gan example

Signed-off-by: brian <brian@brutex.de>

.docker/Dockerfile

@@ -1,4 +1,4 @@
-FROM nvidia/cuda:12.1.0-cudnn8-devel-ubuntu22.04
+FROM nvidia/cuda:11.4.3-cudnn8-devel-ubuntu20.04
 
 RUN apt-get update &&  \
     DEBIAN_FRONTEND=noninteractive apt-get install -y openjdk-11-jdk wget build-essential checkinstall zlib1g-dev libssl-dev git
@@ -11,10 +11,5 @@ RUN wget -nv https://github.com/Kitware/CMake/releases/download/v3.24.2/cmake-3.
     rm cmake-3.24.2-linux-x86_64.sh
 
 
-RUN echo "/usr/local/cuda/compat/" >> /etc/ld.so.conf.d/cuda-driver.conf
-
 RUN echo "nameserver 8.8.8.8" >> /etc/resolv.conf
 
-RUN ldconfig -p | grep cuda
-
-

.jenkins/linux-x86_64-cpu-build.jenkinsfile

@@ -65,7 +65,7 @@ pipeline {
         }*/
         stage('publish-linux-cpu') {
             environment {
-                MAVEN = credentials('Internal_Archiva')
+                MAVEN = credentials('Internal Archiva')
                 OSSRH = credentials('OSSRH')
             }
 
@@ -79,9 +79,4 @@ pipeline {
             }
         }
     }
-    post {
-        always {
-            junit '**/build/test-results/**/*.xml'
-        }
-    }
 }

.jenkins/linux-x86_64-cuda-build.jenkinsfile

@@ -59,10 +59,4 @@ pipeline {
             }
         }
     }
-
-    post {
-        always {
-            junit '**/build/test-results/**/*.xml'
-        }
-    }
 }

.jenkins/linux-x86_64-docker-cpu-build.jenkinsfile

@@ -85,9 +85,4 @@ pipeline {
             }
         }
     }
-    post {
-        always {
-            junit '**/build/test-results/**/*.xml'
-        }
-    }
 }

.jenkins/linux-x86_64-docker-cuda-build.jenkinsfile

@@ -26,7 +26,7 @@ pipeline {
             dir '.docker'
             label 'linux && docker && cuda'
             //additionalBuildArgs  '--build-arg version=1.0.2'
-            args '--gpus all' //needed for test only, you can build without GPU
+            //args '--gpus all' --needed for test only, you can build without GPU
         }
     }
 
@@ -72,10 +72,4 @@ pipeline {
             }
         }
     }
-
-    post {
-        always {
-            junit '**/build/test-results/**/*.xml'
-        }
-    }
 }

brutex-extended-tests/src/test/java/net/brutex/ai/nd4j/tests/LoadBackendTests.java

@@ -36,9 +36,9 @@ import static org.junit.jupiter.api.Assertions.assertTrue;
 public class LoadBackendTests {
 
     @Test
-    public void loadBackend() throws NoSuchFieldException, IllegalAccessException {
+    public void loadBackend() throws ClassNotFoundException, NoSuchFieldException, IllegalAccessException {
         // check if Nd4j is there
-        Logger.getLogger(LoadBackendTests.class.getName()).info("System java.library.path: " + System.getProperty("java.library.path"));
+        //Logger.getLogger(LoadBackendTests.class.getName()).info("System java.library.path: " + System.getProperty("java.library.path"));
         final Field sysPathsField = ClassLoader.class.getDeclaredField("sys_paths");
         sysPathsField.setAccessible(true);
         sysPathsField.set(null, null);

brutex-extended-tests/src/test/java/net/brutex/gan/App.java

@@ -16,7 +16,6 @@ import org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop;
 import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
 import org.deeplearning4j.nn.weights.WeightInit;
 import org.deeplearning4j.optimize.listeners.PerformanceListener;
-import org.junit.jupiter.api.Tag;
 import org.junit.jupiter.api.Test;
 import org.nd4j.linalg.activations.Activation;
 import org.nd4j.linalg.activations.impl.ActivationLReLU;
@@ -123,7 +122,7 @@ public class App {
         return conf;
     }
 
-    @Test @Tag("long-running")
+    @Test
     public void runTest() throws Exception {
         App.main(null);
     }

brutex-extended-tests/src/test/java/net/brutex/gan/App2.java

@@ -37,8 +37,6 @@ import org.datavec.image.loader.NativeImageLoader;
 import org.datavec.image.recordreader.ImageRecordReader;
 import org.datavec.image.transform.*;
 import org.deeplearning4j.datasets.datavec.RecordReaderDataSetIterator;
-import org.deeplearning4j.datasets.iterator.ExistingDataSetIterator;
-import org.deeplearning4j.datasets.iterator.INDArrayDataSetIterator;
 import org.deeplearning4j.datasets.iterator.impl.MnistDataSetIterator;
 import org.deeplearning4j.nn.conf.GradientNormalization;
 import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
@@ -46,29 +44,25 @@ import org.deeplearning4j.nn.conf.layers.*;
 import org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop;
 import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
 import org.deeplearning4j.optimize.listeners.PerformanceListener;
-import org.junit.jupiter.api.Tag;
 import org.junit.jupiter.api.Test;
-import org.nd4j.evaluation.classification.Evaluation;
 import org.nd4j.linalg.api.ndarray.INDArray;
 import org.nd4j.linalg.dataset.DataSet;
 import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
 import org.nd4j.linalg.factory.Nd4j;
 
-import static net.brutex.gan.App2Config.BATCHSIZE;
-
 @Slf4j
 public class App2 {
 
     final int INPUT = CHANNELS*DIMENSIONS*DIMENSIONS;
-
+    static final float COLORSPACE = 255f;
     static final int DIMENSIONS = 28;
     static final int CHANNELS = 1;
     final int ARRAY_SIZE_PER_SAMPLE = DIMENSIONS*DIMENSIONS*CHANNELS;
-
+    final int OUTPUT_PER_PANEL = 10;
 
     final boolean BIAS = true;
 
-
+    static final int BATCHSIZE=128;
 
     private JFrame frame2, frame;
     static final String OUTPUT_DIR = "d:/out/";
@@ -76,12 +70,12 @@ public class App2 {
     final static INDArray label_real = Nd4j.ones(BATCHSIZE, 1);
     final static INDArray label_fake = Nd4j.zeros(BATCHSIZE, 1);
 
-    @Test @Tag("long-running")
+    @Test
     void runTest() throws IOException {
         Nd4j.getMemoryManager().setAutoGcWindow(15 * 1000);
 
         MnistDataSetIterator mnistIter = new MnistDataSetIterator(20, 200);
-        FileSplit fileSplit = new FileSplit(new File("c:/users/brian/downloads/humans3"), NativeImageLoader.getALLOWED_FORMATS());
+        FileSplit fileSplit = new FileSplit(new File("c:/users/brian/downloads/humans2"), NativeImageLoader.getALLOWED_FORMATS());
         ImageTransform transform = new ColorConversionTransform(new Random(42), 7 );
         ImageTransform transform2 = new ShowImageTransform("Tester", 30);
         ImageTransform transform3 = new ResizeImageTransform(DIMENSIONS, DIMENSIONS);
@@ -134,94 +128,12 @@ public class App2 {
 
         log.info("Generator Summary:\n{}", gen.summary());
         log.info("GAN Summary:\n{}", gan.summary());
-        dis.addTrainingListeners(new PerformanceListener(3, true, "DIS"));
-        //gen.addTrainingListeners(new PerformanceListener(3, true, "GEN")); //is never trained separately from GAN
-        gan.addTrainingListeners(new PerformanceListener(3, true, "GAN"));
-/*
-    Thread vt =
-        new Thread(
-            new Runnable() {
-              @Override
-              public void run() {
-                while (true) {
-                  visualize(0, 0, gen);
-                    try {
-                        Thread.sleep(10000);
-                    } catch (InterruptedException e) {
-                        throw new RuntimeException(e);
-                    }
-                }
-              }
-            });
-        vt.start();
-*/
+        dis.addTrainingListeners(new PerformanceListener(10, true, "DIS"));
+        gen.addTrainingListeners(new PerformanceListener(10, true, "GEN"));
+        gan.addTrainingListeners(new PerformanceListener(10, true, "GAN"));
 
-        App2Display display = new App2Display();
-        //Repack training data with new fake/real label. Original MNist has 10 labels, one for each digit
-        DataSet data = null;
-        int j =0;
-        for(int i=0;i<App2Config.EPOCHS;i++) {
-            log.info("Epoch {}", i);
-            data = new DataSet(Nd4j.rand(BATCHSIZE, 784), label_fake);
-            while (trainData.hasNext()) {
-                j++;
-                INDArray real = trainData.next().getFeatures();
-                INDArray fakeIn = Nd4j.rand(BATCHSIZE, App2Config.INPUT);
-
-                INDArray fake = gan.activateSelectedLayers(0, gen.getLayers().length - 1,
-                        Nd4j.rand(BATCHSIZE, App2Config.INPUT));
-                //sigmoid output is -1 to 1
-                fake.addi(1f).divi(2f);
-
-                if (j % 50 == 1) {
-                  display.visualize(new INDArray[] {fake}, App2Config.OUTPUT_PER_PANEL, false);
-                  display.visualize(new INDArray[] {real}, App2Config.OUTPUT_PER_PANEL, true);
-                }
-
-
-                DataSet realSet = new DataSet(real, label_real);
-                DataSet fakeSet = new DataSet(fake, label_fake);
-
-                //start next round if there are not enough images left to have a full batchsize dataset
-                if(real.length() < ARRAY_SIZE_PER_SAMPLE*BATCHSIZE) {
-                    log.warn("Your total number of input images is not a multiple of {}, "
-                            + "thus skipping {} images to make it fit", BATCHSIZE, real.length()/ARRAY_SIZE_PER_SAMPLE);
-                    break;
-                }
-                //if(real.length()/BATCHSIZE!=784) break;
-                data = DataSet.merge(Arrays.asList(data, realSet, fakeSet));
-
-            }
-            //fit the discriminator
-            dis.fit(data);
-            dis.fit(data);
-            // Update the discriminator in the GAN network
-            updateGan(gen, dis, gan);
-
-            //reset the training data and fit the complete GAN
-            if (trainData.resetSupported()) {
-                trainData.reset();
-            } else {
-                log.error("Trainingdata {} does not support reset.", trainData.toString());
-            }
-            gan.fit(new DataSet(Nd4j.rand(BATCHSIZE, App2Config.INPUT), label_real));
-
-            if (trainData.resetSupported()) {
-                trainData.reset();
-            } else {
-                log.error("Trainingdata {} does not support reset.", trainData.toString());
-            }
-
-            log.info("Updated GAN's generator from gen.");
-            updateGen(gen, gan);
-            gen.save(new File("mnist-mlp-generator.dlj"));
-        }
-        //vt.stop();
-
-/*
-        int j;
-        for (int i = 0; i < App2Config.EPOCHS; i++) { //epoch
-            j=0;
+        int j = 0;
+        for (int i = 0; i < 51; i++) { //epoch
             while (trainData.hasNext()) {
                 j++;
                 DataSet next = trainData.next();
@@ -299,8 +211,6 @@ public class App2 {
             log.info("Updated GAN's generator from gen.");
             gen.save(new File("mnist-mlp-generator.dlj"));
         }
-        */
-
     }
 
 
@@ -313,11 +223,110 @@ public class App2 {
 
 
 
+    private static JFrame visualize(INDArray[] samples, int batchElements, JFrame frame, boolean isOrig) {
+        if (isOrig) {
+            frame.setTitle("Viz Original");
+        } else {
+            frame.setTitle("Generated");
+        }
+
+        frame.setDefaultCloseOperation(WindowConstants.DISPOSE_ON_CLOSE);
+        frame.setLayout(new BorderLayout());
+
+        JPanel panelx = new JPanel();
+
+        panelx.setLayout(new GridLayout(4, 4, 8, 8));
+        for (INDArray sample : samples) {
+            for(int i = 0; i<batchElements; i++) {
+                panelx.add(getImage(sample, i, isOrig));
+            }
+        }
+        frame.add(panelx, BorderLayout.CENTER);
+        frame.setVisible(true);
+
+        frame.revalidate();
+        frame.setMinimumSize(new Dimension(300, 20));
+        frame.pack();
+        return frame;
+    }
 
 
+    private static JLabel getImage(INDArray tensor, int batchElement, boolean isOrig) {
+        final BufferedImage bi;
+        if(CHANNELS >1) {
+            bi = new BufferedImage(DIMENSIONS, DIMENSIONS, BufferedImage.TYPE_INT_RGB); //need to change here based on channels
+        } else {
+            bi = new BufferedImage(DIMENSIONS, DIMENSIONS, BufferedImage.TYPE_BYTE_GRAY); //need to change here based on channels
+        }
+        final int imageSize = DIMENSIONS * DIMENSIONS;
+        final int offset = batchElement * imageSize;
+        int pxl = offset * CHANNELS; //where to start in the INDArray
 
+        //Image in NCHW - channels first format
+        for (int c = 0; c < CHANNELS; c++) { //step through the num channels for each pixel
+            for (int y = 0; y < DIMENSIONS; y++) { // step through the columns x
+                for (int x = 0; x < DIMENSIONS; x++) { //step through the rows y
+                    float f_pxl = tensor.getFloat(pxl) * COLORSPACE;
+                    if(isOrig) log.trace("'{}.' Image (x,y,c): ({}, {}, {}) with INDArray with index {} and value '{}'", batchElement, x, y, c, pxl, f_pxl);
+                    bi.getRaster().setSample(x, y, c, f_pxl);
+                    pxl++; //next item in INDArray
+                }
+            }
+        }
+        ImageIcon orig = new ImageIcon(bi);
+        Image imageScaled = orig.getImage().getScaledInstance((4 * DIMENSIONS), (4 * DIMENSIONS), Image.SCALE_DEFAULT);
+        ImageIcon scaled = new ImageIcon(imageScaled);
+        if(! isOrig)  saveImage(imageScaled, batchElement, isOrig);
+        return new JLabel(scaled);
 
+    }
 
+    private static void saveImage(Image image, int batchElement, boolean isOrig) {
+        String outputDirectory = OUTPUT_DIR; // Set the output directory where the images will be saved
+
+        try {
+            // Save the images to disk
+            saveImage(image, outputDirectory, UUID.randomUUID().toString()+".png");
+
+            log.debug("Images saved successfully.");
+        } catch (IOException e) {
+            log.error("Error saving the images: {}", e.getMessage());
+        }
+    }
+    private static void saveImage(Image image, String outputDirectory, String fileName) throws IOException {
+        File directory = new File(outputDirectory);
+        if (!directory.exists()) {
+            directory.mkdir();
+        }
+
+        File outputFile = new File(directory, fileName);
+        ImageIO.write(imageToBufferedImage(image), "png", outputFile);
+    }
+
+    public static BufferedImage imageToBufferedImage(Image image) {
+        if (image instanceof BufferedImage) {
+            return (BufferedImage) image;
+        }
+
+    // Create a buffered image with the same dimensions and transparency as the original image
+        BufferedImage bufferedImage;
+    if (CHANNELS > 1) {
+      bufferedImage =
+          new BufferedImage(
+              image.getWidth(null), image.getHeight(null), BufferedImage.TYPE_INT_ARGB);
+        } else {
+        bufferedImage =
+                new BufferedImage(
+                        image.getWidth(null), image.getHeight(null), BufferedImage.TYPE_BYTE_GRAY);
+        }
+
+        // Draw the original image onto the buffered image
+        Graphics2D g2d = bufferedImage.createGraphics();
+        g2d.drawImage(image, 0, 0, null);
+        g2d.dispose();
+
+        return bufferedImage;
+    }
     private static void updateGen(MultiLayerNetwork gen, MultiLayerNetwork gan) {
         for (int i = 0; i < gen.getLayers().length; i++) {
             gen.getLayer(i).setParams(gan.getLayer(i).getParams());
@@ -331,41 +340,4 @@ public class App2 {
         }
     }
 
-
-    @Test
-    void testDiskriminator() throws IOException {
-        MultiLayerNetwork net = new MultiLayerNetwork(App2Config.discriminator());
-        net.init();
-        net.addTrainingListeners(new PerformanceListener(10, true, "DIS"));
-        DataSetIterator trainData = new MnistDataSetIterator(BATCHSIZE, true, 42);
-
-        DataSet data = null;
-        for(int i=0;i<App2Config.EPOCHS;i++) {
-            log.info("Epoch {}", i);
-            data = new DataSet(Nd4j.rand(BATCHSIZE, 784), label_fake);
-            while (trainData.hasNext()) {
-                INDArray real = trainData.next().getFeatures();
-                long[] l = new long[]{BATCHSIZE, real.length() / BATCHSIZE};
-                INDArray fake = Nd4j.rand(l );
-
-                DataSet realSet = new DataSet(real, label_real);
-                DataSet fakeSet = new DataSet(fake, label_fake);
-                if(real.length()/BATCHSIZE!=784) break;
-                data = DataSet.merge(Arrays.asList(data, realSet, fakeSet));
-
-            }
-            net.fit(data);
-            trainData.reset();
-        }
-
-        long[] l = new long[]{BATCHSIZE, 784};
-        INDArray fake = Nd4j.rand(l );
-        DataSet fakeSet = new DataSet(fake, label_fake);
-        data = DataSet.merge(Arrays.asList(data, fakeSet));
-        ExistingDataSetIterator iter = new ExistingDataSetIterator(data);
-        Evaluation eval = net.evaluate(iter);
-        log.info( "\n" + eval.confusionMatrix());
-    }
-
-
 }

brutex-extended-tests/src/test/java/net/brutex/gan/App2Config.java

@@ -36,17 +36,10 @@ import org.nd4j.linalg.lossfunctions.LossFunctions;
 public class App2Config {
 
   public static final int INPUT = 100;
-  public static final int BATCHSIZE=150;
   public static final int X_DIM = 28;
-  public static final int Y_DIM = 28;
+  public static final int y_DIM = 28;
   public static final int CHANNELS = 1;
-  public static final int EPOCHS = 50;
   public static final IUpdater UPDATER = Adam.builder().learningRate(0.0002).beta1(0.5).build();
-  public static final IUpdater UPDATER_DIS = Adam.builder().learningRate(0.02).beta1(0.5).build();
-  public static final boolean SHOW_GENERATED = true;
-  public static final float COLORSPACE = 255f;
-
-  final static int OUTPUT_PER_PANEL = 10;
 
   static LayerConfiguration[] genLayerConfig() {
     return new LayerConfiguration[] {
@@ -165,7 +158,7 @@ public class App2Config {
                     .gradientNormalization(GradientNormalization.RenormalizeL2PerLayer)
                     .gradientNormalizationThreshold(100)
                     .seed(42)
-                    .updater(UPDATER_DIS)
+                    .updater(UPDATER)
                     .weightInit(WeightInit.XAVIER)
                     // .weightNoise(new WeightNoise(new NormalDistribution(0.5, 0.5)))
                     .weightNoise(null)

brutex-extended-tests/src/test/java/net/brutex/gan/App2Display.java

@@ -1,160 +0,0 @@
-/*
- *
- *    ******************************************************************************
- *    *
- *    * This program and the accompanying materials are made available under the
- *    * terms of the Apache License, Version 2.0 which is available at
- *    * https://www.apache.org/licenses/LICENSE-2.0.
- *    *
- *    *  See the NOTICE file distributed with this work for additional
- *    *  information regarding copyright ownership.
- *    * 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.
- *    *
- *    * SPDX-License-Identifier: Apache-2.0
- *    *****************************************************************************
- *
- */
-
-package net.brutex.gan;
-
-import com.google.inject.Singleton;
-import lombok.extern.slf4j.Slf4j;
-import org.nd4j.linalg.api.ndarray.INDArray;
-
-import javax.imageio.ImageIO;
-import javax.swing.*;
-import java.awt.*;
-import java.awt.color.ColorSpace;
-import java.awt.image.BufferedImage;
-import java.io.File;
-import java.io.IOException;
-import java.util.UUID;
-
-import static net.brutex.gan.App2.OUTPUT_DIR;
-import static net.brutex.gan.App2Config.*;
-@Slf4j
-@Singleton
-public class App2Display {
-
-    private final JFrame frame = new JFrame();
-  private final App2GUI display = new App2GUI();
-
-  private final JPanel real_panel;
-  private final JPanel fake_panel;
-
-
-    public App2Display() {
-        frame.setDefaultCloseOperation(WindowConstants.DISPOSE_ON_CLOSE);
-        frame.setContentPane(display.getOverall_panel());
-        frame.setMinimumSize(new Dimension(300, 20));
-        frame.pack();
-        frame.setVisible(true);
-        real_panel = display.getReal_panel();
-        fake_panel = display.getGen_panel();
-        real_panel.setLayout(new GridLayout(4, 4, 8, 8));
-        fake_panel.setLayout(new GridLayout(4, 4, 8, 8));
-    }
-
-    public void visualize(INDArray[] samples, int batchElements, boolean isOrig) {
-        for (INDArray sample : samples) {
-            for(int i = 0; i<batchElements; i++) {
-                final Image img = this.getImage(sample, i, isOrig);
-                final ImageIcon icon = new ImageIcon(img);
-                if(isOrig) {
-                    if(real_panel.getComponents().length>=OUTPUT_PER_PANEL) {
-                        real_panel.remove(0);
-                    }
-                    real_panel.add(new JLabel(icon));
-                } else {
-                        if(fake_panel.getComponents().length>=OUTPUT_PER_PANEL) {
-                            fake_panel.remove(0);
-                        }
-                    fake_panel.add(new JLabel(icon));
-                }
-            }
-        }
-        frame.pack();
-        frame.repaint();
-    }
-
-    public Image getImage(INDArray tensor, int batchElement, boolean isOrig) {
-        final BufferedImage bi;
-        if(CHANNELS >1) {
-            bi = new BufferedImage(X_DIM, Y_DIM, BufferedImage.TYPE_INT_RGB); //need to change here based on channels
-        } else {
-            bi = new BufferedImage(X_DIM, Y_DIM, BufferedImage.TYPE_BYTE_GRAY); //need to change here based on channels
-        }
-        final int imageSize = X_DIM * Y_DIM;
-        final int offset = batchElement * imageSize;
-        int pxl = offset * CHANNELS; //where to start in the INDArray
-
-        //Image in NCHW - channels first format
-        for (int c = 0; c < CHANNELS; c++) { //step through the num channels for each pixel
-            for (int y = 0; y < X_DIM; y++) { // step through the columns x
-                for (int x = 0; x < Y_DIM; x++) { //step through the rows y
-                    float f_pxl = tensor.getFloat(pxl) * COLORSPACE;
-                    if(isOrig) log.trace("'{}.'{} Image (x,y,c): ({}, {}, {}) with INDArray with index {} and value '{}'", batchElement, isOrig ? "Real" : "Fake", x, y, c, pxl, f_pxl);
-                    bi.getRaster().setSample(x, y, c, f_pxl);
-                    pxl++; //next item in INDArray
-                }
-            }
-        }
-        ImageIcon orig = new ImageIcon(bi);
-        Image imageScaled = orig.getImage().getScaledInstance((4 * X_DIM), (4 * Y_DIM), Image.SCALE_DEFAULT);
-        ImageIcon scaled = new ImageIcon(imageScaled);
-        //if(! isOrig)  saveImage(imageScaled, batchElement, isOrig);
-        return imageScaled;
-
-    }
-
-    private static void saveImage(Image image, int batchElement, boolean isOrig) {
-        String outputDirectory = OUTPUT_DIR; // Set the output directory where the images will be saved
-
-        try {
-            // Save the images to disk
-            saveImage(image, outputDirectory, UUID.randomUUID().toString()+".png");
-
-            log.debug("Images saved successfully.");
-        } catch (IOException e) {
-            log.error("Error saving the images: {}", e.getMessage());
-        }
-    }
-    private static void saveImage(Image image, String outputDirectory, String fileName) throws IOException {
-        File directory = new File(outputDirectory);
-        if (!directory.exists()) {
-            directory.mkdir();
-        }
-
-        File outputFile = new File(directory, fileName);
-        ImageIO.write(imageToBufferedImage(image), "png", outputFile);
-    }
-
-    public static BufferedImage imageToBufferedImage(Image image) {
-        if (image instanceof BufferedImage) {
-            return (BufferedImage) image;
-        }
-
-        // Create a buffered image with the same dimensions and transparency as the original image
-        BufferedImage bufferedImage;
-        if (CHANNELS > 1) {
-            bufferedImage =
-                    new BufferedImage(
-                            image.getWidth(null), image.getHeight(null), BufferedImage.TYPE_INT_ARGB);
-        } else {
-            bufferedImage =
-                    new BufferedImage(
-                            image.getWidth(null), image.getHeight(null), BufferedImage.TYPE_BYTE_GRAY);
-        }
-
-        // Draw the original image onto the buffered image
-        Graphics2D g2d = bufferedImage.createGraphics();
-        g2d.drawImage(image, 0, 0, null);
-        g2d.dispose();
-
-        return bufferedImage;
-    }
-}

brutex-extended-tests/src/test/java/net/brutex/gan/App2GUI.java

@@ -1,61 +0,0 @@
-package net.brutex.gan;
-
-import javax.swing.JPanel;
-import javax.swing.JSplitPane;
-import javax.swing.JLabel;
-import java.awt.BorderLayout;
-
-public class App2GUI extends JPanel {
-
-	/**
-	 * 
-	 */
-	private static final long serialVersionUID = 1L;
-	private JPanel overall_panel;
-	private JPanel real_panel;
-	private JPanel gen_panel;
-
-	/**
-	 * Create the panel.
-	 */
-	public App2GUI() {
-		
-		overall_panel = new JPanel();
-		add(overall_panel);
-		
-		JSplitPane splitPane = new JSplitPane();
-		overall_panel.add(splitPane);
-		
-		JPanel p1 = new JPanel();
-		splitPane.setLeftComponent(p1);
-		p1.setLayout(new BorderLayout(0, 0));
-		
-		JLabel lblNewLabel = new JLabel("Generator");
-		p1.add(lblNewLabel, BorderLayout.NORTH);
-		
-		gen_panel = new JPanel();
-		p1.add(gen_panel, BorderLayout.SOUTH);
-		
-		JPanel p2 = new JPanel();
-		splitPane.setRightComponent(p2);
-		p2.setLayout(new BorderLayout(0, 0));
-		
-		JLabel lblNewLabel_1 = new JLabel("Real");
-		p2.add(lblNewLabel_1, BorderLayout.NORTH);
-		
-		real_panel = new JPanel();
-		p2.add(real_panel, BorderLayout.SOUTH);
-
-	}
-
-
-	public JPanel getOverall_panel() {
-		return overall_panel;
-	}
-	public JPanel getReal_panel() {
-		return real_panel;
-	}
-	public JPanel getGen_panel() {
-		return gen_panel;
-	}
-}

brutex-extended-tests/src/test/java/net/brutex/gan/MnistSimpleGAN.java

@@ -31,7 +31,6 @@ import org.deeplearning4j.nn.conf.layers.DropoutLayer;
 import org.deeplearning4j.nn.conf.layers.OutputLayer;
 import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
 import org.deeplearning4j.nn.weights.WeightInit;
-import org.junit.jupiter.api.Tag;
 import org.junit.jupiter.api.Test;
 import org.nd4j.linalg.activations.Activation;
 import org.nd4j.linalg.activations.impl.ActivationLReLU;
@@ -101,7 +100,7 @@ public class MnistSimpleGAN {
 
     return new MultiLayerNetwork(discConf);
   }
-  @Test @Tag("long-running")
+  @Test
   public void runTest() throws Exception {
     main(null);
   }

cavis-common-platform/build.gradle

@@ -20,7 +20,7 @@ ext {
 
     def javacv = [version:"1.5.7"]
     def opencv = [version: "4.5.5"]
-    def leptonica = [version: "1.83.0"] //fix, only in javacpp 1.5.9
+    def leptonica = [version: "1.82.0"]
     def junit = [version: "5.9.1"]
 
     def flatbuffers = [version: "1.10.0"]
@@ -118,8 +118,7 @@ dependencies {
         api "org.bytedeco:javacv:${javacv.version}"
         api "org.bytedeco:opencv:${opencv.version}-${javacpp.presetsVersion}"
         api "org.bytedeco:openblas:${openblas.version}-${javacpp.presetsVersion}"
-        api "org.bytedeco:leptonica-platform:${leptonica.version}-1.5.9"
-        api "org.bytedeco:leptonica:${leptonica.version}-1.5.9"
+        api "org.bytedeco:leptonica-platform:${leptonica.version}-${javacpp.presetsVersion}"
         api "org.bytedeco:hdf5-platform:${hdf5.version}-${javacpp.presetsVersion}"
         api "org.bytedeco:hdf5:${hdf5.version}-${javacpp.presetsVersion}"
         api "org.bytedeco:hdf5:${hdf5.version}-${javacpp.presetsVersion}:${javacppPlatform}"
@@ -130,7 +129,6 @@ dependencies {
         api "org.bytedeco:cuda:${cuda.version}-${cudnn.version}-${javacpp.presetsVersion}"
         api "org.bytedeco:cuda-platform-redist:${cuda.version}-${cudnn.version}-${javacpp.presetsVersion}"
         api "org.bytedeco:mkl-dnn:0.21.5-${javacpp.presetsVersion}"
-        api "org.bytedeco:mkl:2022.0-${javacpp.presetsVersion}"
         api "org.bytedeco:tensorflow:${tensorflow.version}-${javacpp.presetsVersion}"
         api "org.bytedeco:cpython:${cpython.version}-${javacpp.presetsVersion}:${javacppPlatform}"
         api "org.bytedeco:numpy:${numpy.version}-${javacpp.presetsVersion}:${javacppPlatform}"

cavis-datavec/cavis-datavec-data/cavis-datavec-data-image/build.gradle

@@ -28,8 +28,7 @@ dependencies {
     implementation "org.bytedeco:javacv"
     implementation "org.bytedeco:opencv"
     implementation group: "org.bytedeco", name: "opencv", classifier: buildTarget
-    //implementation "org.bytedeco:leptonica-platform"
-    implementation group: "org.bytedeco", name: "leptonica", classifier: buildTarget
+    implementation "org.bytedeco:leptonica-platform"
     implementation "org.bytedeco:hdf5-platform"
 
     implementation "commons-io:commons-io"

cavis-datavec/cavis-datavec-data/cavis-datavec-data-image/src/main/java/org/datavec/image/loader/NativeImageLoader.java

@@ -46,7 +46,7 @@ import java.nio.ByteOrder;
 import org.bytedeco.leptonica.*;
 import org.bytedeco.opencv.opencv_core.*;
 
-import static org.bytedeco.leptonica.global.leptonica.*;
+import static org.bytedeco.leptonica.global.lept.*;
 import static org.bytedeco.opencv.global.opencv_core.*;
 import static org.bytedeco.opencv.global.opencv_imgcodecs.*;
 import static org.bytedeco.opencv.global.opencv_imgproc.*;

cavis-datavec/cavis-datavec-data/cavis-datavec-data-image/src/test/java/org/datavec/image/loader/TestNativeImageLoader.java

@@ -52,9 +52,10 @@ import java.io.InputStream;
 import java.lang.reflect.Field;
 import java.nio.file.Path;
 import java.util.Random;
+import java.util.stream.IntStream;
 import java.util.stream.Stream;
 
-import static org.bytedeco.leptonica.global.leptonica.*;
+import static org.bytedeco.leptonica.global.lept.*;
 import static org.bytedeco.opencv.global.opencv_core.*;
 import static org.junit.jupiter.api.Assertions.*;
 

cavis-dnn/cavis-dnn-nn/src/test/java/net/brutex/ai/dnn/api/dnnTest.java

@@ -78,7 +78,7 @@ class dnnTest {
      * DenseLayer.Builder().nIn(X_DIM*Y_DIM).nOut(X_DIM*Y_DIM*CHANNELS).activation(Activation.TANH)
      */
     NeuralNetConfiguration network =
-        NN.nn()
+        NN.net()
             .seed(42)
             .updater(Adam.builder().learningRate(0.0002).beta1(0.5).build())
             .gradientNormalization(GradientNormalization.RenormalizeL2PerLayer)

cavis-dnn/cavis-dnn-spark/cavis-dnn-spark-core/build.gradle

@@ -31,7 +31,7 @@ dependencies {
     implementation projects.cavisDnn.cavisDnnData.cavisDnnDataDatasets
     implementation projects.cavisDnn.cavisDnnData.cavisDnnDataDatavecIterators
     implementation projects.cavisDnn.cavisDnnData.cavisDnnDataUtilityIterators
-    implementation "org.apache.hadoop:hadoop-common:3.2.4"
+    implementation "org.apache.hadoop:hadoop-common:3.2.0"
     implementation "com.fasterxml.jackson.dataformat:jackson-dataformat-yaml"
     implementation projects.cavisDatavec.cavisDatavecApi
     implementation projects.cavisDatavec.cavisDatavecSpark.cavisDatavecSparkCore

cavis-native/cavis-native-lib/build.gradle

@@ -52,8 +52,8 @@ buildscript {
         classpath platform(project(":cavis-common-platform"))
         classpath group: "org.bytedeco", name: "openblas"
         classpath group: "org.bytedeco", name: "openblas", classifier: "${javacppPlatform}"
-        classpath group: "org.bytedeco", name:"mkl"
-        classpath group: "org.bytedeco", name:"mkl", classifier: "${javacppPlatform}"
+        classpath group: "org.bytedeco", name:"mkl-dnn"
+        classpath group: "org.bytedeco", name:"mkl-dnn", classifier: "${javacppPlatform}"
         classpath group: "org.bytedeco", name: "javacpp"
         classpath group: "org.bytedeco", name: "javacpp", classifier: "${javacppPlatform}"
     }
@@ -64,7 +64,7 @@ buildscript {
 
 plugins {
     id 'java-library'
-    id 'org.bytedeco.gradle-javacpp-build' version "1.5.7"
+    id 'org.bytedeco.gradle-javacpp-build' version "1.5.9"
     id 'maven-publish'
     id 'signing'
 }
@@ -336,12 +336,11 @@ chipList.each { thisChip ->
                 && !project.getProperty("skip-native").equals("true") && !VISUAL_STUDIO_INSTALL_DIR.isEmpty()) {
             def proc = ["cmd.exe", "/c", "${VISUAL_STUDIO_VCVARS_CMD} > nul && where.exe cl.exe"].execute()
             def outp = proc.text
-            def cl = "\"" + outp.replace("\\", "\\\\").trim() + "\""
+            def cl = outp.replace("\\", "\\\\").trim()
             def currentCompiler = ""
             doFirst{
                 currentCompiler = System.getProperty("org.bytedeco.javacpp.platform.compiler")
                 System.setProperty("org.bytedeco.javacpp.platform.compiler", cl)
-                System.setProperty("platform.compiler.cpp11", cl)
                 logger.quiet("Task ${thisTask.name} overrides compiler '${currentCompiler}' with '${cl}'.")
             }
             doLast {

cavis-native/cavis-native-lib/src/main/cpp/blas/CMakeLists.txt

@@ -102,18 +102,16 @@ ENDIF()
 
 IF(${SD_EXTENSION} MATCHES "avx2")
     message("Extension AVX2 enabled.")
-    #-mmmx -msse -msse2 -msse3 -msse4.1 -msse4.2 -mavx -mavx2 -mfma -mf16c -mprefetchwt1
-    set(ARCH_TUNE "${ARCH_TUNE} -DSD_F16C=true -DF_AVX2=true")
+    set(ARCH_TUNE "${ARCH_TUNE} -mmmx -msse -msse2 -msse3 -msse4.1 -msse4.2 -mavx -mavx2 -mfma -mf16c -mprefetchwt1 -DSD_F16C=true -DF_AVX2=true")
 ELSEIF(${SD_EXTENSION} MATCHES "avx512")
         message("Extension AVX512 enabled.")
         # we need to set flag here, that we can use hardware f16 conversion + tell that cpu features should be tracked
-        #-mmmx -msse -msse2 -msse3 -msse4.1 -msse4.2 -mavx -mavx2 -mfma -mf16c -mavx512f -mavx512vl -mavx512bw -mavx512dq  -mavx512cd -mbmi -mbmi2 -mprefetchwt1 -mclflushopt -mxsavec -mxsaves
-        set(ARCH_TUNE "${ARCH_TUNE} -DSD_F16C=true -DF_AVX512=true")
+        set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -mmmx -msse -msse2 -msse3 -msse4.1 -msse4.2 -mavx -mavx2 -mfma -mf16c -mavx512f -mavx512vl -mavx512bw -mavx512dq  -mavx512cd -mbmi -mbmi2 -mprefetchwt1 -mclflushopt -mxsavec -mxsaves -DSD_F16C=true -DF_AVX512=true")
 ENDIF()
 
 if (NOT WIN32)
         # we don't want this definition for msvc
-        set(ARCH_TUNE "${ARCH_TUNE} -march=${SD_ARCH} -mtune=${ARCH_TYPE}")
+        set(ARCH_TUNE "-march=${SD_ARCH} -mtune=${ARCH_TYPE}")
 endif()
 
 if ("${CMAKE_CXX_COMPILER_ID}" STREQUAL "AppleClang" AND SD_X86_BUILD)

cavis-nd4j/cavis-nd4j-tensorflow/build.gradle

@@ -28,7 +28,7 @@ dependencies {
     implementation group: "org.bytedeco", name: "tensorflow"
     testRuntimeOnly group: "org.bytedeco", name: "tensorflow", classifier: buildTarget
     if(buildTarget.contains("windows") || buildTarget.contains("linux")) {
-        testRuntimeOnly group: "org.bytedeco", name: 'tensorflow', classifier: "${buildTarget}-gpu", version: ''
+        testRuntimeOnly group: "org.bytedeco", name: "tensorflow", classifier: "${buildTarget}-gpu"
     }
     implementation "commons-io:commons-io"
     implementation "com.google.code.gson:gson"

createTestBackends.gradle

@@ -87,7 +87,7 @@ ext {
             cudaTestRuntime group: "org.bytedeco", name: "openblas", classifier: buildTarget
             cudaTestRuntime group: "org.bytedeco", name: "cuda"
             cudaTestRuntime group: "org.bytedeco", name: "cuda", classifier: buildTarget
-            //cudaTestRuntime group: "org.bytedeco", name: "cuda", classifier: "${buildTarget}-redist"
+            cudaTestRuntime group: "org.bytedeco", name: "cuda", classifier: "${buildTarget}-redist"
             cudaTestRuntime (project( path: ":cavis-native:cavis-native-lib", configuration: "cudaSupportRuntimeElements"))
             /*
             cudaTestRuntime(project(":cavis-native:cavis-native-lib")) {