Playing with GAN

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

brutex-extended-tests/build.gradle

@@ -34,6 +34,8 @@ ext {
 }
 
 dependencies {
+    implementation platform(projects.cavisCommonPlatform)
+
     implementation "com.fasterxml.jackson.core:jackson-databind"
     implementation "com.google.guava:guava"
     implementation projects.cavisDnn.cavisDnnCore
@@ -52,6 +54,16 @@ dependencies {
     testImplementation "org.apache.spark:spark-sql_${scalaVersion}"
     testCompileOnly "org.scala-lang:scala-library"
 
+    //Rest Client
+    // define any required OkHttp artifacts without version
+    implementation("com.squareup.okhttp3:okhttp")
+    implementation("com.squareup.okhttp3:logging-interceptor")
+
+
+    implementation "org.bytedeco:javacv"
+    implementation "org.bytedeco:opencv"
+    implementation group: "org.bytedeco", name: "opencv", classifier: buildTarget
+
     implementation "it.unimi.dsi:fastutil-core:8.5.8"
 
     implementation projects.cavisDnn.cavisDnnSpark.cavisDnnSparkCore

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

@@ -21,49 +21,90 @@
 
 package net.brutex.gan;
 
+import java.util.Random;
+import javax.ws.rs.client.ClientBuilder;
+import lombok.extern.slf4j.Slf4j;
+import okhttp3.OkHttpClient;
+import okhttp3.Request;
+import okhttp3.Response;
 import org.apache.commons.lang3.ArrayUtils;
+import org.datavec.api.Writable;
+import org.datavec.api.records.reader.RecordReader;
+import org.datavec.api.split.FileSplit;
+import org.datavec.image.loader.NativeImageLoader;
+import org.datavec.image.recordreader.ImageRecordReader;
+import org.datavec.image.transform.ColorConversionTransform;
+import org.datavec.image.transform.ImageTransform;
+import org.datavec.image.transform.PipelineImageTransform;
+import org.datavec.image.transform.ResizeImageTransform;
+import org.datavec.image.transform.ScaleImageTransform;
+import org.datavec.image.transform.ShowImageTransform;
+import org.deeplearning4j.datasets.datavec.RecordReaderDataSetIterator;
 import org.deeplearning4j.datasets.iterator.impl.MnistDataSetIterator;
 import org.deeplearning4j.nn.conf.GradientNormalization;
 import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
 import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
+import org.deeplearning4j.nn.conf.inputs.InputType;
 import org.deeplearning4j.nn.conf.layers.*;
 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.deeplearning4j.optimize.listeners.ScoreToChartListener;
+import org.glassfish.jersey.client.JerseyClient;
+import org.glassfish.jersey.client.JerseyClientBuilder;
 import org.junit.jupiter.api.Test;
 import org.nd4j.linalg.activations.Activation;
 import org.nd4j.linalg.activations.impl.ActivationLReLU;
 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.dataset.api.preprocessor.ImagePreProcessingScaler;
+import org.nd4j.linalg.dataset.api.preprocessor.NormalizerMinMaxScaler;
 import org.nd4j.linalg.factory.Nd4j;
 import org.nd4j.linalg.learning.config.Adam;
 import org.nd4j.linalg.learning.config.IUpdater;
 import org.nd4j.linalg.lossfunctions.LossFunctions;
 
+
 import javax.swing.*;
 import java.awt.*;
 import java.awt.image.BufferedImage;
 import java.io.File;
 import java.util.Arrays;
+import org.nd4j.linalg.lossfunctions.LossFunctions.LossFunction;
 
+@Slf4j
 public class App {
-  private static final double LEARNING_RATE = 0.0002;
+  private static final double LEARNING_RATE = 0.000002;
   private static final double GRADIENT_THRESHOLD = 100.0;
+
+  private static final int X_DIM = 28;
+  private static final int Y_DIM = 28;
+  private static final int CHANNELS = 1;
+  private static final int batchSize = 9;
+  private static final int INPUT = 128;
+
+  private static final int OUTPUT_PER_PANEL = 4;
+
+  private static final int ARRAY_SIZE_PER_SAMPLE = X_DIM*Y_DIM*CHANNELS;
   private static final IUpdater UPDATER = Adam.builder().learningRate(LEARNING_RATE).beta1(0.5).build();
 
   private  static JFrame frame;
+  private static  JFrame frame2;
   private static JPanel panel;
+  private static JPanel panel2;
 
   private static Layer[] genLayers() {
     return new Layer[] {
-        new DenseLayer.Builder().nIn(100).nOut(256).weightInit(WeightInit.NORMAL).build(),
+        new DenseLayer.Builder().nIn(INPUT).nOut(X_DIM*Y_DIM*CHANNELS).weightInit(WeightInit.NORMAL).build(),
         new ActivationLayer.Builder(new ActivationLReLU(0.2)).build(),
-        new DenseLayer.Builder().nIn(256).nOut(512).build(),
+        new DenseLayer.Builder().nIn(X_DIM*Y_DIM*CHANNELS).nOut(X_DIM*Y_DIM).build(),
         new ActivationLayer.Builder(new ActivationLReLU(0.2)).build(),
-        new DenseLayer.Builder().nIn(512).nOut(1024).build(),
+        new DenseLayer.Builder().nIn(X_DIM*Y_DIM).nOut(X_DIM*Y_DIM).build(),
         new ActivationLayer.Builder(new ActivationLReLU(0.2)).build(),
-        new DenseLayer.Builder().nIn(1024).nOut(784).activation(Activation.TANH).build()
+        new DenseLayer.Builder().nIn(X_DIM*Y_DIM).nOut(X_DIM*Y_DIM*CHANNELS).activation(Activation.TANH)
+        .build()
     };
   }
 
@@ -81,6 +122,7 @@ public class App {
         .weightInit(WeightInit.XAVIER)
         .activation(Activation.IDENTITY)
         .list(genLayers())
+        .setInputType(InputType.convolutional(X_DIM, Y_DIM, CHANNELS))
         .build();
 
     return conf;
@@ -88,16 +130,19 @@ public class App {
 
   private static Layer[] disLayers() {
     return new Layer[]{
-        new DenseLayer.Builder().nIn(784).nOut(1024).build(),
+        new DenseLayer.Builder().nOut(X_DIM*Y_DIM*CHANNELS*2).build(), //input is set by setInputType on the network
         new ActivationLayer.Builder(new ActivationLReLU(0.2)).build(),
         new DropoutLayer.Builder(1 - 0.5).build(),
-        new DenseLayer.Builder().nIn(1024).nOut(512).build(),
+        new DenseLayer.Builder().nIn(X_DIM * Y_DIM*CHANNELS*2).nOut(X_DIM*Y_DIM*CHANNELS*4).build(), //HxBxC
         new ActivationLayer.Builder(new ActivationLReLU(0.2)).build(),
         new DropoutLayer.Builder(1 - 0.5).build(),
-        new DenseLayer.Builder().nIn(512).nOut(256).build(),
+        new DenseLayer.Builder().nIn(X_DIM*Y_DIM*CHANNELS*4).nOut(X_DIM*Y_DIM*CHANNELS).build(),
         new ActivationLayer.Builder(new ActivationLReLU(0.2)).build(),
         new DropoutLayer.Builder(1 - 0.5).build(),
-        new OutputLayer.Builder(LossFunctions.LossFunction.XENT).nIn(256).nOut(1).activation(Activation.SIGMOID).build()
+        new DenseLayer.Builder().nIn(X_DIM*Y_DIM*CHANNELS).nOut(X_DIM*Y_DIM).build(),
+        new ActivationLayer.Builder(new ActivationLReLU(0.2)).build(),
+        new DropoutLayer.Builder(1 - 0.5).build(),
+        new OutputLayer.Builder(LossFunction.XENT).nIn(X_DIM*Y_DIM).nOut(1).activation(Activation.SIGMOID).build()
     };
   }
 
@@ -110,6 +155,7 @@ public class App {
         .weightInit(WeightInit.XAVIER)
         .activation(Activation.IDENTITY)
         .list(disLayers())
+        .setInputType(InputType.convolutional(X_DIM, Y_DIM, CHANNELS))
         .build();
 
     return conf;
@@ -135,6 +181,7 @@ public class App {
         .weightInit(WeightInit.XAVIER)
         .activation(Activation.IDENTITY)
         .list(layers)
+        .setInputType(InputType.convolutional(X_DIM, Y_DIM, CHANNELS))
         .build();
 
     return conf;
@@ -149,7 +196,25 @@ public class App {
   public static void main(String... args) throws Exception {
     Nd4j.getMemoryManager().setAutoGcWindow(15 * 1000);
 
-    MnistDataSetIterator trainData = new MnistDataSetIterator(128, true, 42);
+//    MnistDataSetIterator trainData = new MnistDataSetIterator(128, true, 45);
+  //  FileSplit fileSplit = new FileSplit(new File("c:/users/brian/downloads/flowers"), NativeImageLoader.getALLOWED_FORMATS());
+    FileSplit fileSplit = new FileSplit(new File("c:/users/brian/downloads/humans"), NativeImageLoader.getALLOWED_FORMATS());
+
+
+    ImageTransform transform = new ColorConversionTransform(new Random(42), 7 );
+
+    ImageTransform transform2 = new ShowImageTransform("Tester", 30);
+    ImageTransform transform3 = new ResizeImageTransform(X_DIM, Y_DIM);
+
+    ImageTransform tr = new PipelineImageTransform.Builder()
+        .addImageTransform(transform) //convert to GREY SCALE
+        .addImageTransform(transform3)
+        //.addImageTransform(transform2)
+        .build();
+
+    ImageRecordReader imageRecordReader = new ImageRecordReader(X_DIM, Y_DIM, CHANNELS);
+    imageRecordReader.initialize(fileSplit, tr);
+    DataSetIterator trainData = new RecordReaderDataSetIterator(imageRecordReader, batchSize );
 
     MultiLayerNetwork gen = new MultiLayerNetwork(generator());
     MultiLayerNetwork dis = new MultiLayerNetwork(discriminator());
@@ -160,27 +225,50 @@ public class App {
 
     copyParams(gen, dis, gan);
 
-    gen.setListeners(new PerformanceListener(10, true));
+    //gen.setListeners(new PerformanceListener(10, true));
-    dis.setListeners(new PerformanceListener(10, true));
+    //dis.setListeners(new PerformanceListener(10, true));
-    gan.setListeners(new PerformanceListener(10, true));
+    //gan.setListeners(new PerformanceListener(10, true));
+    gan.setListeners(new ScoreToChartListener("gan"));
+    //dis.setListeners(new ScoreToChartListener("dis"));
 
-    trainData.reset();
+    gan.fit(Nd4j.rand(batchSize, CHANNELS, X_DIM, Y_DIM), Nd4j.zeros(batchSize, 1));
+
+    //gan.fit(new DataSet(trainData.next().getFeatures(), Nd4j.zeros(batchSize, 1)));
+    //trainData.reset();
 
     int j = 0;
-    for (int i = 0; i < 20; i++) {
+    for (int i = 0; i < 201; i++) { //epoch
       while (trainData.hasNext()) {
         j++;
 
+        DataSet next = trainData.next();
         // generate data
-        INDArray real = trainData.next().getFeatures().muli(2).subi(1);
+        INDArray real = next.getFeatures();//.div(255f);
-        int batchSize = (int) real.shape()[0];
 
-        INDArray fakeIn = Nd4j.rand(batchSize, 100);
+        //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(i%20 == 0) {
+         // frame2 = visualize(new INDArray[]{real}, batchSize,
+         //     frame2 == null ? new JFrame() : frame2, true); //real has batchsize number of images
+        }
+       real.divi(255f);
+
+//        int batchSize = (int) real.shape()[0];
+
+        INDArray fakeIn = Nd4j.rand(batchSize, CHANNELS, X_DIM, Y_DIM);
         INDArray fake = gan.activateSelectedLayers(0, gen.getLayers().length - 1, fakeIn);
+        fake = fake.reshape(batchSize, CHANNELS, X_DIM, Y_DIM);
 
+        //log.info("real has {} items.", real.length());
         DataSet realSet = new DataSet(real, Nd4j.zeros(batchSize, 1));
         DataSet fakeSet = new DataSet(fake, Nd4j.ones(batchSize, 1));
 
+
         DataSet data = DataSet.merge(Arrays.asList(realSet, fakeSet));
 
         dis.fit(data);
@@ -189,21 +277,29 @@ public class App {
         // Update the discriminator in the GAN network
         updateGan(gen, dis, gan);
 
-        gan.fit(new DataSet(Nd4j.rand(batchSize, 100), Nd4j.zeros(batchSize, 1)));
+        //gan.fit(new DataSet(Nd4j.rand(batchSize, INPUT), Nd4j.zeros(batchSize, 1)));
+        gan.fit(new DataSet(Nd4j.rand(batchSize, CHANNELS, X_DIM, Y_DIM), Nd4j.zeros(batchSize, 1)));
 
 
         if (j % 10 == 1) {
           System.out.println("Iteration " + j + " Visualizing...");
-          INDArray[] samples = new INDArray[9];
+          INDArray[] samples = batchSize > OUTPUT_PER_PANEL ? new INDArray[OUTPUT_PER_PANEL] : new INDArray[batchSize];
-          DataSet fakeSet2 = new DataSet(fakeIn, Nd4j.ones(batchSize, 1));
 
-          for (int k = 0; k < 9; k++) {
+
+          for (int k = 0; k < samples.length; k++) {
+            //INDArray input = fakeSet2.get(k).getFeatures();
+            DataSet fakeSet2 = new DataSet(fakeIn, Nd4j.ones(batchSize, 1));
             INDArray input = fakeSet2.get(k).getFeatures();
+            input = input.reshape(1,CHANNELS, X_DIM, Y_DIM); //batch size will be 1 here
+
             //samples[k] = gen.output(input, false);
             samples[k] = gan.activateSelectedLayers(0, gen.getLayers().length - 1, input);
+            samples[k] = samples[k].reshape(1, CHANNELS, X_DIM, Y_DIM);
+            //samples[k] =
+            samples[k].addi(1f).divi(2f).muli(255f);
 
           }
-          visualize(samples);
+          frame = visualize(samples, 1, frame == null ? new JFrame() : frame, false); //each samples only has 1 image, thus batchElements=1
         }
       }
       trainData.reset();
@@ -239,41 +335,57 @@ public class App {
     }
   }
 
-  private static void visualize(INDArray[] samples) {
+  private static JFrame visualize(INDArray[] samples, int batchElements, JFrame frame, boolean isOrig) {
-    if (frame == null) {
+    if (isOrig) {
-      frame = new JFrame();
+      frame.setTitle("Viz Original");
-      frame.setTitle("Viz");
+    } else {
+      frame.setTitle("Generated");
+    }
+
     frame.setDefaultCloseOperation(WindowConstants.DISPOSE_ON_CLOSE);
     frame.setLayout(new BorderLayout());
 
-      panel = new JPanel();
+    JPanel panelx = new JPanel();
-
-      panel.setLayout(new GridLayout(samples.length / 3, 1, 8, 8));
-      frame.add(panel, BorderLayout.CENTER);
-      frame.setVisible(true);
-    }
-
-    panel.removeAll();
 
+    panelx.setLayout(new GridLayout(4, 4, 8, 8));
     for (INDArray sample : samples) {
-      panel.add(getImage(sample));
+      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) {
+  private static JLabel getImage(INDArray tensor, int batchElement, boolean isOrig) {
-    BufferedImage bi = new BufferedImage(28, 28, BufferedImage.TYPE_BYTE_GRAY);
+    final BufferedImage bi = new BufferedImage(X_DIM, Y_DIM, BufferedImage.TYPE_BYTE_GRAY);
-    for (int i = 0; i < 784; i++) {
+    final int imageSize = X_DIM * Y_DIM;
-      int pixel = (int)(((tensor.getDouble(i) + 1) * 2) * 255);
+    final int offset = batchElement * imageSize;
-      bi.getRaster().setSample(i % 28, i / 28, 0, pixel);
+    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 < Y_DIM; y++) { // step through the columns x
+        for (int x = 0; x < X_DIM; x++) { //step through the rows y
+          if(isOrig) log.trace("'{}.' Image (x,y,c): ({}, {}, {}) with INDArray with index {} and value '{}'", batchElement, x, y, c, pxl, tensor.getFloat(pxl));
+          bi.getRaster().setSample(x, y, c, tensor.getFloat(pxl));
+          pxl++; //next item in INDArray
         }
+      }
+    }
+
     ImageIcon orig = new ImageIcon(bi);
-    Image imageScaled = orig.getImage().getScaledInstance((8 * 28), (8 * 28), Image.SCALE_REPLICATE);
+
+    Image imageScaled = orig.getImage().getScaledInstance((4 * X_DIM), (4 * Y_DIM), Image.SCALE_DEFAULT);
 
     ImageIcon scaled = new ImageIcon(imageScaled);
 
     return new JLabel(scaled);
+
   }
 }

brutex-extended-tests/src/test/resources/simplelogger.properties

@@ -0,0 +1,49 @@
+#
+#
+#    ******************************************************************************
+#    *
+#    * 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
+#    *****************************************************************************
+#
+#
+
+# SLF4J's SimpleLogger configuration file
+# Simple implementation of Logger that sends all enabled log messages, for all defined loggers, to System.err.
+
+# Default logging detail level for all instances of SimpleLogger.
+# Must be one of ("trace", "debug", "info", "warn", or "error").
+# If not specified, defaults to "info".
+org.slf4j.simpleLogger.defaultLogLevel=trace
+
+# Logging detail level for a SimpleLogger instance named "xxxxx".
+# Must be one of ("trace", "debug", "info", "warn", or "error").
+# If not specified, the default logging detail level is used.
+#org.slf4j.simpleLogger.log.xxxxx=
+#org.slf4j.simpleLogger.log.net.brutex.cavis.backend.cavisrest.JWTAuthenticationFilter=warn
+
+# Set to true if you want the current date and time to be included in output messages.
+# Default is false, and will output the number of milliseconds elapsed since startup.
+#org.slf4j.simpleLogger.showDateTime=false
+
+# The date and time format to be used in the output messages.
+# The pattern describing the date and time format is the same that is used in java.text.SimpleDateFormat.
+# If the format is not specified or is invalid, the default format is used.
+# The default format is yyyy-MM-dd HH:mm:ss:SSS Z.
+#org.slf4j.simpleLogger.dateTimeFormat=yyyy-MM-dd HH:mm:ss:SSS Z
+org.slf4j.simpleLogger.dateTimeFormat=yyyy-MM-dd HH:mm:ss
+
+# Set to true if you want to output the current thread name.
+# Defaults to true.
+org.slf4j.simpleLogger.showThreadName=true

cavis-common-platform/build.gradle

@@ -30,6 +30,8 @@ ext {
 
     def netty = [version: "4.1.68.Final"]
 
+    def okhttp3 = [version: "4.10.0"]
+
 
 javaPlatform {
    allowDependencies()
@@ -40,12 +42,16 @@ dependencies {
     api enforcedPlatform("io.netty:netty-bom:${netty.version}")
     api enforcedPlatform("com.fasterxml.jackson:jackson-bom:${jackson.version}")
     //api enforcedPlatform("com.fasterxml.jackson.core:jackson-annotations:${jackson.version}")
+    api enforcedPlatform("com.squareup.okhttp3:okhttp-bom:${okhttp3.version}")
 
 
     constraints {
         api enforcedPlatform("io.netty:netty-bom:${netty.version}")
         api enforcedPlatform("com.fasterxml.jackson:jackson-bom:${jackson.version}")
+        api enforcedPlatform("com.squareup.okhttp3:okhttp-bom:${okhttp3.version}")
         //api enforcedPlatform("com.fasterxml.jackson.core:jackson-annotations:${jackson.version}")
+        //api "com.squareup.okhttp3:okhttp:${okhttp3}.version"
+        //api "com.squareup.okhttp3:logging-interceptor:${okhttp3}.version"
 
         api 'com.google.guava:guava:30.1-jre'
         api "com.google.protobuf:protobuf-java:3.15.6"
@@ -157,6 +163,7 @@ dependencies {
 
         api "org.agrona:agrona:1.12.0"
 
+
     }
 }
 

cavis-datavec/cavis-datavec-data/cavis-datavec-data-image/src/main/java/org/datavec/image/transform/ColorConversionTransform.java

@@ -22,6 +22,7 @@ package org.datavec.image.transform;
 
 import lombok.Data;
 import lombok.EqualsAndHashCode;
+import lombok.extern.slf4j.Slf4j;
 import org.bytedeco.javacv.OpenCVFrameConverter;
 import org.datavec.image.data.ImageWritable;
 import com.fasterxml.jackson.annotation.JsonInclude;
@@ -35,6 +36,7 @@ import static org.bytedeco.opencv.global.opencv_imgproc.*;
 @JsonInclude(JsonInclude.Include.NON_NULL)
 @Data
 @EqualsAndHashCode(callSuper = false)
+@Slf4j
 public class ColorConversionTransform extends BaseImageTransform {
 
     /**
@@ -85,14 +87,18 @@ public class ColorConversionTransform extends BaseImageTransform {
             return null;
         }
         Mat mat = (Mat) converter.convert(image.getFrame());
-
         Mat result = new Mat();
 
+        if(mat.type() != result.type() ) {
             try {
                 cvtColor(mat, result, conversionCode);
             } catch (Exception e) {
                 throw new RuntimeException(e);
             }
+        } else {
+            log.debug("Image is already at type {}. No conversion done.", mat.type());
+            return image;
+        }
 
         return new ImageWritable(converter.convert(result));
     }

cavis-datavec/cavis-datavec-data/cavis-datavec-data-image/src/main/java/org/datavec/image/transform/ShowImageTransform.java

@@ -85,6 +85,7 @@ public class ShowImageTransform extends BaseImageTransform {
         if (!canvas.isVisible()) {
             return image;
         }
+
         Frame frame = image.getFrame();
         canvas.setCanvasSize(frame.imageWidth, frame.imageHeight);
         canvas.showImage(frame);

cavis-dnn/cavis-dnn-api/src/main/java/org/nd4j/linalg/factory/Nd4j.java

@@ -5171,22 +5171,22 @@ public class Nd4j {
             Class<? extends DistributionFactory> distributionFactoryClazz = ND4JClassLoading.loadClassByName(clazzName);
 
 
-            memoryManager = memoryManagerClazz.newInstance();
+            memoryManager = memoryManagerClazz.getDeclaredConstructor().newInstance();
-            constantHandler = constantProviderClazz.newInstance();
+            constantHandler = constantProviderClazz.getDeclaredConstructor().newInstance();
-            shapeInfoProvider = shapeInfoProviderClazz.newInstance();
+            shapeInfoProvider = shapeInfoProviderClazz.getDeclaredConstructor().newInstance();
-            workspaceManager = workspaceManagerClazz.newInstance();
+            workspaceManager = workspaceManagerClazz.getDeclaredConstructor().newInstance();
 
             Class<? extends OpExecutioner> opExecutionerClazz = ND4JClassLoading
                     .loadClassByName(pp.toString(OP_EXECUTIONER, DefaultOpExecutioner.class.getName()));
 
-            OP_EXECUTIONER_INSTANCE = opExecutionerClazz.newInstance();
+            OP_EXECUTIONER_INSTANCE = opExecutionerClazz.getDeclaredConstructor().newInstance();
             Constructor c2 = ndArrayFactoryClazz.getConstructor(DataType.class, char.class);
             INSTANCE = (NDArrayFactory) c2.newInstance(dtype, ORDER);
-            CONVOLUTION_INSTANCE = convolutionInstanceClazz.newInstance();
+            CONVOLUTION_INSTANCE = convolutionInstanceClazz.getDeclaredConstructor().newInstance();
-            BLAS_WRAPPER_INSTANCE = blasWrapperClazz.newInstance();
+            BLAS_WRAPPER_INSTANCE = blasWrapperClazz.getDeclaredConstructor().newInstance();
-            DATA_BUFFER_FACTORY_INSTANCE = dataBufferFactoryClazz.newInstance();
+            DATA_BUFFER_FACTORY_INSTANCE = dataBufferFactoryClazz.getDeclaredConstructor().newInstance();
 
-            DISTRIBUTION_FACTORY = distributionFactoryClazz.newInstance();
+            DISTRIBUTION_FACTORY = distributionFactoryClazz.getDeclaredConstructor().newInstance();
 
             if (isFallback()) {
                 fallbackMode.set(true);

cavis-dnn/cavis-dnn-common/src/main/java/org/nd4j/common/config/ND4JClassLoading.java

@@ -58,11 +58,13 @@ public final class ND4JClassLoading {
 
     @SuppressWarnings("unchecked")
     public static <T> Class<T> loadClassByName(String className, boolean initialize, ClassLoader classLoader) {
+
         try {
-            log.info(String.format("Trying to load class [%s]", className));
+            Class<T> clazz = (Class<T>) Class.forName(className, initialize, classLoader);
-            return (Class<T>) Class.forName(className, initialize, classLoader);
+            log.info(String.format("Trying to load class [%s] - Success", className));
+            return clazz;
         } catch (ClassNotFoundException classNotFoundException) {
-            log.error(String.format("Cannot find class [%s] of provided class-loader.", className));
+            log.error(String.format("Trying to load class [%s] - Failure: Cannot find class with provided class-loader.", className));
             return null;
         }
     }

cavis-dnn/cavis-dnn-nn/build.gradle

@@ -21,6 +21,8 @@
 apply from: "${project.rootProject.projectDir}/createTestBackends.gradle"
 
 dependencies {
+    implementation platform(projects.cavisCommonPlatform)
+
     implementation projects.cavisDnn.cavisDnnData.cavisDnnDataUtilityIterators
     implementation 'org.lucee:oswego-concurrent:1.3.4'
     implementation projects.cavisDnn.cavisDnnCommon
@@ -50,4 +52,9 @@ dependencies {
     implementation "com.fasterxml.jackson.core:jackson-databind"
     implementation "com.fasterxml.jackson.dataformat:jackson-dataformat-yaml"
     implementation "com.jakewharton.byteunits:byteunits:0.9.1"
+
+    //Rest Client
+    // define any required OkHttp artifacts without version
+    implementation "com.squareup.okhttp3:okhttp"
+    implementation "com.squareup.okhttp3:logging-interceptor"
 }

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/Layer.java

@@ -215,7 +215,7 @@ public abstract class Layer implements TrainingConfig, Serializable, Cloneable {
 
     /**
      * Get the updater for the given parameter. Typically the same updater will be used for all
-     * updaters, but this is not necessarily the case
+     * parameters, but this is not necessarily the case
      *
      * @param paramName Parameter name
      * @return IUpdater for the parameter

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/layers/feedforward/dense/DenseLayer.java

@@ -30,6 +30,7 @@ import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
  * @author Adam Gibson
  */
 public class DenseLayer extends BaseLayer<org.deeplearning4j.nn.conf.layers.DenseLayer> {
+
   public DenseLayer(NeuralNetConfiguration conf, DataType dataType) {
     super(conf, dataType);
   }
@@ -45,12 +46,12 @@ public class DenseLayer extends BaseLayer<org.deeplearning4j.nn.conf.layers.Dens
   }
 
   @Override
-    public boolean hasBias(){
+  public boolean hasBias() {
     return layerConf().hasBias();
   }
 
   @Override
-    public boolean hasLayerNorm(){
+  public boolean hasLayerNorm() {
     return layerConf().hasLayerNorm();
   }
 }

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/optimize/listeners/ScoreToChartListener.java

@@ -0,0 +1,62 @@
+/*
+ *
+ *    ******************************************************************************
+ *    *
+ *    * 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 org.deeplearning4j.optimize.listeners;
+
+import java.io.IOException;
+import lombok.extern.slf4j.Slf4j;
+import okhttp3.OkHttpClient;
+import okhttp3.Request;
+import okhttp3.Response;
+import org.deeplearning4j.nn.api.Model;
+import org.deeplearning4j.optimize.api.BaseTrainingListener;
+
+@Slf4j
+public class ScoreToChartListener extends BaseTrainingListener {
+
+  final String url = "http://bru5:8080/cavis-rest-1.0-SNAPSHOT.war/hello/hello-world?";
+  final String seriesName;
+
+  public ScoreToChartListener(String seriesName) {
+    this.seriesName = seriesName;
+  }
+
+  @Override
+  public void iterationDone(Model model, int iteration, int epoch) {
+    double score = model.score();
+    String nurl = url+"s="+score+"&n="+seriesName;
+    OkHttpClient client = new OkHttpClient();
+
+    Request request = new Request.Builder()
+        .url(nurl)
+        .build();
+
+    try {
+      Response response = client.newCall(request).execute();
+      log.debug(String.format("Did send score to chart at '%s'.", nurl));
+      response.body().close();
+    } catch (IOException e) {
+      log.warn(String.format("Could not send score to chart at '%s' because %s", nurl, e.getMessage()));
+    }
+    //response.body().string();
+    }
+
+}

cavis-native/cavis-native-blas/src/main/java/org/nd4j/nativeblas/NativeOpsGPUInfoProvider.java

@@ -37,7 +37,6 @@ public class NativeOpsGPUInfoProvider implements GPUInfoProvider {
 
     List<GPUInfo> gpus = new ArrayList<>();
 
-
     int nDevices = nativeOps.getAvailableDevices();
     if (nDevices > 0) {
       for (int i = 0; i < nDevices; i++) {

cavis-native/cavis-native-jcublas/src/main/java/org/nd4j/jita/allocator/impl/AtomicAllocator.java

@@ -83,7 +83,7 @@ import java.util.concurrent.locks.ReentrantReadWriteLock;
  */
 @Slf4j
 public class AtomicAllocator implements Allocator {
-    private static final AtomicAllocator INSTANCE = new AtomicAllocator();
+    private static AtomicAllocator INSTANCE = new AtomicAllocator();
 
     private Configuration configuration;
 
@@ -122,6 +122,7 @@ public class AtomicAllocator implements Allocator {
     private final AtomicLong useTracker = new AtomicLong(System.currentTimeMillis());
 
     public static AtomicAllocator getInstance() {
+        if(INSTANCE == null) INSTANCE = new AtomicAllocator();
         if (INSTANCE == null)
             throw new RuntimeException("AtomicAllocator is NULL");
         return INSTANCE;

cavis-native/cavis-native-jcublas/src/main/java/org/nd4j/linalg/jcublas/buffer/BaseCudaDataBuffer.java

@@ -402,6 +402,10 @@ public abstract class BaseCudaDataBuffer extends BaseDataBuffer implements JCuda
             val ctx = AtomicAllocator.getInstance().getDeviceContext();
             val devicePtr = allocationPoint.getDevicePointer();
             NativeOpsHolder.getInstance().getDeviceNativeOps().memsetAsync(devicePtr, 0, length * elementSize, 0, ctx.getSpecialStream());
+            int ec = NativeOpsHolder.getInstance().getDeviceNativeOps().lastErrorCode();
+            if(ec != 0) {
+                throw new RuntimeException(NativeOpsHolder.getInstance().getDeviceNativeOps().lastErrorMessage());
+            }
             ctx.getSpecialStream().synchronize();
         }
 

cavis-native/cavis-native-jcublas/src/test/java/org/nd4j/linalg/jcublas/buffer/BaseCudaDataBufferTest.java

@@ -0,0 +1,55 @@
+/*
+ *
+ *    ******************************************************************************
+ *    *
+ *    * 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 org.nd4j.linalg.jcublas.buffer;
+
+import static org.junit.jupiter.api.Assertions.*;
+
+import lombok.extern.slf4j.Slf4j;
+import org.bytedeco.javacpp.BytePointer;
+import org.bytedeco.javacpp.Pointer;
+import org.junit.jupiter.api.Test;
+import org.nd4j.linalg.api.buffer.DataBuffer;
+import org.nd4j.linalg.api.environment.Nd4jEnvironment;
+import org.nd4j.linalg.factory.Nd4j;
+import org.nd4j.nativeblas.NativeOpsHolder;
+
+@Slf4j
+class BaseCudaDataBufferTest {
+
+  @Test
+  public void testMemoryAlloc() throws InterruptedException {
+    BaseCudaDataBuffer cuBuffer = new CudaLongDataBuffer(16l);
+    log.info(
+        "Allocation Status: " + cuBuffer.getAllocationPoint().getAllocationStatus().toString());
+    Thread.sleep(3000);
+    cuBuffer.getAllocationPoint().tickDeviceWrite();
+    DataBuffer buf = Nd4j.rand(8,1).shapeInfoDataBuffer();
+    NativeOpsHolder.getInstance().getDeviceNativeOps().memcpySync(cuBuffer.pointer(), buf.pointer(), 8,  0, new Pointer() );
+
+    log.info(
+        "Allocation Status: " + cuBuffer.getAllocationPoint().getAllocationStatus().toString());
+
+    cuBuffer.release();
+  }
+
+
+}