I am trying to create a mobile app object detection using a custom model that I created when I try to use my model I get this error.
E/AndroidRuntime: FATAL EXCEPTION: inference
Process: com.objdetector, PID: 8767
java.lang.IllegalArgumentException: Cannot copy from a TensorFlowLite tensor (StatefulPartitionedCall:1) with shape [1, 25] to a Java object with shape [1, 10, 4].
at org.tensorflow.lite.Tensor.throwIfDstShapeIsIncompatible(Tensor.java:461)
at org.tensorflow.lite.Tensor.copyTo(Tensor.java:252)
at org.tensorflow.lite.NativeInterpreterWrapper.run(NativeInterpreterWrapper.java:170)
at org.tensorflow.lite.Interpreter.runForMultipleInputsOutputs(Interpreter.java:343)
at com.objdetector.deepmodel.MobileNetObjDetector.detectObjects(MobileNetObjDetector.java:130)
at com.objdetector.MainActivity.lambda$onImageAvailable$0$MainActivity(MainActivity.java:98)
at com.objdetector.-$$Lambda$MainActivity$MYtVDhek_YLxj8lClVnaIcVW0Gg.run(lambda)
at android.os.Handler.handleCallback(Handler.java:751)
at android.os.Handler.dispatchMessage(Handler.java:95)
at android.os.Looper.loop(Looper.java:154)
at android.os.HandlerThread.run(HandlerThread.java:61)
and this is my MobileNetObjDetector
public class MobileNetObjDetector {
private static final String MODEL_FILENAME = "letters.tflite";
private static final String LABEL_FILENAME = "label.txt";
private static final int INPUT_SIZE = 320;
private static final int NUM_BYTES_PER_CHANNEL = 1;
private static final float IMAGE_MEAN = 128.0f;
private static final float IMAGE_STD = 128.0f;
private static final int NUM_DETECTIONS = 10;
private static final String LOGGING_TAG = MobileNetObjDetector.class.getName();
private ByteBuffer imgData;
private Interpreter tfLite;
private int[] intValues;
private float[][][] outputLocations;
private float[][] outputClasses;
private float[][] outputScores;
private float[] numDetections;
private Vector<String> labels = new Vector<String>();
private MobileNetObjDetector(final AssetManager assetManager) throws IOException {
init(assetManager);
}
private void init(final AssetManager assetManager) throws IOException {
imgData = ByteBuffer.allocateDirect(1 * INPUT_SIZE * INPUT_SIZE * 3 * NUM_BYTES_PER_CHANNEL);
imgData.order(ByteOrder.nativeOrder());
intValues = new int[INPUT_SIZE * INPUT_SIZE];
outputLocations = new float[1][NUM_DETECTIONS][4];
outputClasses = new float[1][NUM_DETECTIONS];
outputScores = new float[1][NUM_DETECTIONS];
numDetections = new float[1];
InputStream labelsInput = assetManager.open(LABEL_FILENAME);
BufferedReader br = new BufferedReader(new InputStreamReader(labelsInput));
String line;
while ((line = br.readLine()) != null) {
labels.add(line);
}
br.close();
try {
tfLite = new Interpreter(loadModelFile(assetManager));
Log.i(LOGGING_TAG, "Input tensor shapes:");
for (int i=0; i<tfLite.getInputTensorCount(); i++) {
int[] shape = tfLite.getInputTensor(i).shape();
String stringShape = "";
for(int j = 0; j < shape.length; j++) {
stringShape = stringShape + ", " + shape[j];
}
Log.i(LOGGING_TAG, "Shape of input tensor " + i + ": " + stringShape);
}
Log.i(LOGGING_TAG, "Output tensor shapes:");
for (int i=0; i<tfLite.getOutputTensorCount(); i++) {
int[] shape = tfLite.getOutputTensor(i).shape();
String stringShape = "";
for(int j = 0; j < shape.length; j++) {
stringShape = stringShape + ", " + shape[j];
}
Log.i(LOGGING_TAG, "Shape of output tensor " + i + ": " + tfLite.getOutputTensor(i).name() + " " + stringShape);
}
} catch (Exception e) {
e.printStackTrace();
throw new RuntimeException(e);
}
}
public static MobileNetObjDetector create(final AssetManager assetManager) throws IOException {
return new MobileNetObjDetector(assetManager);
}
private static MappedByteBuffer loadModelFile(AssetManager assets)
throws IOException {
AssetFileDescriptor fileDescriptor = assets.openFd(MODEL_FILENAME);
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);
}
public void close() {
tfLite.close();
}
public List<DetectionResult> detectObjects(final Bitmap bitmap) {
bitmap.getPixels(intValues, 0, bitmap.getWidth(), 0, 0, bitmap.getWidth(), bitmap.getHeight());
imgData.rewind();
for (int i = 0; i < INPUT_SIZE; ++i) {
for (int j = 0; j < INPUT_SIZE; ++j) {
int pixelValue = intValues[i * INPUT_SIZE + j];
imgData.put((byte) ((pixelValue >> 16) & 0xFF));
imgData.put((byte) ((pixelValue >> 8) & 0xFF));
imgData.put((byte) (pixelValue & 0xFF));
}
}
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);
tfLite.runForMultipleInputsOutputs(inputArray, outputMap);
final ArrayList<DetectionResult> recognitions = new ArrayList<>(NUM_DETECTIONS);
for (int i = 0; i < NUM_DETECTIONS; ++i) {
final RectF detection =
new RectF(
outputLocations[0][i][1] * INPUT_SIZE,
outputLocations[0][i][0] * INPUT_SIZE,
outputLocations[0][i][3] * INPUT_SIZE,
outputLocations[0][i][2] * INPUT_SIZE);
int labelOffset = 1;
recognitions.add(
new DetectionResult(
i,
labels.get((int) outputClasses[0][i] + labelOffset),
outputScores[0][i],
detection));
}
return recognitions;
}
}
the model and label that i use is here.
I am quiet new to machine learning and I got this code from this.
I have tried changing the outputLocations to
private float[][] outputLocations
and NUM_DETECTIONS to
private static final int NUM_DETECTIONS = 25;
this error occurs
E/AndroidRuntime: FATAL EXCEPTION: inference
Process: com.objdetector, PID: 9398
java.lang.IllegalArgumentException: Cannot copy from a TensorFlowLite tensor (StatefulPartitionedCall:3) with shape [1, 25, 4] to a Java object with shape [1, 25].
at org.tensorflow.lite.Tensor.throwIfDstShapeIsIncompatible(Tensor.java:461)
at org.tensorflow.lite.Tensor.copyTo(Tensor.java:252)
at org.tensorflow.lite.NativeInterpreterWrapper.run(NativeInterpreterWrapper.java:170)
at org.tensorflow.lite.Interpreter.runForMultipleInputsOutputs(Interpreter.java:343)
at com.objdetector.deepmodel.MobileNetObjDetector.detectObjects(MobileNetObjDetector.java:130)
at com.objdetector.MainActivity.lambda$onImageAvailable$0$MainActivity(MainActivity.java:98)
at com.objdetector.-$$Lambda$MainActivity$MYtVDhek_YLxj8lClVnaIcVW0Gg.run(lambda)
at android.os.Handler.handleCallback(Handler.java:751)
at android.os.Handler.dispatchMessage(Handler.java:95)
at android.os.Looper.loop(Looper.java:154)
at android.os.HandlerThread.run(HandlerThread.java:61)
I also wanted to try this but I couldn't find it in the Model Maker that I use
None of the duplicate questions I saw really answered the question and I've been working on it for almost a week now and ran out of ideas on what to change since I don't know how to read the netron.app also can anyone help me with this?
