[Java] Tidying up the sample MNIST code (#3824)

* Updating the Java sample to load MNIST in libsvm format.
* java - code formatting fix.
Co-authored-by: Adam Pocock <adam.pocock@oracle.com>

java/README.md

@@ -9,7 +9,17 @@ TBD: maven distribution
 
 The minimum supported Java Runtime is version 8.
 
-An example implementation is located in [src/test/java/sample/ScoreMNIST.java](src/test/java/sample/ScoreMNIST.java)
+An example implementation is located in
+[src/test/java/sample/ScoreMNIST.java](src/test/java/sample/ScoreMNIST.java).
+Once compiled the sample code expects the following arguments `ScoreMNIST
+<path-to-mnist-model> <path-to-mnist> <scikit-learn-flag>`.  MNIST is expected
+to be in libsvm format. If the optional scikit-learn flag is supplied the model
+is expected to be produced by skl2onnx (so expects a flat feature vector, and
+produces a structured output), otherwise the model is expected to be a CNN from
+pytorch (expecting a `[1][1][28][28]` input, producing a vector of
+probabilities).  Two example models are provided in [testdata](testdata),
+`cnn_mnist_pytorch.onnx` and `lr_mnist_scikit.onnx`. The first is a LeNet5 style
+CNN trained using PyTorch, the second is a logistic regression trained using scikit-learn.
 
 This project can be built manually using the instructions below.
 

java/src/test/java/sample/ScoreMNIST.java

@@ -12,15 +12,16 @@ import ai.onnxruntime.OrtSession;
 import ai.onnxruntime.OrtSession.Result;
 import ai.onnxruntime.OrtSession.SessionOptions;
 import ai.onnxruntime.OrtSession.SessionOptions.OptLevel;
-import java.io.BufferedInputStream;
-import java.io.FileInputStream;
+import java.io.BufferedReader;
+import java.io.FileReader;
 import java.io.IOException;
-import java.io.ObjectInputStream;
+import java.util.ArrayList;
 import java.util.Arrays;
 import java.util.Collections;
 import java.util.List;
 import java.util.logging.Level;
 import java.util.logging.Logger;
+import java.util.regex.Pattern;
 
 /**
  * Demo code, supporting both a pytorch CNN trained on MNIST and a scikit-learn model trained on
@@ -29,6 +30,8 @@ import java.util.logging.Logger;
 public class ScoreMNIST {
 
   private static final Logger logger = Logger.getLogger(ScoreMNIST.class.getName());
+  /** Pattern for splitting libsvm format files. */
+  private static final Pattern splitPattern = Pattern.compile("\\s+");
 
   /** A named tuple for sparse classification data. */
   private static class SparseData {
@@ -38,29 +41,112 @@ public class ScoreMNIST {
 
     public SparseData(int[] labels, List<int[]> indices, List<float[]> values) {
       this.labels = labels;
-      this.indices = indices;
-      this.values = values;
+      this.indices = Collections.unmodifiableList(indices);
+      this.values = Collections.unmodifiableList(values);
     }
   }
 
   /**
-   * Deserialises the data and puts it in a named tuple.
+   * Converts a List of Integer into an int array.
+   *
+   * @param list The list to convert.
+   * @return The int array.
+   */
+  private static int[] convertInts(List<Integer> list) {
+    int[] output = new int[list.size()];
+    for (int i = 0; i < list.size(); i++) {
+      output[i] = list.get(i);
+    }
+    return output;
+  }
+
+  /**
+   * Converts a List of Float into a float array.
+   *
+   * @param list The list to convert.
+   * @return The float array.
+   */
+  private static float[] convertFloats(List<Float> list) {
+    float[] output = new float[list.size()];
+    for (int i = 0; i < list.size(); i++) {
+      output[i] = list.get(i);
+    }
+    return output;
+  }
+
+  /**
+   * Loads data from a libsvm format file.
    *
    * @param path The path to load the data from.
    * @return A named tuple containing the data.
    * @throws IOException If it failed to read the file.
-   * @throws ClassNotFoundException If a class wasn't found (only uses JDK types so this would be
-   *     very odd).
    */
-  @SuppressWarnings("unchecked")
-  private static SparseData load(String path) throws IOException, ClassNotFoundException {
-    try (ObjectInputStream ois =
-        new ObjectInputStream(new BufferedInputStream(new FileInputStream(path)))) {
-      int[] labels = (int[]) ois.readObject();
-      List<int[]> indices = (List<int[]>) ois.readObject();
-      List<float[]> values = (List<float[]>) ois.readObject();
-      return new SparseData(labels, indices, values);
+  private static SparseData load(String path) throws IOException {
+    int pos = 0;
+    List<int[]> indices = new ArrayList<>();
+    List<float[]> values = new ArrayList<>();
+    List<Integer> labels = new ArrayList<>();
+    String line;
+    int maxFeatureID = Integer.MIN_VALUE;
+    try (BufferedReader reader = new BufferedReader(new FileReader(path))) {
+      for (; ; ) {
+        line = reader.readLine();
+        if (line == null) {
+          break;
+        }
+        pos++;
+        String[] fields = splitPattern.split(line);
+        int lastID = -1;
+        try {
+          boolean valid = true;
+          List<Integer> curIndices = new ArrayList<>();
+          List<Float> curValues = new ArrayList<>();
+          for (int i = 1; i < fields.length && valid; i++) {
+            int ind = fields[i].indexOf(':');
+            if (ind < 0) {
+              logger.warning(String.format("Weird line at %d", pos));
+              valid = false;
+            }
+            String ids = fields[i].substring(0, ind);
+            int id = Integer.parseInt(ids);
+            curIndices.add(id);
+            if (maxFeatureID < id) {
+              maxFeatureID = id;
+            }
+            float val = Float.parseFloat(fields[i].substring(ind + 1));
+            curValues.add(val);
+            if (id <= lastID) {
+              logger.warning(String.format("Repeated features at line %d", pos));
+              valid = false;
+            } else {
+              lastID = id;
+            }
+          }
+          if (valid) {
+            // Store the label
+            labels.add(Integer.parseInt(fields[0]));
+            // Store the features
+            indices.add(convertInts(curIndices));
+            values.add(convertFloats(curValues));
+          } else {
+            throw new IOException("Invalid LibSVM format file at line " + pos);
+          }
+        } catch (NumberFormatException ex) {
+          logger.warning(String.format("Weird line at %d", pos));
+          throw new IOException("Invalid LibSVM format file", ex);
+        }
+      }
     }
+
+    logger.info(
+        "Loaded "
+            + maxFeatureID
+            + " features, "
+            + labels.size()
+            + " samples, from + '"
+            + path
+            + "'.");
+    return new SparseData(convertInts(labels), indices, values);
   }
 
   /**
@@ -170,11 +256,10 @@ public class ScoreMNIST {
     return idx;
   }
 
-  public static void main(String[] args) throws OrtException, IOException, ClassNotFoundException {
+  public static void main(String[] args) throws OrtException, IOException {
     if (args.length < 2 || args.length > 3) {
       System.out.println("Usage: ScoreMNIST <model-path> <test-data> <optional:scikit-learn-flag>");
-      System.out.println(
-          "The test data input format is a Java serialized file containing an array of int labels, a list of int[] feature indices, and a list of float[] feature values");
+      System.out.println("The test data input should be a libsvm format version of MNIST.");
       return;
     }
 
@@ -232,7 +317,7 @@ public class ScoreMNIST {
 
             confusionMatrix[data.labels[i]][predLabel]++;
 
-            if (i % 500 == 0) {
+            if (i % 2000 == 0) {
               logger.log(Level.INFO, "Cur accuracy = " + ((float) correctCount) / (i + 1));
               logger.log(Level.INFO, "Output type = " + output.get(0).toString());
               if (args.length == 3) {