TestUtil.resize(canvasCopy.get().toImage(), width, height)));
subreport.p("Input Parameters:");
subreport.eval(() -> {
return ArtistryUtil.toJson(styleSetup);
});
canvasCopy.set(textureGeneration.optimize(
subreport,
textureGeneration.measureStyle(styleSetup), canvasCopy.get(),
getTrainingMinutes(),
getMaxIterations(), isVerbose(), styleSetup.precision
));
}
return canvasCopy.get();
});
log.p(log.png(subresult.toImage(), reportName));
}
}
@Nonnull
public abstract List> getLayers();
}