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Visual Neural Network Applications
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/*
 * 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.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 javax.annotation.Nonnull;
import java.util.Arrays;
import java.util.List;
import java.util.Map;
import java.util.stream.Collectors;

/**
 * The type Simple.
 */
public class Simple extends ImageScript {

  /**
   * The Content mixing coeff.
   */
  public final double contentMixingCoeff = 1e1;
  /**
   * The Dream coeff.
   */
  public final double dreamCoeff = 1e-1;
  /**
   * The Resolution.
   */
  public int resolution = 400;
  /**
   * The Coeff style mean.
   */
  public double coeff_style_mean = 1e1;
  /**
   * The Coeff style bandCovariance.
   */
  public double coeff_style_cov = 1e0;
  /**
   * The Style sources.
   */
  public String[] styleSources = {
      "git://github.com/jcjohnson/fast-neural-style.git/master/images/styles/starry_night_crop.jpg"
  };
  /**
   * The Content sources.
   */
  public String[] contentSources = {
      "https://upload.wikimedia.org/wikipedia/commons/thumb/9/97/The_Earth_seen_from_Apollo_17.jpg/1024px-The_Earth_seen_from_Apollo_17.jpg"
  };

  public void accept(@Nonnull NotebookOutput log) {
    StyleTransfer.VGG19 styleTransfer = new StyleTransfer.VGG19();
    Precision precision = Precision.Float;
    styleTransfer.parallelLossFunctions = true;
    styleTransfer.setTiled(false);
    Arrays.stream(contentSources).forEach(contentSource ->
    {
      log.p("Content Source:");
      log.p(log.png(ArtistryUtil.load(contentSource, resolution), "Content Image"));
      log.p("Style Source:");
      for (final CharSequence styleSource : styleSources) {
        log.p(log.png(ArtistryUtil.load(styleSource, resolution), "Style Image"));
      }
      final Map, StyleTransfer.StyleCoefficients> styles = TestUtil.buildMap(x ->
          x.put(
              Arrays.asList(styleSources),
              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(x1 -> x1.stream())
          .collect(Collectors.toMap(x1 -> x1, file -> ArtistryUtil.load(file, resolution))))), styles);
      styleTransfer.transfer(log, canvasImage, styleSetup,
          getTrainingMinutes(), styleTransfer.measureStyle(styleSetup), getMaxIterations(), isVerbose()
      );
    });
  }

  /**
   * 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(Simple.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(Simple.class));
    }
  }

}