Error Implementing model training using YOLOv8 on Android

[vishalvky007]

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Question

Getting this error java.lang.IllegalArgumentException: Cannot copy from a TensorFlowLite tensor (Identity) with shape [1, 8, 8400] to a Java object with shape [1, 10, 4].

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[glenn-jocher]

Hello! It seems like there's a mismatch between the expected output tensor shape from your TensorFlow Lite model and the shape defined in your Java code for handling the output.

Make sure the shape you're specifying in Java matches the model's final output. Based on the error, your model returns a tensor of shape [1, 8, 8400], so your Java object should reflect this shape to avoid shape incompatibility issues.

Here's how you might adjust your Java side:

// Instead, your output shape here should likely correlate with the shape [1, 8, 8400]
float[][][] output = new float[1][8][8400];

Adjust the dimensions accordingly to match your specific model outputs! If the error persists or there are further details you can provide, don't hesitate to reach out. Happy coding! 😊

[vishalvky007]

package org.tensorflow.lite.examples.detection.tflite;

import android.content.res.AssetFileDescriptor; import android.content.res.AssetManager; import android.graphics.Bitmap; import android.graphics.RectF; import android.os.Trace; import java.io.BufferedReader; import java.io.FileInputStream; import java.io.IOException; import java.io.InputStream; import java.io.InputStreamReader; import java.nio.ByteBuffer; import java.nio.ByteOrder; import java.nio.MappedByteBuffer; import java.nio.channels.FileChannel; import java.util.ArrayList; import java.util.HashMap; import java.util.List; import java.util.Map; import java.util.Vector; import org.tensorflow.lite.Interpreter; import org.tensorflow.lite.examples.detection.env.Logger;

public class TFLiteObjectDetectionAPIModel implements Classifier { private static final Logger LOGGER = new Logger();

// Only return this many results. private static final int NUM_DETECTIONS = 10; // Float model private static final float IMAGE_MEAN = 128.0f; private static final float IMAGE_STD = 128.0f; // Number of threads in the java app private static final int NUM_THREADS = 4; private boolean isModelQuantized; // Config values. private int inputSize; // Pre-allocated buffers. private final Vector labels = new Vector(); private int[] intValues; // outputLocations: array of shape [Batchsize, NUM_DETECTIONS,4] // contains the location of detected boxes private float[][][] outputLocations; // outputClasses: array of shape [Batchsize, NUM_DETECTIONS] // contains the classes of detected boxes private float[][] outputClasses; // outputScores: array of shape [Batchsize, NUM_DETECTIONS] // contains the scores of detected boxes private float[][] outputScores; // numDetections: array of shape [Batchsize] // contains the number of detected boxes private float[] numDetections;

private ByteBuffer imgData;

private Interpreter tfLite;

private TFLiteObjectDetectionAPIModel() {}

/* Memory-map the model file in Assets. / private static MappedByteBuffer loadModelFile(AssetManager assets, String modelFilename) throws IOException { AssetFileDescriptor fileDescriptor = assets.openFd(modelFilename); FileInputStream inputStream = new FileInputStream(fileDescriptor.getFileDescriptor()); FileChannel fileChannel = inputStream.getChannel(); long startOffset = fileDescriptor.getStartOffset(); long declaredLength = fileDescriptor.getDeclaredLength(); return fileChannel.map(FileChannel.MapMode.READ_ONLY, startOffset, declaredLength); }

/**

• Initializes a native TensorFlow session for classifying images.
• @param assetManager The asset manager to be used to load assets.
• @param modelFilename The filepath of the model GraphDef protocol buffer.
• @param labelFilename The filepath of label file for classes.
• @param inputSize The size of image input

• @param isQuantized Boolean representing model is quantized or not */ public static Classifier create( final AssetManager assetManager, final String modelFilename, final String labelFilename, final int inputSize, final boolean isQuantized) throws IOException { final TFLiteObjectDetectionAPIModel d = new TFLiteObjectDetectionAPIModel();

  InputStream labelsInput = null; String actualFilename = labelFilename.split("file:////android_asset/")[1]; labelsInput = assetManager.open(actualFilename); BufferedReader br = null; br = new BufferedReader(new InputStreamReader(labelsInput)); String line; while ((line = br.readLine()) != null) { LOGGER.w(line); d.labels.add(line); } br.close();

  d.inputSize = 2100;

  try { d.tfLite = new Interpreter(loadModelFile(assetManager, modelFilename)); } catch (Exception e) { throw new RuntimeException(e); }

  d.isModelQuantized = isQuantized; // Pre-allocate buffers. int numBytesPerChannel; if (isQuantized) { numBytesPerChannel = 1; // Quantized } else { numBytesPerChannel = 4; // Floating point } d.imgData = ByteBuffer.allocateDirect(1 inputSize inputSize 3 numBytesPerChannel); d.imgData.order(ByteOrder.nativeOrder()); d.intValues = new int[d.inputSize * d.inputSize];

  d.tfLite.setNumThreads(NUM_THREADS); d.outputLocations = new float[1][NUM_DETECTIONS][4]; d.outputClasses = new float[1][NUM_DETECTIONS]; d.outputScores = new float[1][NUM_DETECTIONS]; d.numDetections = new float[1]; return d; }

  @Override public List recognizeImage(final Bitmap bitmap) { // Log this method so that it can be analyzed with systrace. Trace.beginSection("recognizeImage");

  Trace.beginSection("preprocessBitmap"); // Preprocess the image data from 0-255 int to normalized float based // on the provided parameters. bitmap.getPixels(intValues, 0, bitmap.getWidth(), 0, 0, bitmap.getWidth(), bitmap.getHeight());

  imgData.rewind(); for (int i = 0; i < inputSize; ++i) { for (int j = 0; j < inputSize; ++j) { int pixelValue = intValues[i * inputSize + j]; if (isModelQuantized) { // Quantized model imgData.put((byte) ((pixelValue >> 16) & 0xFF)); imgData.put((byte) ((pixelValue >> 8) & 0xFF)); imgData.put((byte) (pixelValue & 0xFF)); } else { // Float model imgData.putFloat((((pixelValue >> 16) & 0xFF) - IMAGE_MEAN) / IMAGE_STD); imgData.putFloat((((pixelValue >> 8) & 0xFF) - IMAGE_MEAN) / IMAGE_STD); imgData.putFloat(((pixelValue & 0xFF) - IMAGE_MEAN) / IMAGE_STD); } } } Trace.endSection(); // preprocessBitmap

  // Copy the input data into TensorFlow. Trace.beginSection("feed"); outputLocations = new float[1][NUM_DETECTIONS][4]; outputClasses = new float[1][NUM_DETECTIONS]; outputScores = new float[1][NUM_DETECTIONS]; numDetections = new float[1];

  Object[] inputArray = {imgData}; Map<Integer, Object> outputMap = new HashMap<>();

  outputMap.put(0, outputLocations); outputMap.put(1, outputClasses); outputMap.put(2, outputScores); outputMap.put(3, numDetections); Trace.endSection();

  // Run the inference call. Trace.beginSection("run"); tfLite.runForMultipleInputsOutputs(inputArray, outputMap); Trace.endSection();

  // Show the best detections. // after scaling them back to the input size. final ArrayList recognitions = new ArrayList<>(NUM_DETECTIONS); for (int i = 0; i < NUM_DETECTIONS; ++i) { final RectF detection = new RectF( outputLocations[0][i][1] inputSize, outputLocations[0][i][0] inputSize, outputLocations[0][i][3] inputSize, outputLocations[0][i][2] inputSize); // SSD Mobilenet V1 Model assumes class 0 is background class // in label file and class labels start from 1 to number_of_classes+1, // while outputClasses correspond to class index from 0 to number_of_classes int labelOffset = 1; recognitions.add( new Recognition( "" + i, labels.get((int) outputClasses[0][i] + labelOffset), outputScores[0][i], detection)); } Trace.endSection(); // "recognizeImage" return recognitions; }

  @Override public void enableStatLogging(final boolean logStats) {}

  @Override public String getStatString() { return ""; }

  @Override public void close() {}

  public void setNumThreads(int num_threads) { if (tfLite != null) tfLite.setNumThreads(num_threads); }

  @Override public void setUseNNAPI(boolean isChecked) { if (tfLite != null) tfLite.setUseNNAPI(isChecked); } }

I'm not getting the Java Object where it is declared?