Merge branch 'develop'

.travis.yml

@@ -1,13 +1,13 @@
 language: java
 install: true
 
-# before_install commands required as of https://www.deps.co/guides/travis-ci-latest-java/
 matrix:
   include:
-    - jdk: openjdk11
+    - jdk:
+      - openjdk8
+      - openjdk11
       before_install:
-        - sudo rm "${JAVA_HOME}/lib/security/cacerts"
-        - sudo ln -s /etc/ssl/certs/java/cacerts "${JAVA_HOME}/lib/security/cacerts"
+        - sudo ./fix-java-verts.sh
 
 before_cache:
   - rm -f  $HOME/.gradle/caches/modules-2/modules-2.lock

GUIEditors.props

@@ -50,6 +50,8 @@ weka.dl4j.inference.Dl4jCNNExplorer=weka.gui.GenericObjectEditor
 
 weka.dl4j.inference.ModelOutputDecoder=weka.gui.GenericObjectEditor
 
+weka.dl4j.inference.CustomModelSetup=weka.gui.GenericObjectEditor
+
 weka.dl4j.interpretability.AbstractCNNSaliencyMapWrapper=weka.gui.GenericObjectEditor
 
 

GenericPropertiesCreator.props

@@ -67,5 +67,8 @@ weka.dl4j.inference.Dl4jCNNExplorer =\
 weka.dl4j.inference.ModelOutputDecoder =\
  weka.dl4j.inference
 
+weka.dl4j.inference.CustomModelSetup =\
+ weka.dl4j.inference
+
 weka.dl4j.interpretability.AbstractCNNSaliencyMapWrapper =\
- weka.dl4j.interpretability
+ weka.dl4j.interpretability

build.gradle

@@ -14,8 +14,8 @@ def is_cuda_version_valid = cuda_version in valid_cuda_versions
 
 // Set JDK compatibility
 allprojects {
-    sourceCompatibility = 1.11
-    targetCompatibility = 1.11
+    sourceCompatibility = 1.8
+    targetCompatibility = 1.8
 }
 
 /*
@@ -335,11 +335,10 @@ task travisTest(type: Test) {
     }
 
     filter {
-        // Excluded from travis due to taking too long / using too much memory
+        // Excluded from travis due to taking too long / using too much memory and throwing OOM exception.
         excludeTestsMatching 'ZooModelTest'
         excludeTestsMatching 'Dl4jCNNExplorerTest'
         excludeTestsMatching 'Dl4jMlpFilterTest'
-        excludeTestsMatching 'ScoreCAMTest'
     }
 }
 

docs/examples/dl4j-inference.md

@@ -94,7 +94,7 @@ see the results in the output panel, again correctly predicting the target class
 $ java weka.Run .Dl4jCNNExplorer \
     -decoder ".ModelOutputDecoder -builtIn VGGFACE" \
     -zooModel ".Dl4jVGG -variation VGG16 -pretrained VGGFACE" \
-    -i src/test/resources/images/ben_stiller.jpg
+    -i $WEKA_HOME/packages/wekaDeeplearning4j/src/test/resources/images/ben_stiller.jpg
 ```
 
 ```bash
@@ -125,8 +125,11 @@ which you'd like to experiment with; the process is largely the same as above, w
 
 On the `Dl4j Inference` panel, open the `Dl4jCNNExplorer` settings:
 
-- Set `Use serialized model file` to `True`
-- Select your previously saved `.model` file as the `Serialized model file`
+- Set `Use custom-trained model file` to `True`
+- Open the `CustomModelSetup` settings
+    - Select your previously saved `.model` file as the `Serialized model file`
+    - Set the input `channels`, `width`, and `height` with the values used to train the model. 
+    These values will be identical to those set on the `ImageInstanceIterator`. 
 - Open the `ModelOutputDecoder` settings:
     - Set `Built in class map` to `CUSTOM`
     - Select the `Class map file` on your machine. This can be in two forms:
@@ -160,12 +163,16 @@ to classify between different dog breeds isn't going to give accurate answers wh
 
 ### Command Line
 
+This example uses a custom-trained model which used an `ImageInstanceIterator` using 
+`channels`, `width`, and `height` of `3`, `56`, `56`, respectively. These values are explicitly
+defined in the `CustomModelSetup`.
+
 ```bash
 $ java weka.Run .Dl4jCNNExplorer \
-    -decoder ".ModelOutputDecoder -builtIn CUSTOM -classMapFile /path/to/classmap.txt" \
-    -model-file /path/to/saved/model/Dl4jMlpClassifier.model \
-    -use-model-file
-    -i /path/to/input/image.png
+    -i path/to/image.jpg \
+    -custom-model ".CustomModelSetup -channels 3 -height 56 -width 56 -model-file path/to/customModel.model" \
+    -decoder ".ModelOutputDecoder -builtIn CUSTOM -classMapFile path/to/training.arff" \
+    -useCustomModel
 ```
 
 ## Example 4: Saliency Map Generation
@@ -245,7 +252,7 @@ know what the class ID may be.
 
 ```bash
 $ java weka.Run .Dl4jCNNExplorer \
-    -i "src/test/resources/images/catAndDog.jpg" \
+    -i "$WEKA_HOME/packages/wekaDeeplearning4j/src/test/resources/images/catAndDog.jpg" \
     -generate-map \
     -saliency-map ".WekaScoreCAM -bs 8 -normalize -output output_image.png -target-classes -1" \
     -zooModel ".KerasResNet -variation RESNET101"
@@ -255,7 +262,7 @@ $ java weka.Run .Dl4jCNNExplorer \
 
 ```bash
 $ java weka.Run .Dl4jCNNExplorer \
-    -i "src/test/resources/images/catAndDog.jpg" \
+    -i "$WEKA_HOME/packages/wekaDeeplearning4j/src/test/resources/images/catAndDog.jpg" \
     -generate-map \
     -saliency-map ".WekaScoreCAM -bs 8 -normalize -output output_image.png -target-classes -1,281" \
     -zooModel ".KerasResNet -variation RESNET101"

docs/examples/featurize-mnist.md

@@ -43,7 +43,8 @@ All zoo models have a **default** feature extraction layer, which is typically t
 activations, hence why it's set to the default (although you can use any intermediary layer).
 
 `PoolingType` does not need to be specified when using the default activation layer - the outputs are already the
- correct dimensionality.
+ correct dimensionality (`[batch size, num activations]`). If using an intermediary layer the outputs will
+ typically be of size `[batch size, width, height, num channels]`.
 
 ## Example 1: Default MNIST Minimal
 The following example walks through using a pretrained ResNet50 (from the Deeplearning4j model zoo)

fix-java-verts.sh

@@ -0,0 +1,9 @@
+# Commands required as of https://www.deps.co/guides/travis-ci-latest-java/
+JAVA_FILE="${JAVA_HOME}/lib/security/cacerts"
+
+if [ -f "$JAVA_FILE" ]; then
+  sudo rm -f "${JAVA_HOME}/lib/security/cacerts"
+  sudo ln -s /etc/ssl/certs/java/cacerts "${JAVA_HOME}/lib/security/cacerts"
+else
+  echo "Couldn't find java certificates folder, ignoring..."
+fi

src/main/java/weka/classifiers/functions/Dl4jMlpClassifier.java

@@ -31,7 +31,6 @@
 import org.apache.commons.io.output.CountingOutputStream;
 import org.apache.commons.io.output.NullOutputStream;
 import org.apache.commons.lang3.time.StopWatch;
-import org.apache.lucene.util.ThreadInterruptedException;
 import org.deeplearning4j.datasets.iterator.AsyncDataSetIterator;
 import org.deeplearning4j.exception.DL4JException;
 import org.deeplearning4j.exception.DL4JInvalidConfigException;
@@ -65,7 +64,7 @@
 import weka.dl4j.enums.CacheMode;
 import weka.dl4j.enums.ConvolutionMode;
 import weka.dl4j.enums.PoolingType;
-import weka.dl4j.enums.PretrainedType;
+import weka.dl4j.inference.CustomModelSetup;
 import weka.dl4j.iterators.instance.*;
 import weka.dl4j.iterators.instance.api.ConvolutionalIterator;
 import weka.dl4j.iterators.instance.sequence.text.cnn.CnnTextEmbeddingInstanceIterator;
@@ -279,7 +278,7 @@ public class Dl4jMlpClassifier extends RandomizableClassifier implements
    */
   protected ProgressManager progressManager;
 
-  private SwingWorker<Layer[], Layer> layerSwingWorker;
+  private SwingWorker<Layer[], Void> layerSwingWorker;
 
   public Dl4jMlpClassifier() {
     if (!s_cudaMultiGPUSet) {
@@ -810,6 +809,18 @@ public void setParameterAveragingFrequency(int frequency) {
     averagingFrequency = frequency;
   }
 
+  /**
+   * Get the name of the loaded model
+   * @return Model name
+   */
+  public String getModelName() {
+    if (useZooModel()) {
+      return getZooModel().getPrettyName();
+    } else {
+      return "Custom trained Dl4jMlpClassifier";
+    }
+  }
+
   /**
    * The method used to train the classifier.
    *
@@ -1254,7 +1265,18 @@ protected Instances initFilters(Instances data) throws Exception {
     return data;
   }
 
-
+  public InputType.InputTypeConvolutional getInputShape(CustomModelSetup customModelSetup) {
+    if (useZooModel()) {
+      int[] inputShape = getZooModel().getInputShape();
+      log.debug("Zoo Model shape for image inference is: " + Arrays.toString(inputShape));
+      return new InputType.InputTypeConvolutional(inputShape[1], inputShape[2], inputShape[0]);
+    }
+
+    return new InputType.InputTypeConvolutional(
+            customModelSetup.getInputHeight(),
+            customModelSetup.getInputWidth(),
+            customModelSetup.getInputChannels());
+  }
 
   /**
    * Build the Zoomodel instance
@@ -1669,7 +1691,7 @@ public void setZooModel(AbstractZooModel zooModel) {
       progressManager = new ProgressManager("Parsing model layers...");
       progressManager.start();
 
-      layerSwingWorker = new SwingWorker<>() {
+      layerSwingWorker = new SwingWorker<Layer[], Void>() {
         @Override
         protected Layer[] doInBackground() throws Exception {
           return parseLayers();
@@ -1705,7 +1727,7 @@ private Layer[] parseLayers() {
       Thread.currentThread().setContextClassLoader(
               this.getClass().getClassLoader());
       ComputationGraph tmpCg =
-              zooModel.init(dummyNumLabels, getSeed(), zooModel.getShape()[0], isFilterMode());
+              zooModel.init(dummyNumLabels, getSeed(), zooModel.getInputShape(), isFilterMode());
       tmpCg.init();
        tmpLayers =
               Arrays.stream(tmpCg.getLayers())
@@ -1737,6 +1759,7 @@ public static Dl4jMlpClassifier tryLoadFromFile(File serializedModelFile, Abstra
       // First try load from the WEKA binary model file
       try (ObjectInputStream ois = new ObjectInputStream(new FileInputStream(serializedModelFile))) {
         model = (Dl4jMlpClassifier) ois.readObject();
+        model.setCustomNet();
       } catch (Exception e) {
         throw new WekaException("Couldn't load Dl4jMlpClassifier from model file");
       }
@@ -1786,7 +1809,7 @@ public static Dl4jMlpClassifier loadInferenceModel(File serializedModelFile, Abs
     Dl4jMlpClassifier model = tryLoadFromFile(serializedModelFile, zooModelType);
 
     if (!Utils.notDefaultFileLocation(serializedModelFile))
-      model.loadZooModelNoData(2, 1, zooModelType.getShape()[0]);
+      model.loadZooModelNoData(2, 1, zooModelType.getInputShape());
 
     return model;
   }
@@ -1986,7 +2009,7 @@ public String toString() {
    *
    * @return True if zoomodel is not CustomNet
    */
-  protected boolean useZooModel() {
+  public boolean useZooModel() {
     return !(zooModel instanceof CustomNet);
   }
 

src/main/java/weka/core/progress/CommandLineProgressBar.java

@@ -1,13 +1,15 @@
 package weka.core.progress;
 
 import lombok.extern.log4j.Log4j2;
+import org.apache.commons.lang.CharUtils;
+import org.apache.commons.lang.StringUtils;
 
 import java.util.Timer;
 import java.util.TimerTask;
 
 @Log4j2
-/**
- * Command line implementation of a progress bar
+/*
+  Command line implementation of a progress bar
  */
 public class CommandLineProgressBar extends AbstractProgressBar {
 
@@ -67,9 +69,9 @@ public void refreshDisplay() {
 
         System.err.printf("\r%s: [%s%s%s] %s",
                 getProgressMessage(),
-                progressRemainingChar.repeat(leftSpace),
-                progressChar.repeat(currNumDots),
-                progressRemainingChar.repeat(rightSpace),
+                StringUtils.repeat(progressRemainingChar, leftSpace),
+                StringUtils.repeat(progressChar, currNumDots),
+                StringUtils.repeat(progressRemainingChar, rightSpace),
                 getETAString());
     }
 

src/main/java/weka/dl4j/Utils.java

@@ -496,74 +496,6 @@ public static String defaultFileLocation() {
     return WekaPackageManager.getPackageHome().getPath();
   }
 
-  public static void saveNDArray(INDArray array, String filenamePrefix) {
-    BufferedImage img = Utils.imageFromINDArray(array);
-    try {
-      ImageIO.write(img, "png", new File(filenamePrefix + ".png"));
-    } catch (IOException ex) {
-      ex.printStackTrace();
-    }
-  }
-
-  /**
-   * Takes an INDArray containing an image loaded using the native image loader
-   * libraries associated with DL4J, and converts it into a BufferedImage.
-   * The INDArray contains the color values split up across three channels (RGB)
-   * and in the integer range 0-255.
-   *
-   * @param array INDArray containing an image in order [N, C, H, W] or [C, H, W]
-   * @return BufferedImage
-   */
-  public static BufferedImage imageFromINDArray(INDArray array) {
-    long[] shape = array.shape();
-
-    boolean is4d = false;
-    String dimString = "3D";
-
-    if (shape.length == 4) {
-      is4d = true;
-      dimString = "4D";
-    }
-
-    log.debug(String.format("Converting %s INDArray to image...", dimString));
-
-    long height = shape[1];
-    long width = shape[2];
-
-    if (is4d) {
-      height = shape[2];
-      width = shape[3];
-    }
-
-    BufferedImage image = new BufferedImage((int) width, (int) height, BufferedImage.TYPE_INT_RGB);
-    for (int x = 0; x < width; x++) {
-      for (int y = 0; y < height; y++) {
-        int red, green, blue;
-
-        if (is4d) {
-          red = array.getInt(0, 2, y, x);
-          green = array.getInt(0, 1, y, x);
-          blue = array.getInt(0, 0, y, x);
-        } else {
-          red = array.getInt(2, y, x);
-          green = array.getInt(1, y, x);
-          blue = array.getInt(0, y, x);
-        }
-
-        //handle out of bounds pixel values
-        red = Math.min(red, 255);
-        green = Math.min(green, 255);
-        blue = Math.min(blue, 255);
-
-        red = Math.max(red, 0);
-        green = Math.max(green, 0);
-        blue = Math.max(blue, 0);
-        image.setRGB(x, y, new Color(red, green, blue).getRGB());
-      }
-    }
-    return image;
-  }
-
   public static InputType.InputTypeConvolutional decodeCNNShape (int[] shape) {
     return decodeCNNShape(Arrays.stream(shape).asLongStream().toArray());
   }